Journal articles on the topic 'Multiple Discriminant Analysis (MDA)'
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F.M. Radzi, N., A. Che Soh, A. J. Ishak, M. K. Hasan, and U. K. Mohamad Yusof. "Aromatic Herbs Classification by using Discriminant Analysis Techniques." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 3 (March 1, 2017): 530. http://dx.doi.org/10.11591/ijeecs.v5.i3.pp530-535.
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<p>An electronic nose was used to distinguish between selected herb samples according to their family group species. This paper aims to evaluate the potential of using the electronic nose to characterize three groups of families of twelve herb species based on the discriminant analysis approach. The feature extraction involves the use of a signal processing technique that simplifies classification and yields optimal results. Two discriminant techniques:- the principal component analysis (PCA) and the multiple discriminant analysis (MDA) were used to investigate the potential to distinguish herb species between several herbs within the same family group. The results showed that the twelve herb species can be better classified using the MDA method compared to the PCA method.</p>
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Mvula Chijoriga, Marcellina. "Application of multiple discriminant analysis (MDA) as a credit scoring and risk assessment model." International Journal of Emerging Markets 6, no. 2 (April 12, 2011): 132–47. http://dx.doi.org/10.1108/17468801111119498.
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PurposeThe purpose of this research is to investigate whether inclusion of risk assessment variables in the multiple discriminant analysis (MDA) model improved the banks ability in making correct customer classification, predict firm's performance and credit risk assessment.Design/methodology/approachThe paper reviews literature on the application of financial distress and credit scoring methods, and the use of risk assessment variables in classification models. The study used a sample of 56 performing and non‐performing assets (NPA) of a privatized commercial bank in Tanzania. Financial ratios were used as independent variables for building the MDA model with a variation of five MDA models. Different statistical tests for normality, equality of covariance, goodness of fit and multi‐colinearity were performed. Using the estimation and validation samples, test results showed that the MDA base model had a higher level of predictability hence classifying correctly the performing and NPA with a correctness of 92.9 and 96.4 percent, respectively. Lagging the classification two years, the results showed that the model could predict correctly two years in advance. When MDA was used as a risk assessment model, it showed improved correct customer classification and credit risk assessment.FindingsThe findings confirmed financial ratios as good classification and predictor variables of firm's performance. If the bank had used the MDA for classifying and evaluating its customers, the probability of failure could have been known two years before actual failure, and the misclassification costs could have been calculated objectively. In this way, the bank could have reduced its non‐performing loans and its credit risk exposure.Research limitations/implicationsThe valiadation sample used in the study was smaller compared to the estimation sample. MDA works better as a credit scoring method in the banking environment two years before and after failure. The study was done on the current financial crisis of 2009.Practical implicationsUse of MDA helps banks to determine objectively the misclassification costs and its expected misclassification errors plus determining the provisions for bad debts. Banks could have reduced the non‐performing loans and their credit risks exposure if they had used the MDA method in the loan‐evaluation and classification process. The study has proved that quantitative credit scoring models improve management decision making as compared to subjective assessment methods. For improved credit and risk assessment, a combination of both qualitative and quantitave methods should be considered.Originality/valueThe findings have shown that using the MDA, commercial banks could have improved their objective decision making by correctly classifying the credit worthiness of a customer, predicting firm's future performance as well as assessing their credit risk. It has also shown that other than financial variables, inclusion of stability measures improves management decision making and objective provisioning of bad debts. The recent financial crisis emphasizes the need for developing objective credit scoring methods and instituting prudent risk assessment culture to limit the extent and potential of failure.
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Irewati, Titiek. "Prediksi Kinerja Keuangan Perusahaan Asuransi Jiwa dengan Mempergunakan Multiple Discriminant Analysis Step Wise." Journal Of Social Research 1, no. 9 (September 2, 2022): 1009–19. http://dx.doi.org/10.55324/josr.v1i9.218.
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Latar Belakang : Penilaian kesehatan keuangan perusahaan asuransi jiwa mempermudah masyarakat untuk memilih perusahaan yang dapat dipercaya.
Tujuan : Pada penelitian ini, peneliti ingin mengetahui model fungsi yang dapat dipergunakan untuk melakukan pengelompokan perusahaan perusahaan asuransi jiwa di Indonesia berdasarkan kondisi kesehatan keuangannya dengan tolok ukur rasio rasio keuangan yang dianalisis mempergunakan Multiple Discriminant Analysis (MDA).
Metode : Penelitian ini menggunakan metode Multiple Discriminant Analysis (MDA) stepwise membuat suatu model prediksi yang menggabungkan beberapa rasio keuangan sehingga lebih memudahkan interpretasi.
Hasil : Pada penelitian ini, diperoleh rasio rasio keuangan yang berperan signifikan untuk membedakan kondisi keuangan perusahaan perusahaan asuransi adalah Rasio Asset, RBC dan Rasio Beban.
Kesimpulan : Dalam hal ini diharapkan perusahaan asuransi jiwa harus mengelola operasionalnya sehingga dapat memberikan kepercayaan kepada konsumen.
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Urrechaga, Eloísa, Urko Aguirre, and Silvia Izquierdo. "Multivariable Discriminant Analysis for the Differential Diagnosis of Microcytic Anemia." Anemia 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/457834.
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Introduction. Iron deficiency anemia and thalassemia are the most common causes of microcytic anemia. Powerful statistical computer programming enables sensitive discriminant analyses to aid in the diagnosis. We aimed at investigating the performance of the multiple discriminant analysis (MDA) to the differential diagnosis of microcytic anemia.Methods. The training group was composed of 200β-thalassemia carriers, 65α-thalassemia carriers, 170 iron deficiency anemia (IDA), and 45 mixed cases of thalassemia and acute phase response or iron deficiency. A set of potential predictor parameters that could detect differences among groups were selected: Red Blood Cells (RBC), hemoglobin (Hb), mean cell volume (MCV), mean cell hemoglobin (MCH), and RBC distribution width (RDW). The functions obtained with MDA analysis were applied to a set of 628 consecutive patients with microcytic anemia.Results. For classifying patients into two groups (genetic anemia and acquired anemia), only one function was needed; 87.9%β-thalassemia carriers, and 83.3%α-thalassemia carriers, and 72.1% in the mixed group were correctly classified.Conclusion. Linear discriminant functions based on hemogram data can aid in differentiating between IDA and thalassemia, so samples can be efficiently selected for further analysis to confirm the presence of genetic anemia.
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Aly, Ibrahim M., H. A. Barlow, and Richard W. Jones. "The Usefulness of SFAS No. 82 (Current Cost) Information in Discriminating Business Failure: An Empirical Study." Journal of Accounting, Auditing & Finance 7, no. 2 (April 1992): 217–29. http://dx.doi.org/10.1177/0148558x9200700209.
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This study examines the usefulness of current cost information (CC) provided to meet the requirements of SFAS No. 33 and SFAS No. 82, as compared to historical cost information (HC) in discriminating business failure. The study also examines the usefulness of CC data as supplement to HC data. Two multivariate statistical techniques, multiple discriminant analysis (MDA) and logistic regression analysis (LRA), are used to derive the ex-post classification results. Three functions are developed based on ratios computed with HC, CC, and the combined HCICC. The resulting functions are used to classify fifty-two firms as failed or nonfailed. The analysis is repeated for three time periods: one, two, and three years before bankruptcy. The main results of the various analyses indicate that the combined HCICC model has more discriminant power than does the HC model alone in each of the three years before bankruptcy. LRA has a better classification rate than MDA for the selected sample.
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Bogdan, Siniša, Luka Šikić, and Suzana Bareša. "Predicting bankruptcy based on the full population of Croatian companies." Ekonomski pregled 72, no. 5 (2021): 643–69. http://dx.doi.org/10.32910/ep.72.5.1.
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This paper analyses the bankruptcy prediction based on the population of companies representative of the total business sector in Croatia. The representativity of the sample is achieved through the propensity score matching of the full population of bankrupt and similar non-bankrupt companies. The robust estimation of bankruptcy prediction is carried out through the multiple discriminant analysis (MDA) and logistic regression (logit). The results indicate high classification accuracy of both models, but more favourable performance of the logit estimation. Overall accuracy of the MDA model was 73.7%, while the overall accuracy of the logit model was 76.3%. The results serve as a bankruptcy estimation benchmark for the business sector in Croatia.
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Holbrook, Morris B., Eric A. Greenleaf, and Robert M. Schindler. "A Dynamic Spatial Analysis of Changes in Aesthetic Responses." Empirical Studies of the Arts 4, no. 1 (January 1986): 47–61. http://dx.doi.org/10.2190/v2x1-gme0-bwpx-xb86.
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Models of evaluative judgment in applied aesthetics generally assume that movements of objects' positions in a multidimensional perceptual space will produce corresponding changes in preferences. However, such spatial representations have usually been tested using static designs or, at best, longitudinal studies that fail to tie perceptual movements to shifts in affective response. This study reports what we believe to be a first dynamic analysis of changes in aesthetic responses. Specifically, twenty-nine aesthetically naive subjects supplied perceptual and affective ratings of twenty art prints on four occasions spaced a week apart. Multiple discriminant analysis (MDA) of the perception data created an MDA space representing each person's perceptions of each print at the beginning and end of the period. We then constructed each individual's preference function based on the MDA space, used beginning and ending perceived positions to compute changes in that preference function, regarded these as predictions of changes in actual affect, and correlated predicted with actual affective shifts to obtain a validity assessment of about r2 = .25 for our dynamic spatial analysis of trends in tastes.
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Sheppard, Brian E., Merhala Thurai, Peter Rodriguez, Patrick C. Kennedy, and David R. Hudak. "Improved Precipitation Typing Using POSS Spectral Modal Analysis." Journal of Atmospheric and Oceanic Technology 38, no. 3 (March 2021): 537–54. http://dx.doi.org/10.1175/jtech-d-20-0075.1.
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AbstractThe Precipitation Occurrence Sensor System (POSS) is a small X-band Doppler radar that measures the Doppler velocity spectra from precipitation falling in a small volume near the sensor. The sensor records a 2D frequency of occurrence matrix of the velocity and power at the mode of each spectrum measured over 1 min. The centroid of the distribution of these modes, along with other spectral parameters, defines a data vector input to a multiple discriminant analysis (MDA) for classification of the precipitation type. This requires the a priori determination of a training set for different types, particle size distributions (PSDs), and wind speed conditions. A software model combines POSS system parameters, a particle scattering cross section, and terminal velocity models, to simulate the real-time Doppler signal measured by the system for different PSDs and wind speeds. This is processed in the same manner as the system hardware to produce bootstrap samples of the modal centroid distributions for the MDA training set. MDA results are compared to images from the Multi-Angle Snowflake Camera (MASC) at the MASCRAD site near Easton, Colorado, and to the CSU–CHILL X-band radar observations from Greeley, Colorado. In the four case studies presented, POSS successfully identified precipitation transitions through a range of types (rain, graupel, rimed dendrites, aggregates, unrimed dendrites). Also two separate events of hail were reported and confirmed by the images.
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Vazquez-Brust, Diego A., and José Antonio Plaza-Úbeda. "What Characteristics Do the Firms Have That Go Beyond Compliance with Regulation in Environmental Protection? A Multiple Discriminant Analysis." Sustainability 13, no. 4 (February 9, 2021): 1873. http://dx.doi.org/10.3390/su13041873.
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This paper is focused on analyzing the characteristics of firms that have environmental performance beyond the requirements of regulation in environmental protection. To identify such characteristics, we propose a value and context model building on environmental paradigms as conceptualized by Dryzek’s environmental discourse theory. Using multiple discriminant analysis (MDA) to analyze data collected from a multi-respondent survey of Argentinean polluting firms, we identify distinctive characteristics of firms going beyond regulation and firms that do not comply with regulation. In particular, comparing with other five environmental discourses, endorsement of green growth is evaluated in its connection with compliance patterns. We find that supporting green growth discourse (also known as ecological modernization) is one of the characteristics of those firms that go beyond compliance in their environmental performance.
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Goldberg, Joseph H., and Jack C. Schryver. "Eye-Gaze Control of the Computer Interface: Discrimination of Zoom Intent." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 37, no. 19 (October 1993): 1370–74. http://dx.doi.org/10.1518/107118193784162272.
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An analysis methodology and associated experiment were developed to assess whether definable and repeatable signatures of eye-gaze characteristics are evident, preceding a decision to zoom-in, zoom-out, or not to zoom at a computer interface. This user intent discrimination procedure can have broad application in disability aids and telerobotic control. Eye-gaze was collected from 10 subjects in a controlled experiment, requiring zoom decisions. The eye-gaze data were clustered, then fed into a multiple discriminant analysis (MDA) for optimal definition of heuristics separating the zoom-in, zoom-out, and no-zoom conditions. Confusion matrix analyses showed that a number of variable combinations classified at a statistically significant level, but practical significance was more difficult to establish. Composite contour plots demonstrated the regions in parameter space consistently assigned by the MDA to unique zoom conditions. Peak classification occurred at about 1200-1600 msec. Improvements in the methodology to achieve practical real-time zoom control are considered.
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Ragothaman, Srinivasan, and Angeline Lavin. "Restatements Due to Improper Revenue Recognition: A Neural Networks Perspective." Journal of Emerging Technologies in Accounting 5, no. 1 (January 1, 2008): 129–42. http://dx.doi.org/10.2308/jeta.2008.5.1.129.
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ABSTRACT: The Securities and Exchange Commission (SEC) issued Staff Accounting Bulletin No. 101 (SEC 1999) in an attempt to curb improper revenue recognition practices. Nonetheless, revenue restatements and the subsequent earnings restatements have continued unabated. Our goal is to contribute to the emerging technologies literature by applying the neural networks methodology to the study of revenue restatements. We also compare the results of the neural network classification with classifications obtained from multiple discriminant analysis (MDA) and logistic regression (Logit) models. Six financial and governance variables were used to train the neural network on a sample of 180 firms, and the model was validated using a holdout sample of 51 additional firms. The results show that the neural network model has superior predictive power for predicting revenue restatement firms when compared to the MDA and Logit models. However, the Logit and MDA models predict nonrevenue restatement firms better. Moreover, when misclassification costs are included, the neural network (NN) model performs the best with the lowest relative misclassification costs.
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Rosa-Filho, José Souto, Carlos Emílio Bemvenuti, and Michael Elliott. "Predicting biological parameters of estuarine benthic communities using models based on environmental data." Brazilian Archives of Biology and Technology 47, no. 4 (August 2004): 613–27. http://dx.doi.org/10.1590/s1516-89132004000400015.
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This study aimed to predict the biological parameters (species composition, abundance, richness, diversity and evenness) of benthic assemblages in southern Brazil estuaries using models based on environmental data (sediment characteristics, salinity, air and water temperature and depth). Samples were collected seasonally from five estuaries between the winter of 1996 and the summer of 1998. At each estuary, samples were taken in unpolluted areas with similar characteristics related to presence or absence of vegetation, depth and distance from the mouth. In order to obtain predictive models, two methods were used, the first one based on Multiple Discriminant Analysis (MDA), and the second based on Multiple Linear Regression (MLR). Models using MDA had better results than those based on linear regression. The best results using MLR were obtained for diversity and richness. It could be concluded that the use predictions models based on environmental data would be very useful in environmental monitoring studies in estuaries.
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Lord, Justin, Amy Landry, Grant T. Savage, and Robert Weech-Maldonado. "Predicting Nursing Home Financial Distress Using the Altman Z-Score." INQUIRY: The Journal of Health Care Organization, Provision, and Financing 57 (January 2020): 004695802093494. http://dx.doi.org/10.1177/0046958020934946.
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This article uses a modified Altman Z-score to predict financial distress within the nursing home industry. The modified Altman Z-score model uses multiple discriminant analysis (MDA) to examine multiple financial ratios simultaneously to assess a firm’s financial distress. This study utilized data from Medicare Cost Reports, LTCFocus, and the Area Resource File. Our sample consisted of 167 268 nursing home-year observations, or an average of 10 454 facilities per year, in the United States from 2000 through 2015. The independent financial variables, liquidity, profitability, efficiency, and net worth were entered stepwise into the MDA model. All of the financial variables, with the exception of net worth, significantly contributed to the discriminating power of the model. K-means clustering was used to classify the latent variable into 3 categorical groups: distressed, risk-of-financial distress, and healthy. These findings will provide policy makers and practitioners another tool to identify nursing homes that are at risk of financial distress.
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Rondeau, Christopher M., J. Addison Betances, and Michael A. Temple. "Securing ZigBee Commercial Communications Using Constellation Based Distinct Native Attribute Fingerprinting." Security and Communication Networks 2018 (July 11, 2018): 1–14. http://dx.doi.org/10.1155/2018/1489347.
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This work provides development of Constellation Based DNA (CB-DNA) Fingerprinting for use in systems employing quadrature modulations and includes network protection demonstrations for ZigBee offset quadrature phase shift keying modulation. Results are based on 120 unique networks comprised of seven authorized ZigBee RZSUBSTICK devices, with three additional like-model devices serving as unauthorized rogue devices. Authorized network device fingerprints are used to train a Multiple Discriminant Analysis (MDA) classifier and Rogue Rejection Rate (RRR) estimated for 2520 attacks involving rogue devices presenting themselves as authorized devices. With MDA training thresholds set to achieve a True Verification Rate (TVR) of TVR = 95% for authorized network devices, the collective rogue device detection results for SNR ≥ 12 dB include average burst-by-burst RRR ≈ 94% across all 2520 attack scenarios with individual rogue device attack performance spanning 83.32% < RRR < 99.81%.
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Xu, Qiang, Jordan Hristov, Liying Cao, and Xinggui Que. "Time to flashover of a vinyl based lining material: Cone calorimeter experiments." Thermal Science 15, no. 3 (2011): 785–92. http://dx.doi.org/10.2298/tsci100621003x.
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Fire behaviour of a vinyl based lining material with and without anti-corrosion painting has been evaluated through 35 and 50kW/m2 cone calorimeter tests. The minimum heat flux requited for surface ignition was estimated. The data were compared by those provided by a revised Kokkala-Thomas?s classification index prediction model, the ?stman-Tsantaridis empirical linear regression model and the Hansen-Hovde multiple discriminant function analysis (MDA). All results collected allowed to predict the material flashover time and to classify the lining material. The results illustrate some differences in the classification of the material due to different approaches of the models used.
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Pardede, Mazmur. "Analisis Prediksi Kebangkrutan Dengan Model ALTMAN Z-SCORE pada PT Indofood Sukses Makmur TBK Periode 2019 – 2021." Jurnal Multidisiplin Madani 2, no. 8 (August 30, 2022): 3465–68. http://dx.doi.org/10.55927/mudima.v2i8.1020.
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Tujuan dari penelitian ini adalah untuk mengetahui serta menganalisis prediksi kebangkrutan pada PT Indofood Sukses Makmur Tbk tahun 2020-2021 menggunakan model Altman Z-Score dengan metode Multiple Discriminant Analysis (MDA) pada lima jenis rasio keuangan. yaitu (X1) Modal Kerja/Jumlah Aktiva, (X2) Saldo Laba/Jumlah Aktiva, (X3) Laba Sebelum Bunga & Pajak/Total Aktiva, (X4) Nilai Pasar Ekuitas/Total Kewajiban dan (X5) Penjualan/Total Aktiva. Penelitian menggunakan deskriptif analisis Hasil analisis menunjukkan PT Indofood Sukses Makmur Tbk mengalami peningkatan kinerja keuangannya pada tahun 2020-2021, tetapi pada tahun 2019 mesuk di zona abu-abu karna dampak dari pandemi yang berlangsung saat itu.
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Ibañez, Agustín, Sol Fittipaldi, Catalina Trujillo, Tania Jaramillo, Alejandra Torres, Juan F. Cardona, Rodrigo Rivera, et al. "Predicting and Characterizing Neurodegenerative Subtypes with Multimodal Neurocognitive Signatures of Social and Cognitive Processes." Journal of Alzheimer's Disease 83, no. 1 (August 31, 2021): 227–48. http://dx.doi.org/10.3233/jad-210163.
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Background: Social cognition is critically compromised across neurodegenerative diseases, including the behavioral variant frontotemporal dementia (bvFTD), Alzheimer’s disease (AD), and Parkinson’s disease (PD). However, no previous study has used social cognition and other cognitive tasks to predict diagnoses of these conditions, let alone reporting the brain correlates of prediction outcomes. Objective: We performed a diagnostic classification analysis using social cognition, cognitive screening (CS), and executive function (EF) measures, and explored which anatomical and functional networks were associated with main predictors. Methods: Multiple group discriminant function analyses (MDAs) and ROC analyses of social cognition (facial emotional recognition, theory of mind), CS, and EF were implemented in 223 participants (bvFTD, AD, PD, controls). Gray matter volume and functional connectivity correlates of top discriminant scores were investigated. Results: Although all patient groups revealed deficits in social cognition, CS, and EF, our classification approach provided robust discriminatory characterizations. Regarding controls, probabilistic social cognition outcomes provided the best characterization for bvFTD (together with CS) and PD, but not AD (for which CS alone was the best predictor). Within patient groups, the best MDA probabilities scores yielded high classification rates for bvFTD versus PD (98.3%, social cognition), AD versus PD (98.6%, social cognition + CS), and bvFTD versus AD (71.7%, social cognition + CS). Top MDA scores were associated with specific patterns of atrophy and functional networks across neurodegenerative conditions. Conclusion: Standardized validated measures of social cognition, in combination with CS, can provide a dimensional classification with specific pathophysiological markers of neurodegeneration diagnoses.
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Dellana, Scott, and David West. "Survival analysis of supply chain financial risk." Journal of Risk Finance 17, no. 2 (March 21, 2016): 130–51. http://dx.doi.org/10.1108/jrf-11-2015-0112.
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Purpose The purpose of this paper is to apply survival analysis, using Cox proportional hazards regression (CPHR), to the problem of predicting if and when supply chain (SC) customers or suppliers might file a petition for bankruptcy so that proactive steps may be taken to avoid a SC disruption. Design/methodology/approach CPHR is first compared to multiple discriminant analysis (MDA) and logistic regression (LR) to assess its suitability and accuracy to SC applications using three years of financial quarterly data for 69 non-bankrupt and 74 bankrupt organizations. A k-means clustering approach is then applied to the survival curves of all 143 organizations to explore heuristics for predicting the timing of bankruptcy petitions. Findings CPHR makes bankruptcy predictions at least as accurately as MDA and LR. The survival function also provides valuable information on when bankruptcy might occur. This information allows SC members to be prioritized into three groups: financially healthy companies of no immediate risk, companies with imminent risk of bankruptcy and companies with intermediate levels of risk that need monitoring. Originality/value The current paper proposes a new analytical approach to scanning and assessing the financial risk of SC members (suppliers or customers). Traditional models are able to predict if but not when a financial failure will occur. Lacking this information, it is impossible for SC managers to prioritize risk mitigation activities. A simple decision rule is developed to guide SC managers in setting these priorities.
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Sun, Jie, Xin Liu, Wenguo Ai, and Qianyuan Tian. "Dynamic financial distress prediction based on class-imbalanced data batches." International Journal of Financial Engineering 08, no. 03 (May 14, 2021): 2150026. http://dx.doi.org/10.1142/s2424786321500262.
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This study proposes two approaches for dynamic financial distress prediction (FDP) based on class-imbalanced data batches by considering both concept drift and class imbalance. One is based on sliding time window and synthetic minority over-sampling technique (SMOTE) and the other is based on sliding time window and majority class partition. Support vector machine, multiple discriminant analysis (MDA) and logistic regression are used as base classifiers in the experiments on a real-world dataset. The results indicate that the two approaches perform better than the pure dynamic FDP (DFDP) models without class imbalance processing and the static FDP models either with or without class imbalance processing.
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Ma’aji, Muhammad M., Nur Adiana Hiau Abdullah, and Karren Lee-Hwei Khaw. "Predicting Financial Distress among SMEs in Malaysia." European Scientific Journal, ESJ 14, no. 7 (March 31, 2018): 91. http://dx.doi.org/10.19044/esj.2018.v14n7p91.
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Predicting financial distress among Small and Medium Enterprises (SMEs) can have a significant impact on the economy as it serves as an effective early warning signal. The study develops distress prediction models combining financial, non-financial and governance variables which were used to analyze the influence of major corporate governance characteristics, like ownership and board structures, on the likelihood of financial distress. Multiple Discriminant Analysis (MDA) model as one of the extensively documented approaches was used. The final sample for the estimation model consists of 172 companies with 50 percent non-failed cases and 50 percent failed cases for the period between 2000 to 2012. The prediction models perform relatively well especially in MDA model that incorporate governance, financial and non-financial variables, with an overall accuracy rate of 90.7 percent in the estimated sample. The accuracy rate in the holdout sample was 91.2 percent for the MDA model. This evidence shows that the models serve as efficient earlywarning signals and can thus be beneficial for monitoring and evaluation. Controlling shareholder, number of directors, and gender of managing director are found to be significant predictors of financially distressed SMEs.
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Ganesha, Agha Swara, Tomy G. Soemapradja, Darman Darman, and Desmizar Desmizar. "Model Analisis Prediksi Kebangkrutan Bank Swasta Nasional Periode 2002-2006." Binus Business Review 3, no. 2 (November 30, 2012): 719. http://dx.doi.org/10.21512/bbr.v3i2.1356.
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There are two main objectives to be achieved by this study:to determine the accuracy level of prediction models of health national private banks using CAMEL ratios, and model the value of Z for the national private commercial banks by using multiple discriminant analysis (MDA) as well as Altman Z values on the model. Determination of the model using the Z value ratios banking health of Capital, Assets, Earnings and Liability (CAEL), then create a new Z value model specifically for national private commercial bank in Indonesia by using statistical analysis of MDA, with SPSS. The samples used were 30 banks, consisting of 19 survived banks in 2002 and 11 bankrupt banks in the same year. The results showed that the model value of Z in the year 2003-2006 cannot reach good accuracy when measured on a per year. Instead, the new Z value model generated by this study has better accuracy in predicting the rate of bankruptcy cases nationwide private commercial bank in Indonesia (86.7%) in 2002 and an average accuracy of 71.67% for the 4-year period of the review.
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Onencan, Abby, Bert Enserink, and Bartel Van de Walle. "Influence of Personal Attributes and Demographic Diversity on Nzoia Basin Negotiation Outcomes." Water 11, no. 2 (January 29, 2019): 227. http://dx.doi.org/10.3390/w11020227.
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The Kenyan government has made significant advances in water resources management at the local authority (county) level with little or no cooperation at the drainage basin level. Research on critical determinants of cooperation amongst transboundary water negotiation teams is limited. In this paper, we assess whether personal attribute diversity (PAD) is a stronger factor than demographic diversity (gender, age, and education play) in determining whether the negotiation team will cooperate or make unilateral actions. We use a negotiation game to study decisions taken by water policymakers. After that, we conduct a multiple discriminant analysis (MDA) to assess the influence of PAD, gender, age, and education on water negotiation outcomes. The findings indicate that PAD plays a significant role in determining whether the group will cooperate or compete. Gender, education, and age barely influence the outcome. Only upon removal of the PAD variable do we see an increase in the discriminant power of gender and education. Age has minimal influence on the negotiation outcomes. We apply the research at a lower level of governance (Nzoia River Basin). However, results might be extrapolated to a bigger basin, like the Nile Basin, through future multiple level analysis which takes account of the complex socio-technical systems.
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Thom de Souza, Rodrigo Clemente, Maria Teresinha Arns Steiner, and Leandro dos Santos Coelho. "Performance Improvement in the Pattern Classification of Nominal Data Sets Applying Multiple Correspondence Analysis." Applied Mechanics and Materials 670-671 (October 2014): 1482–87. http://dx.doi.org/10.4028/www.scientific.net/amm.670-671.1482.
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Classification is a supervised learning problem used to discriminate data instances in different classes. The solution to this problem is obtained through algorithms (classifiers) that look for patterns of relationships between classes in known cases, using these relationships to classify unknown cases. The performance of the classifiers depends substantially of the data types. In order to give proper treatment to nominal data, this paper shows that the application of previous transformations can substantially improve the performance of classifiers, bringing significant benefits to the result of the whole process of Knowledge Discovery in Databases (KDD). This paper uses three different data sets with nominal data and two well-known classifiers: the Linear Discriminant Analysis (LDA), and the Naïve-Bayes (NB). For data transformation, the paper applies an approach called Geometric Data Analysis (GDA). The GDA techniques compared in this paper are the traditional Principal Component Analysis (PCA) and the underexplored Multiple Correspondence Analysis (MCA). The results confirm the capability of the GDA transformation to improve the classification accuracy and attest the superiority of the MCA in comparison with its precursor, the PCA, when applied to nominal data.
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Saromah, Tati, and Choiroel Woestho. "Pengaruh Penerapan Good Corporate Governance dan Solvabilitas Terhadap Peringkat Obligasi Pada Industri Perbankan." Jurnal Kajian Ilmiah 21, no. 4 (December 29, 2021): 391–400. http://dx.doi.org/10.31599/jki.v21i4.708.
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This research was conducted to determine how the effect of the implementation of good corporate governance and solvency on bond ratings. This research is conducted because there is still little research on the prediction of bond ratings in Indonesia. This is due to the limited data on bonds and investors' understanding of bonds. The purpose of this study are to determine the partial and simultaneous effect of good corporate governance and solvency on bond ratings in the banking industry in 2007-2015. The research analysis method used in this research is Multiple Discriminant Analysis (MDA) which consists of classical assumption tests, multiple linear regression tests, and hypothesis testing. This type of research is quantitative using secondary data and the sampling technique used is purposive sampling. The results in this study are partially Good Corporate Governance (X1) has a significant positive effect on bond ratings (Y), and Solvency (X2) has no effect on bond ratings (Y), as well as simultaneously Good Corporate Governance (X1) and Solvency (X2). ) has a significant effect on bond ratings (Y). Keywords: Good Corporate Governance, Solvency Ratio, and Bond Ratings Abstrak Penelitian ini dilakukan untuk mengetahui bagaimana pengaruh penerapan good corporate governance dan solvabilitas terhadap peringkat obligasi. Penelitian ini dilakukan karena masih sedikit penelitian tentang prediksi peringkat obligasi di Indonesia. Hal ini disebabkan terbatasnya data obligasi dan pemahaman investor terhadap obligasi Tujuan dalam penelitian ini adalah untuk mengetahui pengaruh secara parsial dan simultan antara good corporate governance dan solvabilitas terhadap peringkat obligasi pada industri perbankan tahun 2007-2015. Metode analisis penelitian yang digunakan dalam penelitian ini adalah Multiple Discriminant Analysis (MDA) yang terdiri dari uji asumsi klasik, uji regresi linier berganda, dan uji hipotesis. Jenis penelitian ini adalah kuantitatif dengan menggunakan data sekunder dan teknik pengambilan sampel yang digunakan adalah purposive sampling. Hasil dalam penelitian ini adalah secara parsial Good Corporate Governance (X1) berpengaruh positif signifikan terhadap peringkat obligasi (Y), dan Solvabilitas (X2) tidak berpengaruh terhadap peringkat obligasi(Y), serta secara simultan Good Corporate Governance (X1) dan Solvabilitas (X2) berpengaruh signifikan terhadap peringkat obligasi (Y). Kata kunci: Prinsip Tata Kelola Perusahaan, Rasio Solvabilitas, Peringkat Obligasi
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ALKAN, Leyla. "HOUSING MARKET DIFFERENTIATION: THE CASES OF YENIMAHALLE AND ÇANKAYA IN ANKARA." International Journal of Strategic Property Management 19, no. 1 (April 1, 2015): 13–26. http://dx.doi.org/10.3846/1648715x.2014.1000429.
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This study aims to investigate housing market differentiation, drawing upon the results of case studies of the Çankaya and Yenimahalle districts that adopt a set of statistical techniques. As a first step, a cluster analysis is carried out to identify whether identifiable clusters of housing attributes exist on the basis of neighborhoods. Next, a Multiple Discriminant Analysis (MDA) is applied to investigate the differences between clusters, and to understand which housing attributes contribute most to submarket separation. Finally, a hedonic price analysis is conducted for each cluster and for the overall market to identify price differences in the housing market. The results of the study support the hypothesis that the housing market is segmented in Yenimahalle and Çankaya, and that location is the main determining factor in this segmented structure of different house values. The study also reveals that within this segmented structure, each cluster has its own dynamics, and that the price formation in each cluster is dependent on different variables.
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Adamowicz, Krzysztof, and Tomasz Noga. "Assessment applicability of selected models of multiple discriminant analyses to forecast financial situation of Polish wood sector enterprises." Folia Forestalia Polonica 59, no. 1 (March 1, 2017): 59–67. http://dx.doi.org/10.1515/ffp-2017-0006.
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Abstract In the last three decades forecasting bankruptcy of enterprises has been an important and difficult problem, used as an impulse for many research projects (Ribeiro et al. 2012). At present many methods of bankruptcy prediction are available. In view of the specific character of economic activity in individual sectors, specialised methods adapted to a given branch of industry are being used increasingly often. For this reason an important scientific problem is related with the indication of an appropriate model or group of models to prepare forecasts for a given branch of industry. Thus research has been conducted to select an appropriate model of Multiple Discriminant Analysis (MDA), best adapted to forecasting changes in the wood industry. This study analyses 10 prediction models popular in Poland. Effectiveness of the model proposed by Jagiełło, developed for all industrial enterprises, may be labelled accidental. That model is not adapted to predict financial changes in wood sector companies in Poland. The generally known Altman model showed the greatest effectiveness in the identification of enterprises at risk of bankruptcy. However, that model was burdened with one of the greatest errors in the classification of healthy enterprises as sick. The best effectiveness in the identification of enterprises not threatened with bankruptcy was found for forecasts prepared using the Prusak 2 model. However, forecasts based on those models were characterised by erroneous classification of sick companies as healthy. The model best fit to predict the financial situation of Polish wood sector companies was the Poznań model Pz = 3.562 · X1 + 1.588 · X2 + 4.288 · X3 + 6.719 · X4 - 2.368 where: X1 - net income / total assets; X2 - (current assets - stock) / current liabilities; X3 - fixed capital / total assets X4 - income from sales / sales revenue).
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Wieprow, Joanna, and Agnieszka Gawlik. "The Use of Discriminant Analysis to Assess the Risk of Bankruptcy of Enterprises in Crisis Conditions Using the Example of the Tourism Sector in Poland." Risks 9, no. 4 (April 16, 2021): 78. http://dx.doi.org/10.3390/risks9040078.
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The aim of this article is to use multiple discriminant analysis (MDA) and logit models to assess the risk of bankruptcy of companies in the Polish tourism sector in the crisis conditions caused by the COVID-19 pandemic. A review of the literature is used to select models appropriate to analyze the risk of bankruptcy of tourism enterprises listed on the Warsaw Stock Exchange (WSE). The data are from half-year financial statements (the first half of 2019 and 2020, respectively). The obtained results are compared with the current values of the Altman EM-score model and selected financial ratios. An analysis allowed the estimation of the risk of bankruptcy of enterprises from the tourism sector in Poland as well as the assessment of the prognostic value of these models in the tourism sector and the risk of a collapse of this market in Poland. The article fills the research gap created by the negligible use of solvency analysis of the tourism sector and constitutes the basis for estimating the risk of collapse of the tourism sector in a crisis situation.
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Lazaridou-Dimitriadou, M. "Seasonal variation of the water quality of rivers and streams of eastern Mediterranean." Web Ecology 3, no. 1 (May 21, 2002): 20–32. http://dx.doi.org/10.5194/we-3-20-2002.
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Abstract. Biotic and abiotic data on undisturbed or moderately disturbed lotic sites from a number of studies carried out in northern Greece showed that large rivers differ from small rivers, streams or creeks in terms of diversity, dominant groups and the kind of taxa (concerning the sensitivity of the taxa according to Biological Monitoring Working Party (BMWP) biotic scores). This is mainly due to the differences in their physical characteristics. Correlation of the environmental variables using MDA (multiple discriminant analysis) showed that the chief differentiating factors among the above water bodies are substrate, total suspended solids (TSS), conductivity, slope and temperature. Additionally, there is no clear phenological seasonality in the majority of the dominant benthic macroinvertebrate groups when undisturbed or moderately disturbed sites of mountainous creeks and small rivers are examined. By contrast, in downstream sites of long rivers, seasonality characterizes the dominant benthic macroinvertebrate groups, as it does for other Mediterranean animals.
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Bawono, Anton, and Aisyah Setyaningrum. "ANALYSIS OF INDONESIA’S ISLAMIC BANKING BANKRUPTCY PREDICTION FOR PERIOD 2014-2016." IQTISHADIA 11, no. 1 (July 24, 2018): 155. http://dx.doi.org/10.21043/iqtishadia.v11i1.3141.
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<p>The background of this study was based on market share of Isalmic banks in which it is only 5% of National Banks in Indonesia. This indicates ineffective Islamic banks performance. Therefore it will lead to the bankruptcy. Assessing bankruptcy required deep assessment of company performance through its financial ratios; these are Working Capital to Total Assets (WCTA), Earnings Before Interest and Tax to Total Assets (EBITTA), Retained Earnings to Total Assets (RETA) and Book Value of Equity to Book Value of Total Debt (BVEBVTD). The purpose of this study was intended to explain about the influence of those financial ratios on bankruptcy prediction of banks based on Altman Z-Score Model.</p><p>The data was conducted through indirect observation from quarterly financial report of banks for period 2014-2016. The samples were 11 Sharia banks from 13 Sharia banks listed on Indonesia Financial Services Authority (OJK-RI) by January 2017. The process of analysis was started by conducting Stationery analysis then Regression analysis, the test of assumptions and Multiple Discriminant Analysis (MDA).</p>The result suggests that WCTA, EBITTA and BVEBVTD variable show positive and significance effect on bankruptcy prediction, while the RETA variable shows negative and insignificance. Based on this study, there are only two variables, WCTA and BVEBVTD, that couldpredict bankruptcy with 98.2% accuracy.
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Muller, G. H., B. W. Steyn-Bruwer, and W. D. Hamman. "Predicting financial distress of companies listed on the JSE: A comparison of techniques." South African Journal of Business Management 40, no. 1 (March 31, 2009): 21–32. http://dx.doi.org/10.4102/sajbm.v40i1.532.
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In 2006, Steyn-Bruwer and Hamman highlighted several deficiencies in previous research which investigated the prediction of corporate failure (or financial distress) of companies. In their research, Steyn-Bruwer and Hamman made use of the population of companies for the period under review and not only a sample of bankrupt versus successful companies. Here the sample of bankrupt versus successful companies is considered as two extremes on the continuum of financial condition, while the population is considered as the entire continuum of financial condition.The main objective of this research, which was based on the above-mentioned authors’ work, was to test whether some modelling techniques would in fact provide better prediction accuracies than other modelling techniques. The different modelling techniques considered were: Multiple discriminant analysis (MDA), Recursive partitioning (RP), Logit analysis (LA) and Neural networks (NN).From the literature survey it was evident that existing literature did not readily consider the number of Type I and Type II errors made. As such, this study introduces a novel concept (not seen in other research) called the “Normalised Cost of Failure” (NCF) which takes cognisance of the fact that a Type I error typically costs 20 to 38 times that of a Type II error.In terms of the main research objective, the results show that different analysis techniques definitely produce different predictive accuracies. Here, the MDA and RP techniques correctly predict the most “failed” companies, and consequently have the lowest NCF; while the LA and NN techniques provide the best overall predictive accuracy.
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Bulatenko, Mariya, and Ya Romanov. "Analysis of the Economic Security of the Power Grid Complex in Russia Based on an Assessment of the Likelihood of Bankruptcy of Enterprises." Scientific Research and Development. Economics of the Firm 10, no. 4 (December 27, 2021): 34–42. http://dx.doi.org/10.12737/2306-627x-2021-10-4-34-42.
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Natural monopolies are one of the most profitable sectors of the economy. However, due to the lack of competition, the government has to regulate prices through rigid tariffs, thereby limiting the amount of revenue. This trend plays a huge role for the end consumer, as prices are fixed by the public sector. This negatively affects the economic security of the monopolist enterprise. Improper management of an enterprise in conditions of government regulation, limited investment resources, high consumer requirements for the quality of electricity can lead to the bankruptcy of the power grid company.To identify the risk of bankruptcy in the article, calculations were performed using multiple discriminant analysis models (MDA models: Altman's two-factor model and Fedotova's two-factor model) and logistic models (Logit models: Gruzchinsky's model (2003) and Lin-Piesse's model (2004)) of the annual financial reports of enterprises of the electric grid complex of Russia (subsidiaries of PJSC Rosseti, such as: OJSC IDGC of Urals, PJSC IDGC of Center and Volga Region, PJSC Rosseti Volga, PJSC Rosseti Kuban, PJSC TRK) for the last five years (2016-2020, this period characterizes the financial situation of enterprises before and during the Covid-19 pandemic).
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SUN, Jie, Young-Chan LEE, Hui LI, and Qing-Hua HUANG. "COMBINING B&B-BASED HYBRID FEATURE SELECTION AND THE IMBALANCE-ORIENTED MULTIPLE-CLASSIFIER ENSEMBLE FOR IMBALANCED CREDIT RISK ASSESSMENT." Technological and Economic Development of Economy 21, no. 3 (May 26, 2015): 351–78. http://dx.doi.org/10.3846/20294913.2014.884024.
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An ideal model for credit risk assessment is supposed to select important features and process imbalanced data sets in an effective manner. This paper proposes an integrated method that combines B&B (branch and bound)-based hybrid feature selection (BBHFS) with the imbalanceoriented multiple-classifier ensemble (IOMCE) for imbalanced credit risk assessment and uses the support vector machine (SVM) and the multiple discriminant analysis (MDA) as the base predictor. BBHFS is a hybrid feature selection method that integrates the t-test and B&B with the k-fold crossvalidation method to search for a satisfactory feature subset. The IOMCE divides majority samples into several subsets and then combines them with minority samples to construct several training sets for constructing a multiple-classifier ensemble model. We conduct main experiments using a 1:3 imbalanced corporate credit risk data set with continuous features and extended experiments using a 1:5 imbalanced data set with continuous features and a 1:3 imbalanced data set with discrete and nominal features. We combine no feature selection and five feature selection methods (the pure B&B, the factor analysis, the pure t-test, t-test & correlation analysis, and BBHFS) with single-classifier and the IOMCE to construct SVM and MDA models for an empirical comparison. When all features are continuous, the BBHFS-IOMCE method generally outperforms all the other methods. More specifically, BBHFS provides more stable and satisfactory results than the other feature selection methods, and compared with single-classifier models, IOMCE models can significantly enhance the recognition rate for minority samples while incurring a small reduction in the recognition rate for majority samples and maintaining an acceptable overall accuracy. When the features are almost discrete or nominal, the IOMCE method retains its ability to deal with an imbalanced data set, although the five feature selection methods have no significant advantages over no feature selection. This suggests that BBHFS is effective in retaining useful information when reducing the dimensionality of continuous features and that the BBHFS-IOMCE method is an important tool for imbalanced credit risk assessment.
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Lasmana, Andy, and Leni Ashariah. "PREDIKSI RETURN SAHAM PADA PERUSAHAAN SEKTOR PERTAMBANGAN YANG TERDAFTAR DI BURSA EFEK INDONESIA." JURNAL AKUNIDA 5, no. 1 (August 24, 2019): 24. http://dx.doi.org/10.30997/jakd.v5i1.1826.
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Investor di pasar modal memiliki banyak pilihan berinvestasi, sehingga memiliki preferensi resiko yang berbeda. Penelitian ini bertujuan untuk mengetahui pengaruh return saham satu dan dua tahun yang akan datang pada sektor industri pertambangan yang terdaftar di Bursa Efek Indonesia (BEI), dan untuk mengetahui rasio keuangan apa saja yang dapat membedakan kecenderungan kedua kelompok yang memiliki return positif dan return negatif pada sektor industri pertambangan yang terdaftar di Bursa Efek Indonesia (BEI). Populasi dalam penelitian ini adalah industri pertambangan yang terdaftar di Bursa Efek Indonesia (BEI) yang terdiri dari 43 perusahaan. Pengambilan sampel yang berjumlah 29 perusahaan pertambangan dilakukan dengan purposive sampling. Analisis data menggunakan Multiple Discriminant Analysis. Hasil penelitian menunjukan bahwa signifikan perbedaannya di antara perusahaan yang memiliki return positif dan return negatif pada periode (t-2) adalah variabel Current Assets to Total Assets, Operating Profit Margin, Return on Equity, Devidend dan Yield. Sedangkan pada periode satu tahun sebelum return (t-1), signifikan perbedaannya adalah Gross Profit Margin, EBIT to Total Assets, Yield, Current Assets to Sales. Dengan model MDA memiliki tingkat akurasi 82,2% pada periode (t-2) dan 79,3% untuk periode (t-1).
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Nuraini, Ani, Farah Margaretha Leon, and Bahtiar Usman. "Analysis of the Effect of Governance and Research and Development on Probability of Default." International Journal of Science and Society 2, no. 4 (November 2, 2020): 492–506. http://dx.doi.org/10.54783/ijsoc.v2i4.233.
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Research on the probability of default is always very interesting because it is related to the goals of companies that want to live on, this study aims to examine and analyze which variables are important in this study consisting of the audit committee and managerial ownership who are members of governance, as well as the R & D variables. In influencing the probability of default either directly or indirectly by using the mediating variable Current Ratio estimated results (CRh), to predict the probability of default. This study uses Multiple Discriminant Analysis (MDA) in determining the value of the default probability, while the estimation analysis uses two-stage regression. The regression estimation results conclude that the governance variable on the audit committee has no direct effect on the probability of default, while the CRh mediation becomes significant. The managerial ownership variable which is part of the governance variable has a significant effect both directly and indirectly through CRh mediation, as well as the R&D variable which also has a significant effect on the probability of default either directly or indirectly. This study produces a model in which CRh as a mediator can signal the probability of default on non-financial variables such as governance and R&D. The results of this study contribute to early detection of the probability of default of any non-financial variables that affect both governance and R&D. This model was developed to anticipate the occurrence of bankruptcy by detecting the probability of default by using non-financial variables with CRh as mediation which is supported by model tests with Hosmer-Lemeshow and ROC.
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Herlina, Herlina, Ahmad Ridho’i, Anggie Erma Yunita, Mega Puja Azhari, and Ade Reynaldi Saputra. "MODEL PREDIKSI FINANCIAL DISTRESS PADA PERUSAHAAN MANUFAKTUR SEKTOR INDUSTRI BARANG DAN KONSUMSI." Teknika: Engineering and Sains Journal 3, no. 2 (December 31, 2019): 77. http://dx.doi.org/10.51804/tesj.v3i2.490.77-82.
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Kesulitan keuangan (financial distress) adalah sebuah tahapan yang akan dilalui oleh sebuah perusahaan sebelum mengalami kebangkrutan. Dengan alasan tersebut maka kemampuan untuk memprediksi kesulitan keuangan dapat menjadi informasi yang bermanfaat bagi perusahaan maupun investor. Penelitian mengenai financial distress sudah dimulai dari penelitian Altman pada tahun 1968 menggunakan metode Multiple Discriminant Analysis (MDA). Dimulai dari penelitian Altman, muncul penelitian-penelitian lainnya menggunakan pengembangan metode statistik, seperti Logistic Regression. Dari metode statistik kemudian berkembang dengan munculnya penelitian-penelitian menggunakan metode-metode kecerdasan buatan, serta algoritma evolusi untuk berusaha mendapatkan model prediksi financial distress yang akurat. Tujuan dari penelitian ini adalah untuk membandingkan tingkat akurasi dari model prediksi financial distress perusahaan manufaktur terbuka pada sektor industri barang konsumsi yang terdaftar pada Bursa Efek Indonesia menggunakan metode kecerdasan buatan serta algoritma evolusi. Metode yang digunakan untuk metode kecerdasan buatan adalah metode Support Vector Machines dan untuk model algoritma evolusi menggunakan metode Particle Swarm Optimization-Support Vector Machines. Tingkat akurasi dari masing-masing metode akan diukur dari prosentase misklasifikasi terkecil yang dihasilkan. Dari pengujian model menggunakan metode Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 11,11% dengan menggunakan Kernel Linear dan untuk metode Particle Swarm Optimization-Support Vector Machines, didapatkan tingkat misklasifikasi terkecil sebesar 5,56% dengan menggunakan Kernel RBF, ? = 2.
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Mims, Willie H., Michael A. Temple, and Robert F. Mills. "Active 2D-DNA Fingerprinting of WirelessHART Adapters to Ensure Operational Integrity in Industrial Systems." Sensors 22, no. 13 (June 29, 2022): 4906. http://dx.doi.org/10.3390/s22134906.
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The need for reliable communications in industrial systems becomes more evident as industries strive to increase reliance on automation. This trend has sustained the adoption of WirelessHART communications as a key enabling technology and its operational integrity must be ensured. This paper focuses on demonstrating pre-deployment counterfeit detection using active 2D Distinct Native Attribute (2D-DNA) fingerprinting. Counterfeit detection is demonstrated using experimentally collected signals from eight commercial WirelessHART adapters. Adapter fingerprints are used to train 56 Multiple Discriminant Analysis (MDA) models with each representing five authentic network devices. The three non-modeled devices are introduced as counterfeits and a total of 840 individual authentic (modeled) versus counterfeit (non-modeled) ID verification assessments performed. Counterfeit detection is performed on a fingerprint-by-fingerprint basis with best case per-device Counterfeit Detection Rate (%CDR) estimates including 87.6% < %CDR < 99.9% and yielding an average cross-device %CDR ≈ 92.5%. This full-dimensional feature set performance was echoed by dimensionally reduced feature set performance that included per-device 87.0% < %CDR < 99.7% and average cross-device %CDR ≈ 91.4% using only 18-of-291 features—the demonstrated %CDR > 90% with an approximate 92% reduction in the number of fingerprint features is sufficiently promising for small-scale network applications and warrants further consideration.
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Nikolic, Danijela, Milica Spasic, Jasmina Sinzar-Sekulic, Vladimir Randjelovic, and Dmitar Lakusic. "Morphometric analysis of nectaries and their potential use in the taxonomy of the Jovibarba heuffelii complex (Crassulaceae)." Archives of Biological Sciences 67, no. 2 (2015): 511–24. http://dx.doi.org/10.2298/abs140911014n.
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The aim of this study was to quantify the morphological variation of nectaries among 14 populations of Jovibarba heuffelii based on multivariate statistics, and to establish whether nectaries possess taxonomic significance for differentiating taxa within the J. heuffelii complex. To this end, we measured the width of the nectary, its height, the angle between the carpels and nectary, the shape of the nectary and the distance between nectaries were measured and analyzed. Descriptive statistics, the Tukey HSD (honest significant difference) of homogenous groups for the unequal N post-hoc test, canonical discriminant analysis (CDA) and multiple correspondence analysis (MCA) were used. Morphometric analysis showed that the quantitative and semiquantitative characteristics of nectaries in the J. heuffelii complex are highly morphologically variable, both within and between populations, and that they are unreliable as taxonomic characters for taxon differentiation within the J. heuffelii complex.
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Xin, Xiu, and Xiaoyi Xiong. "Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs." International Journal of Advanced Pervasive and Ubiquitous Computing 3, no. 4 (October 2011): 6–14. http://dx.doi.org/10.4018/japuc.2011100102.
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The operating status of an enterprise is disclosed periodically in a financial statement. Financial distress prediction is important for business bankruptcy prevention, and various quantitative prediction methods based on financial ratios have been proposed. This paper presents a financial distress prediction model based on wavelet neural networks (WNNs). The transfer functions of the neurons in WNNs are wavelet base functions which are determined by dilation and translation factors. Back propagation algorithm was used to train the WNNs. Principal component analysis (PCA) method was used to reduce the dimension of the inputs of the WNNs. Multiple discriminate analysis (MDA), Logit, Probit, and WNNs were employed to a dataset selected from Chinese-listed companies. The results demonstrate that the proposed WNNs-based model performs well in comparison with the other three models.
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Bouramtane, Tarik, Halima Hilal, Ary Tavares Rezende-Filho, Khalil Bouramtane, Laurent Barbiero, Shiny Abraham, Vincent Valles, et al. "Mapping Gully Erosion Variability and Susceptibility Using Remote Sensing, Multivariate Statistical Analysis, and Machine Learning in South Mato Grosso, Brazil." Geosciences 12, no. 6 (June 1, 2022): 235. http://dx.doi.org/10.3390/geosciences12060235.
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In Brazil, the development of gullies constitutes widespread land degradation, especially in the state of South Mato Grosso, where fighting against this degradation has become a priority for policy makers. However, the environmental and anthropogenic factors that promote gully development are multiple, interact, and present a complexity that can vary by locality, making their prediction difficult. In this framework, a database was constructed for the Rio Ivinhema basin in the southern part of the state, including 400 georeferenced gullies and 13 geo-environmental descriptors. Multivariate statistical analysis was performed using principal component analysis (PCA) to identify the processes controlling the variability in gully development. Susceptibility maps were created through four machine learning models: multivariate discriminant analysis (MDA), logistic regression (LR), classification and regression tree (CART), and random forest (RF). The predictive performance of the models was analyzed by five evaluation indices: accuracy (ACC), sensitivity (SST), specificity (SPF), precision (PRC), and Receiver Operating Characteristic curve (ROC curve). The results show the existence of two major processes controlling gully erosion. The first is the surface runoff process, which is related to conditions of slightly higher relief and higher rainfall. The second also reflects high surface runoff conditions, but rather related to high drainage density and downslope, close to the river network. Human activity represented by peri-urban areas, construction of small earthen dams, and extensive rotational farming contribute significantly to gully formation. The four machine learning models yielded fairly similar results and validated susceptibility maps (ROC curve > 0.8). However, we noted a better performance of the random forest (RF) model (86% and 89.8% for training and test, respectively, with an ROC curve value of 0.931). The evaluation of the contribution of the parameters shows that susceptibility to gully erosion is not governed primarily by a single factor, but rather by the interconnection between different factors, mainly elevation, geology, precipitation, and land use.
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Biryukov, A. N., and G. M. Bagautdinova. "ANALYSIS OF ASSESSMENT TECHNIQUES OF THREAT OF BANKRUPTCY AND FINANCIAL CONDITION OF THE ENTERPRISE: ECONOMIC CALCULATION." Scientific Review Theory and Practice 11, no. 7 (2021): 2268–82. http://dx.doi.org/10.35679/2226-0226-2021-11-7-2268-2282.
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The large number of techniques of the analysis of bankruptcy both in foreign, and in the Russian theory and practice often complicates the choice of a system of indicators for identification of threat of bankruptcy of the concrete enterprise. At the same time it should be noted that actually all techniques are constructed or on one of two approaches (quantitative or qualitative), or with application of one of three models: scoring model, model of the multiple discriminant analysis (MDA) and rating model. Also it causes various, often opposite, opinions on their applicability in the conditions of the Russian economy and the problem of adequacy of the received estimates keeps the relevance. In general it is possible to draw a conclusion on quite low predictive opportunities of the western and domestic models. Such result for the western models can be explained with the fact that they do not con- sider the Russian specifics and are constructed according to other data. More exact results are explained by a large number of variables in comparison with the Russian models. Feature of the Russian models also is that coefficients are picked up in the expert way, without application of methods of the statistical analysis. One shortcoming is inherent in all techniques – they consider a condition of an indicator only at the time of the analysis, and change of dynamics of indicators in time are not considered. A possibility of further development of methodology of forecasting of bankruptcy on the basis of data of financial statements of the Russian enterprises is the reliability of information on the state of affairs at the concrete enterprises and difficulty of its receiving. The practical application of the recommended indicators in the economic analysis of LLC «N» in the publication made it possible to draw conclusions similar to those that were made based on the results of studies of the use of models by foreign and Russian authors.
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Dea Raisa Oma Putri, Wahyu Indah Mursalini, and Rasidah Nasrah. "Analisis Prediksi Kebangkrutan Menggunakan Model Springate (S-Score) Pada Perusahaan Sub Sektor Ritel Di Bursa Efek Indonesia 2016-2020." GEMILANG: Jurnal Manajemen dan Akuntansi 3, no. 1 (December 13, 2022): 1–20. http://dx.doi.org/10.56910/gemilang.v3i1.297.
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Penelitian ini bertujuan untuk mengetahui potensi kebangkrutan menggunakan metode Springate (S-Score) pada perusahaan Sub Sektor Ritel yang terdaftar di Bursa Efek Indonesia tahun 2016-2020. Objek yang digunakan dalam penelitian ini adalah perusahaan sub sektor ritel yang terdaftar di Bursa Efek Indonesia. Sumber data yang digunakan adalah data sekunder. Jenis data yang digunakan adalah data kuantitatif. Sampel yang digunakan dalam penelitian ini adalah sebanyak 21 perusahaan ritel yang memenuhi kriteria sampel. Teknik analisa data yang digunakan menggunakan metode Multiple Discriminant Analysis (MDA) yaitu: memilih empat dari 19 rasio keuangan inti, sehingga dapat digunakan untuk membedakan apakah perusahaan tergolong bangkrut atau tidak. Springate mengemukakan nilai cut off yang berlaku untuk model ini adalah 0,862, nilai S-score yang didapat > 0,862 diprediski tidak bangkrut dan nilai S-score yang didapat < 0,862 diprediski bangkrut. Berdasarkan perhitungan Springate (S-Score) hasil penelitian ini bahwa pada tahun 2016 terdapat 3 perusahaan yang diprediksi bangkrut, pada tahun 2017 terdapat 2 perusahaan yang diprediksi bangkrut, pada tahun 2018 ada 1 perusahaan yang diprediksi bangkrut, pada tahun 2019 ada 2 perusahaan yang diprediksi bangkrut dan pada tahun 2020 ada 5 perusahaan yang diprediksi bangkrut didapat bahwa dari 21 perusahaan Sub Sektor Ritel terdapat 6 perusahaan diprediski bangkrut dalam waktu periode tahun 2016-2020 Diantaranya adalah perusahaan Centratama Telekomunikasi Indonesia Tbk, Erajaya Swasembada Tbk, Kioson Komersial Indonesia Tbk, Global Teleshop Tbk, Sona Topas Tourism Industry Tbk dan Tiphone Mobile Indonesia Tbk. Model Springate (S-Score) dalam memprediksi adanya potensi (indikasi) kebangkrutan perusahaan memiliki tingkat keakuratan hingga 92,5%.
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Legaz, Isabel, Estefanía Barrera-Pérez, Agustín Sibón, Francisco Martínez-Díaz, and María D. Pérez-Cárceles. "Combining Oxidative Stress Markers and Expression of Surfactant Protein A in Lungs in the Diagnosis of Seawater Drowning." Life 13, no. 1 (January 5, 2023): 159. http://dx.doi.org/10.3390/life13010159.
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Background and Objectives. The diagnosis of seawater drowning (SWD) remains one of the most complex and contentious. It is one of the leading causes of unintentional death around the world. In most cases, the forensic pathologist must reach an accurate diagnosis from the autopsy findings and a series of complementary tests such as histopathological, biological, and chemical studies. Despite the lung being the most affected organ in death by submersion, there are few studies on this type of death’s impact on this organ. The aim was to investigate human lung cadavers of forensic cases due to different causes of death, the concentration of the oxidative stress markers malondialdehyde (MDA) and γ-glutamyl-l-cysteinyl glycine (GSH), and the relationship with the expression of surfactant protein A (SP-A) to try to discriminate SWD from other types of causes of death. Materials and Methods. A total of 93 forensic autopsy cases were analyzed. Deaths were classified into three major groups based on the scene, cause of death, and autopsy findings (external foam, frothy fluid in airways, overlapping medial edges of the lungs): (a) drowning in seawater (n = 35), (b) other asphyxia (n = 33), such as hangings (n = 23), suffocations (n = 6), and strangulation (n = 4), and (c) other causes (n = 25), such as multiple suffocations. Oxidative stress markers (MDA and GSH) and the immunohistochemical expression of SP-A were determined in both lungs. Results. MDA levels were statistically higher in both lungs in cases of SWD than in other causes of death (p = 0.023). Similarly, significantly higher levels of GSH were observed in SWD compared to the rest of the deaths (p = 0.002), which was more significant in the right lung. Higher immunohistochemical expression of SP-A was obtained in the cases of SWD than in the other causes of death, with higher levels in both lungs. The correlation analysis between the levels of oxidative stress (MDA and GSH) in the lung tissue and the expression level of SP-A showed positive and significant results in SWD, both in the alveolar membrane and the alveolar space. Conclusions. Determining the levels of MDA and GSH in lung tissue and the expression level of SP-A can be of great importance in diagnosing SWD and the circumstances of death. A better understanding of the physiology of submersion is essential for its possible repercussions in adopting measures in the approach to patients who have survived a submersion process. It is also necessary for forensic pathology to correctly interpret the events that lead to submersion.
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Zhu, Qiusha, Lin Lin, Mei-Ling Shyu, and Dianting Liu. "Utilizing Context Information to Enhance Content-Based Image Classification." International Journal of Multimedia Data Engineering and Management 2, no. 3 (July 2011): 34–51. http://dx.doi.org/10.4018/jmdem.2011070103.
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Traditional image classification relies on text information such as tags, which requires a lot of human effort to annotate them. Therefore, recent work focuses more on training the classifiers directly on visual features extracted from image content. The performance of content-based classification is improving steadily, but it is still far below users’ expectation. Moreover, in a web environment, HTML surrounding texts associated with images naturally serve as context information and are complementary to content information. This paper proposes a novel two-stage image classification framework that aims to improve the performance of content-based image classification by utilizing context information of web-based images. A new TF*IDF weighting scheme is proposed to extract discriminant textual features from HTML surrounding texts. Both content-based and context-based classifiers are built by applying multiple correspondence analysis (MCA). Experiments on web-based images from Microsoft Research Asia (MSRA-MM) dataset show that the proposed framework achieves promising results.
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Yakubu, Abdulmojeed, Praise Jegede, Mathew Wheto, Ayoola J. Shoyombo, Ayotunde O. Adebambo, Mustapha A. Popoola, Osamede H. Osaiyuwu, et al. "Multivariate characterisation of morpho-biometric traits of indigenous helmeted Guinea fowl (Numida meleagris) in Nigeria." PLOS ONE 17, no. 6 (June 13, 2022): e0261048. http://dx.doi.org/10.1371/journal.pone.0261048.
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This study was conducted to characterise phenotypically helmeted Guinea fowls in three agro-ecologies in Nigeria using multivariate approach. Eighteen biometric characters, four morphological indices and eleven qualitative physical traits were investigated in a total of 569 adult birds (158 males and 411 females). Descriptive statistics, non-parametric Kruskal–Wallis H test followed by the Mann–Whitney U and Dunn-Bonferroni tests for post hoc, Multiple Correspondence Analysis (MCA), Univariate Analysis, Canonical Discriminant Analysis, Categorical Principal Component Analysis and Decision Trees were employed to discern the effects of agro-ecological zone and sex on the morphostructural parameters. Agro-ecology had significant effect (P<0.05; P<0.01) on all the colour traits. In general, the most frequently observed colour phenotype of Guinea fowl had pearl plumage colour (54.0%), pale red skin colour (94.2%), black shank colour (68.7%), brown eye colour (49.7%), white earlobe colour (54.8%) and brown helmet colour (72.6%). The frequencies of helmet shape and wattle size were significantly influenced (P<0.01) by agro-ecology and sex. Overall, birds from the Southern Guinea Savanna zone had significantly higher values (P<0.05) for most biometric traits compared to their Sudano-Sahelian and Tropical Rainforest counterparts. They were also more compact (120.00 vs. 110.00 vs. 107.69) but had lesser condition index (7.66 vs. 9.45 vs. 9.30) and lower long-leggedness (19.71 vs. 19.23 vs. 9.51) than their counterparts from the two other zones. Sexual dimorphism (P<0.05) was in favour of male birds especially those in Southern Guinea Savanna and Sudano-Sahelian zones. However, the MCA and discriminant analysis revealed considerable intermingling of the qualitative physical traits, biometric traits and body indices especially between the Sudano-Sahelian and Tropical Rainforest birds. In spite of the high level of genetic admixture, the Guinea fowl populations could to a relative extent be distinguished using wing length, body length and eye colour. Generally, the birds from the three zones appeared to be more homogeneous than heterogeneous in nature. However, further complementary work on genomics will guide future selection and breeding programs geared towards improving the productivity, survival and environmental adaptation of indigenous helmeted Guinea fowls in the tropics.
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Hamid, Hashibah, Nor Idayu Mahat, and Safwati Ibrahim. "ADAPTIVE VARIABLE EXTRACTIONS WITH LDA FOR CLASSIFICATION OF MIXED VARIABLES, AND APPLICATIONS TO MEDICAL DATA." Journal of Information and Communication Technology 20, Number 3 (June 11, 2021): 305–27. http://dx.doi.org/10.32890/jict2021.20.3.2.
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The strategy surrounding the extraction of a number of mixed variables is examined in this paper in building a model for Linear Discriminant Analysis (LDA). Two methods for extracting crucial variables from a dataset with categorical and continuous variables were employed, namely, multiple correspondence analysis (MCA) and principal component analysis (PCA). However, in this case, direct use of either MCA or PCA on mixed variables is impossible due to restrictions on the structure of data that each method could handle. Therefore, this paper executes some adjustments including a strategy for managing mixed variables so that those mixed variables are equivalent in values. With this, both MCA and PCA can be performed on mixed variables simultaneously. The variables following this strategy of extraction were then utilised in the construction of the LDA model before applying them to classify objects going forward. The suggested models, using three real sets of medical data were then tested, where the results indicated that using a combination of the two methods of MCA and PCA for extraction and LDA could reduce the model’s size, having a positive effect on classifying and better performance of the model since it leads towards minimising the leave-one-out error rate. Accordingly, the models proposed in this paper, including the strategy that was adapted was successful in presenting good results over the full LDA model. Regarding the indicators that were used to extract and to retain the variables in the model, cumulative variance explained (CVE), eigenvalue, and a non-significant shift in the CVE (constant change), could be considered a useful reference or guideline for practitioners experiencing similar issues in future.
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Koyratty, Nadia, Andrew Jones, Roseanne Schuster, Katarzyna Kordas, Chin-Shang Li, Mduduzi Mbuya, Godfred Boateng, et al. "Food Insecurity and Water Insecurity in Rural Zimbabwe: Development of Multidimensional Household Measures." International Journal of Environmental Research and Public Health 18, no. 11 (June 3, 2021): 6020. http://dx.doi.org/10.3390/ijerph18116020.
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Background: With millions of people experiencing malnutrition and inadequate water access, FI and WI remain topics of vital importance to global health. Existing unidimensional FI and WI metrics do not all capture similar multidimensional aspects, thus restricting our ability to assess and address food- and water-related issues. Methods: Using the Sanitation, Hygiene and Infant Nutrition Efficacy (SHINE) trial data, our study conceptualizes household FI (N = 3551) and WI (N = 3311) separately in a way that captures their key dimensions. We developed measures of FI and WI for rural Zimbabwean households based on multiple correspondence analysis (MCA) for categorical data. Results: Three FI dimensions were retained: ‘poor food access’, ‘household shocks’ and ‘low food quality and availability’, as were three WI dimensions: ‘poor water access’, ‘poor water quality’, and ‘low water reliability’. Internal validity of the multidimensional models was assessed using confirmatory factor analysis (CFA) with test samples at baseline and 18 months. The dimension scores were associated with a group of exogenous variables (SES, HIV-status, season, depression, perceived health, food aid, water collection), additionally indicating predictive, convergent and discriminant validities. Conclusions: FI and WI dimensions are sufficiently distinct to be characterized via separate indicators. These indicators are critical for identifying specific problematic insecurity aspects and for finding new targets to improve health and nutrition interventions.
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Chai, Jing, Xinghao Ding, Hongtao Chen, and Tingyu Li. "Multiple-instance discriminant analysis." Pattern Recognition 47, no. 7 (July 2014): 2517–31. http://dx.doi.org/10.1016/j.patcog.2014.02.002.
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Cynthia, Cynthia, Janson Hendryli, and Dyah Erny Herwindiati. "KLASIFIKASI CITRA BATIK INDONESIA DAN MALAYSIA DENGAN METODE MODIFIED DISCRIMINANT ANALYSIS." Computatio : Journal of Computer Science and Information Systems 3, no. 1 (June 18, 2019): 11. http://dx.doi.org/10.24912/computatio.v3i1.2973.
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The application of Indonesian and Malaysian batik image classification using the Linear Discriminant Analysis (LDA) and Modified Discriminant Analysis (MDA) method is an introduction application that is used to classify images in the form of batik. Making this application uses the Java programming language to run feature retrieval methods, namely Color Histogram and Daubechies Wavelet and classification methods, namely LDA and MDA. Testing is done by blackbox testing method and confusion matrix. Tests are performed using color features, texture features, and a combination of training images and new test images. The best percentage test results are testing using color features, whereas with texture and the combination of both features get a slightly lower test percentage result.Aplikasi klasifikasi citra batik Indonesia dan Malaysia dengan metode Linear Discriminant Analysis (LDA) dan Modified Discriminant Analysis (MDA) merupakan aplikasi pengenalan yang digunakan untuk mengklasifikasi citra berupa batik. Pembuatan aplikasi ini menggunakan bahasa pemrograman Java untuk menjalankan metode pengambilan fitur yaitu Color Histogram dan Daubechies Wavelet dan metode pengklasifikasian yaitu LDA dan MDA. Pengujian dilakukan dengan metode blackbox testing dan matriks konfusi. Pengujian dilakukan dengan menggunakan fitur ciri warna, ciri tekstur, dan gabungan dari citra latih dan citra uji baru. Hasil persentase pengujian terbaik adalah pengujian dengan menggunakan ciri warna, sedangkan dengan ciri tekstur dan gabungan mendapatkan hasil persentase pengujian sedikit rendah.
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De Avila, J., A. Ramos-Casallas, D. Acero-M, C. Florez, V. Parra-Izquierdo, L. Chila, W. Bautista-Molano, et al. "POS0332 CD71 RECEPTOR APICAL EXPRESSION IN ILEUM IS RELATED WITH HIGH LEVELS OF SERUM SIgA AND ACTIVITY DISEASE IN SpA PATIENTS." Annals of the Rheumatic Diseases 81, Suppl 1 (May 23, 2022): 418.2–419. http://dx.doi.org/10.1136/annrheumdis-2022-eular.4196.
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BackgroundCurrently, mucosal immunity research has been investigated whether the serum levels of SIgA reflects the over-activation immune system in gut associated lymphoid tissues (GALT) and if the antigenic stimuli in the mucosa are responsible for inducing and maintaining the inflammatory response in Spondyloartrhitis (SpA). Based on our previous results, variances on levels of SIgA might influence in disease activity. SIgA Reverse transcytosis (retrotranscytosis) has been demonstrated as a GALT mechanism which enhance systemic inflammation in some autoimmune pathologies such as celiac disease.ObjectivesThis study proposes the evaluation of receptors associated with retrotranscytosis CD71 and Dectin-1 (Dec-1) in gut tissues from SpA without IBD patients to explore a possible mechanism responsible of gastrointestinal inflammatory imbalance.MethodsIn total, 180 patients with SpA (ASAS/criteria) were assessed by rheumatologists, of which (n=65, 36.1%) met the selection criteria and from them (n=41, 63.1%) by a gastroenterologist to perform digital chromoendoscopy with magnification for colon e ileum and histological analysis. Pregnant and lactating women, and cancer patients, patients with other autoinflammatory diseases, autoimmune diseases, immunodeficiency, chronic pancreatitis, or chronic liver disease, and those who had received antibiotic treatment in the last 3 months were excluded from the study. Furthermore, those patients with SpA and concomitant IBD were excluded. All included patients were between 18 and 65 years old. CD71 and Dec-1 apical expression were identified by indirect immunofluorescence. Fecal Calprotectin (FC), Serum SIgA in house and clinical indices BASDAI, BASFI, ASDAS-CRP, ASDAS-ESR were measured. The association were evaluated using the Chi-square or Fisher’s exact test and a multiple correspondence discriminant analysis (MCDA) was performed including those variables with significant associations from bivariate analysis and some that are considered clinically relevant to explain the impact of SIgA and CD71 in SpA.ResultsThe average age of the patients included was 44,6±10.2 years, 56.1% were men, 39.0% were HLA-B*27:05 positive, 90.2% had axial involvement. Serum levels of SIgA were 62.3±24.1 gr/mL, CRP 1.7±2.4 and ESR 14.1±12.0 mm/h. Positive levels of FC (>120ng/mL) were observed in 31.7%. BASDAI >4 was found in 58.5% of patients and ASDAS-CRP >2.1 in 75.6%. Apical expression of CD71 and Dec-1 in the ileum was observed in 48.8% and 36% respectively, the expression of both receptors in colon tissues were irrelevant. CD71 expression was associated with high levels of serum SIgA (p=0.05). However, no associations were observed between retrotranscytosis receptors and any of the clinical and histological variables. The MCDA showed a Cronbach’s Alpha reliability coefficient of 0.763 and showed two dimensions: a main dimension (Dim 1) related to the presence of loss of the vascular pattern in the ileum (CC 0.325), FC + diarrhea (CC 0.695), FC + abdominal distension (CC 0.883) and FC + abdominal pain (CC 0.885) and a secondary dimension (Dim 2) that collected the variables serum SIgA (CC 0.513), ASDAS-CRP >2.1 (CC 0.311), CD71 (CC 0.424), please see Figure 1.Figure 1.Multiple correspondence discriminant analysis for CD71, serum SIgA and activity index in SpA patientConclusionThe findings reflect a possible relationship among the apical expression of CD71in ileum with high levels of serum SIgA and activity, suggesting that retrotranscytosis mediated by this receptor might be a mechanism that mediate the intestine-joint axis in SpA.AcknowledgementsThe Ministry of Science, Technology, and Innovation - MinCiencias (Grants No. 68022 and 57442). Universidad El Bosque (PCI-2018-10300), Hospital Militar Central (Grant 2017-023), Clínicos IPS, Gastroadvanced, Fundación Instituto de Reumatología Fernando Chalem, in Bogota, Colombia and Biomedicina de Chihuahua, MéxicoDisclosure of InterestsNone declared
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Egbert, Jesse, and Douglas Biber. "Do all roads lead to Rome?: Modeling register variation with factor analysis and discriminant analysis." Corpus Linguistics and Linguistic Theory 14, no. 2 (September 25, 2018): 233–73. http://dx.doi.org/10.1515/cllt-2016-0016.
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Abstract Previous theoretical and empirical research on register variation has argued that linguistic co-occurrence patterns have a highly systematic relationship to register differences, because they both share the same functional underpinnings. The goal of this study is to test this claim through a comparison of two statistical techniques that have been used to describe register variation: factor analysis (as used in Multi-Dimensional analysis, MDA) and canonical discriminant analysis (CDA). MDA and CDA have different statistical bases and thus give priority to different analytical considerations: linguistic co-occurrence in the case of MDA and the prediction of register differences in the case of CDA. Thus, there is no statistical reason to expect that the two techniques, if applied to the same corpus, will produce similar results. We hypothesize that although MDA and CDA approach register variation from opposite sides, they will produce similar results because both types of statistical patterns are motivated by underlying discourse functions. The present paper tests this claim through a case-study analysis of variation among web registers, applying MDA and CDA to analyze register variation in the same corpus of texts.