Статті в журналах: "Discrete multivariate model"

1

Barr, Aiala. "A multivariate model for discrete data sets." Communications in Statistics - Theory and Methods 18, no. 2 (January 1989): 445–59. http://dx.doi.org/10.1080/03610928908829910.

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2

Barbiero, Alessandro. "Estimating a multivariate model with discrete Weibull margins." Journal of Statistical Theory and Practice 11, no. 4 (February 8, 2017): 503–14. http://dx.doi.org/10.1080/15598608.2017.1292483.

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3

Zilko, Aurelius A., and Dorota Kurowicka. "Copula in a multivariate mixed discrete–continuous model." Computational Statistics & Data Analysis 103 (November 2016): 28–55. http://dx.doi.org/10.1016/j.csda.2016.02.017.

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4

Zhang, Ke. "Multivariate discrete grey model base on dummy drivers." Grey Systems: Theory and Application 6, no. 2 (August 1, 2016): 246–58. http://dx.doi.org/10.1108/gs-09-2015-0051.

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Purpose – The purpose of this paper is to solve the problem that the qualitative relative factors cannot be employed in traditional multivariate grey models. Design/methodology/approach – First, a new model is constructed though introducing dummy drivers. Then, the parameters estimation method and recursive function of the model are discussed. Furthermore, dummy driver setting, pre and post test methods of dummy drivers are proposed. At last, the per capita income forecasting of rural residents in Henan province of China is solved with the proposed model. Findings – The proposed model is the reasonable extension of original one. The accuracy of it is higher than former model. In the case study, the forecasting results of proposed model are compared with other grey forecasting models, and prove that proposed model has not only high accuracy, but also clear physical meaning. Practical implications – The method proposed in the paper could be used in policy effect measure, marketing forecasting, etc., when the predictor variables are influenced by some qualitative variables. Originality/value – It will promote the accuracy of multivariate grey forecasting model.

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5

Douglas, Jeffrey A., Michael R. Kosorok, and Betty A. Chewning. "A latent variable model for discrete multivariate psychometric waiting times." Psychometrika 64, no. 1 (March 1999): 69–82. http://dx.doi.org/10.1007/bf02294320.

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6

Liu, Jiwei. "Test of Ordered Multivariate Discrete Selection Model for Average Life Expectancy." Journal of Applied Mathematics and Physics 10, no. 02 (2022): 261–69. http://dx.doi.org/10.4236/jamp.2022.102020.

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7

Golob, Thomas F., and Amelia C. Regan. "Trucking industry adoption of information technology: a multivariate discrete choice model." Transportation Research Part C: Emerging Technologies 10, no. 3 (June 2002): 205–28. http://dx.doi.org/10.1016/s0968-090x(02)00006-2.

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8

Ma, Xin, Mei Xie, Wenqing Wu, Bo Zeng, Yong Wang, and Xinxing Wu. "The novel fractional discrete multivariate grey system model and its applications." Applied Mathematical Modelling 70 (June 2019): 402–24. http://dx.doi.org/10.1016/j.apm.2019.01.039.

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9

Lim, Yaeji, Ying Kuen Cheung, and Hee-Seok Oh. "A generalization of functional clustering for discrete multivariate longitudinal data." Statistical Methods in Medical Research 29, no. 11 (May 5, 2020): 3205–17. http://dx.doi.org/10.1177/0962280220921912.

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This paper presents a new model-based generalized functional clustering method for discrete longitudinal data, such as multivariate binomial and Poisson distributed data. For this purpose, we propose a multivariate functional principal component analysis (MFPCA)-based clustering procedure for a latent multivariate Gaussian process instead of the original functional data directly. The main contribution of this study is two-fold: modeling of discrete longitudinal data with the latent multivariate Gaussian process and developing of a clustering algorithm based on MFPCA coupled with the latent multivariate Gaussian process. Numerical experiments, including real data analysis and a simulation study, demonstrate the promising empirical properties of the proposed approach.

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10

Bai, Hao, Yuan Zhong, Xin Gao, and Wei Xu. "Multivariate Mixed Response Model with Pairwise Composite-Likelihood Method." Stats 3, no. 3 (July 15, 2020): 203–20. http://dx.doi.org/10.3390/stats3030016.

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In clinical research, study outcomes usually consist of various patients’ information corresponding to the treatment. To have a better understanding of the effects of different treatments, one often needs to analyze multiple clinical outcomes simultaneously, while the data are usually mixed with both continuous and discrete variables. We propose the multivariate mixed response model to implement statistical inference based on the conditional grouped continuous model through a pairwise composite-likelihood approach. It can simplify the multivariate model by dealing with three types of bivariate models and incorporating the asymptotical properties of the composite likelihood via the Godambe information. We demonstrate the validity and the statistic power of the multivariate mixed response model through simulation studies and clinical applications. This composite-likelihood method is advantageous for statistical inference on correlated multivariate mixed outcomes.

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11

Geraldo, Issa Cherif. "On the maximum likelihood estimator for a discrete multivariate crash frequencies model." Afrika Statistika 15, no. 2 (April 1, 2020): 2335–48. http://dx.doi.org/10.16929/as/2020.2325.161.

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In this paper, we study the maximum likelihood estimator (MLE) of the parameter vector of a discrete multivariate crash frequencies model used in the statistical analysis of the effectiveness of a road safety measure. We derive the closed-form expression of the MLE afterwards we prove its strong consistency and we obtain the exact variance of the components of the MLE except one component whose variance is approximated via the delta method.

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12

Terui, Nobuhiko, and Masataka Ban. "Multivariate Time Series Model with Hierarchical Structure for Over-Dispersed Discrete Outcomes." Journal of Forecasting 33, no. 5 (May 23, 2014): 376–90. http://dx.doi.org/10.1002/for.2301.

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13

Omori, Yasuhiro, and Richard A. Johnson. "Efficient Semiparametric Bayesian Estimation of Multivariate Discrete Proportional Hazards Model with Random Effects." Communications in Statistics - Theory and Methods 38, no. 1 (October 18, 2008): 29–41. http://dx.doi.org/10.1080/03610920802155478.

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14

He, Zhi, Qiang Wang, Yi Shen, and Yan Wang. "Discrete multivariate gray model based boundary extension for bi-dimensional empirical mode decomposition." Signal Processing 93, no. 1 (January 2013): 124–38. http://dx.doi.org/10.1016/j.sigpro.2012.07.009.

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15

Schliep, Erin M., and Jennifer A. Hoeting. "Multilevel Latent Gaussian Process Model for Mixed Discrete and Continuous Multivariate Response Data." Journal of Agricultural, Biological, and Environmental Statistics 18, no. 4 (April 11, 2013): 492–513. http://dx.doi.org/10.1007/s13253-013-0136-z.

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16

Ma, Xin, and Zhi-bin Liu. "Research on the novel recursive discrete multivariate grey prediction model and its applications." Applied Mathematical Modelling 40, no. 7-8 (April 2016): 4876–90. http://dx.doi.org/10.1016/j.apm.2015.12.021.

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17

Neelon, Brian, Azza Shoaibi, and Sara E. Benjamin‐Neelon. "A multivariate discrete failure time model for the analysis of infant motor development." Statistics in Medicine 38, no. 9 (November 28, 2018): 1543–57. http://dx.doi.org/10.1002/sim.8055.

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18

K. Naveen Kumar,. "Multivariate Approach for Texture Segmentation using Probabilistic Statistical Model." Journal of Electrical Systems 20, no. 2 (April 4, 2024): 2381–85. http://dx.doi.org/10.52783/jes.2003.

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The analysis of the regions of the image is of the prerogatives in the fields of medical and global systems meant for location identification. This analysis is strongly associated with partitions of regions of interest such as segmentation. For an effective strategy of analyzing the regions of interest, texture of the image plays a major concern. The texture is generally characterized using signal processing methods namely Discrete Cosine Transformation coefficients and their specific insights leading to feature vector selection. Further, to identify regions, a statistical model needs to be identified for the feature matrix vector and thus make use of Gaussian mixture model with extensions. The Expectation Maximization approach is used, and performance is assessed by experimenting with random images from the Brodatz data store domain. Performance measurements for texture segmentation that can be attributed are Global Cons. Error (GC), Prob. Rand. Index (PR) and Variation of data (VA). These are determined alongside the confusion matrix. To assess the improvement, a comparison was made with other existing models and showed better. The algorithms will be exceptionally helpful for clinical analysis and in radio navigation map systems.

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19

Maybank, S. J. "A multivariate distribution for sub-images." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 465, no. 2103 (December 16, 2008): 983–1001. http://dx.doi.org/10.1098/rspa.2008.0212.

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A new method for obtaining multivariate distributions for sub-images of natural images is described. The information in each sub-image is summarized by a measurement vector in a measurement space. The dimension of the measurement space is reduced by applying a random projection to the truncated output of the discrete cosine transforms of the sub-images. The measurement space is then reparametrized, such that a Gaussian distribution is a good model for the measurement vectors in the reparametrized space. An Ornstein–Uhlenbeck process, associated with the Gaussian distribution, is used to model the differences between measurement vectors obtained from matching sub-images. The probability of a false alarm and the probability of accepting a correct match are calculated. The accuracy of the resulting statistical model for matching sub-images is tested using images from the Middlebury stereo database with promising results. In particular, if the probability of accepting a correct match is relatively large, then there is good agreement between the calculated and the experimental probabilities of obtaining a unique match that is also a correct match.

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20

Pigeon, Mathieu, Katrien Antonio, and Michel Denuit. "INDIVIDUAL LOSS RESERVING WITH THE MULTIVARIATE SKEW NORMAL FRAMEWORK." ASTIN Bulletin 43, no. 3 (August 6, 2013): 399–428. http://dx.doi.org/10.1017/asb.2013.20.

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AbstractThe evaluation of future cash flows and solvency capital recently gained importance in general insurance. To assist in this process, our paper proposes a novel loss reserving model, designed for individual claims developing in discrete time. We model the occurrence of claims, as well as their reporting delay, the time to the first payment, and the cash flows in the development process. Our approach uses development factors similar to those of the well-known chain–ladder method. We suggest the Multivariate Skew Normal distribution as a multivariate distribution suitable for modeling these development factors. Empirical analysis using a real portfolio and out-of-sample prediction tests demonstrate the relevance of the model proposed.

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21

Aristodemou, Eleni, and Adam M. Rosen. "A discrete choice model for partially ordered alternatives." Quantitative Economics 13, no. 3 (2022): 863–906. http://dx.doi.org/10.3982/qe1497.

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In this paper, we analyze a discrete choice model for partially ordered alternatives. The alternatives are differentiated along two dimensions: the first an unordered “horizontal” dimension, and the second an ordered “vertical” dimension. The model can be used in circumstances in which individuals choose among products of different brands, wherein each brand offers an ordered choice menu, for example, by offering products of varying quality. The unordered–ordered nature of the discrete choice problem is used to characterize the identified set of model parameters. Following an initial nonparametric analysis that relies on shape restrictions inherent in the ordered dimension of the problem, we then provide a specialized analysis for parametric specifications that generalize common ordered choice models. We characterize conditional choice probabilities as a function of model primitives with particular analysis focusing on cases in which unobservable taste for quality of each brand offering is multivariate normally distributed. We provide explicit formulae used for estimation and inference via maximum likelihood, and we consider inference based on Wald and quasi‐likelihood ratio statistics, the latter of which can be robust to a possible lack of point identification. An empirical illustration is conducted using data on razor blade purchases in which each brand has product offerings vertically differentiated by quality.

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22

Tang, Ying. "Monitoring and Visualization of Physical Exercise Physiological Indicators Driven by Discrete Data." Scientific Programming 2022 (March 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/8297739.

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Physical exercise physiological index monitoring has a wide range of applications in the fields of physiological index planning and design and organizational network evolution. Among the existing analysis methods for monitoring data points of physical exercise physiological indicators, the analysis error of point events under linear constraints is relatively large. Based on discrete data-driven datasets, this paper realizes the monitoring and visualization of sports physiological indicators. First, the principal component analysis of multivariate discrete data is used for dimensionality reduction. Second, the clustering of discrete physical exercise data uses the BIC criterion to preset the number of clusters, and the R software is used to visually realize the clustering results of physical exercise physiological indicators in each region in the text. The experiment solves the problem of mismatch of model parameter combinations when the physical exercise index monitoring quantity is used for the auxiliary analysis of the clustering results. Through the ARI index monitoring, the accuracy of the clustering physical exercise results of the method is increased to 89.7%, and the error rate is controlled within 4.3%. It promotes the superiority and effectiveness of multivariate discrete data-driven model clustering methods.

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23

Siu, D. P., and G. S. Ladde. "A Multivariate Stochastic Hybrid Model with Switching Coefficients and Jumps: Solution and Distribution." Journal of Probability and Statistics 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/720614.

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In this work, a class of multidimensional stochastic hybrid dynamic models is studied. The system under investigation is a first-order linear nonhomogeneous system of Itô-Doob type stochastic differential equations with switching coefficients. The switching of the system is governed by a discrete dynamic which is monitored by a non-homogeneous Poisson process. Closed-form solutions of the systems are obtained. Furthermore, the major part of the work is devoted to finding closed-form probability density functions of the solution processes of linear homogeneous and Ornstein-Uhlenbeck type systems with jumps.

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24

Manton, Kenneth G., and Max A. Woodbury. "A Continuous-Time Multivariate Gaussian Stochastic Model of Change in Discrete and Continuous Variables." Sociological Methodology 15 (1985): 277. http://dx.doi.org/10.2307/270853.

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25

Zeng, Fan, Xiao Cao, and Huan-Ming Zhang. "Analysis of Digital Life Effect of Residents’ Trust Based on Multivariate Discrete Choice Model." Journal of Function Spaces 2022 (July 18, 2022): 1–14. http://dx.doi.org/10.1155/2022/1801399.

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In view of the impact of digital life on residents’ trust, firstly, based on the data of China Household Finance Survey (CHFS), this paper analyzes the change of residents’ trust in the era of data flooding. Through basic research and marginal effect analysis, it is found that digital life enhances residents’ trust. Secondly, the robustness of the conclusion is confirmed by constructing a series of models such as ordered probit of instrumental variable (IV-O probit), ordered probit of propensity score matching method (PSM-O probit), and ordered probit Heckman (O-probit-Heckman). Finally, through the mechanism test, it is concluded that digital life can enhance residents’ trust by increasing social activities and forming an inclusive social environment. It will also increase trust by obtaining information and increasing their cognitive level. The conclusion that digital life can enhance residents’ trust is more prominent among urban residents, nonpoor Hukou residents, and Han residents. Further research shows that the breadth and depth of digital life have a positive impact on residents’ trust, and digital life can improve residents’ risk tolerance.

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26

Bhat, Chandra R., Sebastian Astroza, Raghuprasad Sidharthan, and Prerna C. Bhat. "A multivariate hurdle count data model with an endogenous multiple discrete–continuous selection system." Transportation Research Part B: Methodological 63 (May 2014): 77–97. http://dx.doi.org/10.1016/j.trb.2014.02.006.

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27

KONLSHI, KATSUNOBU, KOUJI MAEGAWA, TOSHIO YOSHIMURA, and HIROKAZU ISHIHARA. "Multivariate discrete-time model reference adaptive control with input-smoothing property and its application." International Journal of Systems Science 19, no. 5 (January 1988): 761–78. http://dx.doi.org/10.1080/00207728808967641.

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28

Freeman, Guy, and Jim Q. Smith. "Dynamic staged trees for discrete multivariate time series: forecasting, model selection and causal analysis." Bayesian Analysis 6, no. 2 (June 2011): 279–305. http://dx.doi.org/10.1214/11-ba610.

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29

Wang, Huiping, and Zhun Zhang. "A spatial lagged multivariate discrete grey model for forecasting an economy-energy-environment system." Journal of Cleaner Production 404 (June 2023): 136922. http://dx.doi.org/10.1016/j.jclepro.2023.136922.

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30

Warton, David I., Loïc Thibaut, and Yi Alice Wang. "The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses." PLOS ONE 12, no. 7 (July 24, 2017): e0181790. http://dx.doi.org/10.1371/journal.pone.0181790.

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31

Li, Ruidong, Xiancheng Wang, Zeng Wang, Zhentao Zhu, and Zewen Liu. "Multistage Quality Prediction Using Neural Networks in Discrete Manufacturing Systems." Applied Sciences 13, no. 15 (July 29, 2023): 8776. http://dx.doi.org/10.3390/app13158776.

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The deployment of a manufacturing execution system (MES) holds promising potential in facilitating the accumulation of a substantial amount of inspection data. Low quality levels in discrete manufacturing environments are the result of multi-factor coupling and the failure to detect quality issues promptly in accordance with manufacturing settings, which may trigger the propagation of downstream defects. Currently, most inspection quality methods consist of direct measurements followed by manual judgment. The integration of deep learning methods provides a feasible way in which to identify defects on time, thus improving the acceptance rate of factories. This paper focuses on the design of a data-driven quality prediction and control model, built around discrete manufacturing characteristics, and uses fuzzy theory to evaluate the quality levels of production stages. Furthermore, a multivariate long- and short-term memory sequence model is proposed in order to explore the qualitative information from time domain features. The data regarding the produced water dispensers are validated using three evaluation indices, namely, RMSE, MAE, and MAPE. The results indicate that the multivariate long- and short-term memory model exhibits stronger prediction performance.

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32

Ivanov, A., and I. Savych. "On the least squares estimator asymptotic normality of the multivariate symmetric textured surface parameters." Theory of Probability and Mathematical Statistics 105 (December 7, 2021): 151–69. http://dx.doi.org/10.1090/tpms/1161.

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A multivariate trigonometric regression model is considered. Various discrete modifications of the similar bivariate model received serious attention in the literature on signal and image processing due to multiple applications in the analysis of symmetric textured surfaces. In the paper asymptotic normality of the least squares estimator for amplitudes and angular frequencies is obtained in multivariate trigonometric model assuming that the random noise is a homogeneous or homogeneous and isotropic Gaussian, in particular, strongly dependent random field on R M , M > 2. \mathbb {R}^M,\,\, M>2.

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33

Sreedharan, S., G. R. Poornima, Meena Nair, Genanew Bekele, Wathiq Mansoor, and Ananth Rao. "Users’ Satisfaction of Public Utility Services – Multivariate System Analysis." International Conference on Advances in Business, Management and Law (ICABML) 2017 1, no. 1 (December 24, 2017): 30–60. http://dx.doi.org/10.30585/icabml-cp.v1i1.12.

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This research develops a multivariate system framework for assessing user satisfaction of public water utility organization in a developing country and predicts quality, quantity and overall user satisfaction for policy initiatives. The model framework is applied to the data collected by Public Affairs Centre (PAC) based on the Citizen Report Card approach pioneered by it. Wald test confirms that there exits cross equation correlation across quality, quantity and overall users’ satisfaction dimensions. Based on the system model, the study identifies statistically significant factors that explain users’ loyalty to express satisfaction and voice to express dissatisfaction of users. Policy initiatives are proposed on key factors to reduce voice factors set so that the service provider could improve its service delivery. The system model correctly predicts 85% of satisfied customers across quality, quantity and overall satisfaction dimensions. Keywords: Multivariate, Logit, Discrete choice Model, Public Sector Utility service provider

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34

Wang, Zhi Guo. "Dynamic Method for Portfolio Choice of Manufacturing Processes Based on Copulas." Applied Mechanics and Materials 44-47 (December 2010): 562–66. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.562.

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Multivariate volatility modeling is always a hot topic in academic research. It is difficult to consider how to construct multivariate joint distribution. Copulas, a statistic method, can be used to decompose multivariate joint distribution into marginal distribution and correlation structure. This advantage is applied into calculating the dynamic VAR of a portfolio in the paper. Furthermore, a new model for dynamic portfolio choice based on copulas is proposed, and empirical analysis is operated for the several typical discrete manufacturing processes in the end.

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35

Islam, Md Rabiul, Md Rashed-Al-Mahfuz, Shamim Ahmad, and Md Khademul Islam Molla. "Multiband Prediction Model for Financial Time Series with Multivariate Empirical Mode Decomposition." Discrete Dynamics in Nature and Society 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/593018.

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This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predicted subband signals are summed up to obtain the overall prediction. The ARMA model works better for stationary signal. With multiband representation, each subband becomes a band-limited (narrow band) signal and hence better prediction is achieved. The performance of the proposed MEMD-ARMA model is compared with classical EMD, discrete wavelet transform (DWT), and with full band ARMA model in terms of signal-to-noise ratio (SNR) and mean square error (MSE) between the original and predicted time series. The simulation results show that the MEMD-ARMA-based method performs better than the other methods.

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36

Spaces, Journal of Function. "Retracted: Analysis of Digital Life Effect of Residents’ Trust Based on Multivariate Discrete Choice Model." Journal of Function Spaces 2023 (September 14, 2023): 1. http://dx.doi.org/10.1155/2023/9792714.

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37

Robinson, Peter M. "ON DISCRETE SAMPLING OF TIME-VARYING CONTINUOUS-TIME SYSTEMS." Econometric Theory 25, no. 4 (August 2009): 985–94. http://dx.doi.org/10.1017/s0266466608090373.

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We consider a multivariate continuous-time process, generated by a system of linear stochastic differential equations, driven by white noise, and involving coefficients that possibly vary over time. The process is observable only at discrete, but not necessarily equally-spaced, time points (though equal spacing significantly simplifies matters). Such settings represent partial extensions of ones studied extensively by A.R. Bergstrom. A model for the observed time series is deduced. Initially we focus on a first-order model, but higher-order models are discussed in the case of equally-spaced observations. Some discussion of issues of statistical inference is included.

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38

Kustra, Rafal. "Reduced-Rank Regularized Multivariate Model for High-Dimensional Data." Journal of Computational and Graphical Statistics 15, no. 2 (June 2006): 312–38. http://dx.doi.org/10.1198/016214506x112416.

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39

Kakkeri, Roopa B., and Dattatraya S. Bormane. "Electromyography Signal Analysis for the Detection of TMJ Disorder Using Classification Models and Multivariate Analysis." Revue d'Intelligence Artificielle 36, no. 3 (June 30, 2022): 503–8. http://dx.doi.org/10.18280/ria.360320.

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Temporomandibular joint (TMJ) disorder is a wide term that encompasses a variety of disorders with varying etiologies. The purpose of this study was to analyses electromyographic signals with wavelet transform for the diagnostic methods which help in TMJ issues in patients who visited the Dental Centre. Due to the increasing importance of electromyography signals in diagnosing muscular disorders, such as temporomandibular joint disorder, it has been widely used. Through various techniques, such as discrete wavelet transform and power spectral density, it is possible to identify and minimize the noise in the signals, which can be very useful in the diagnosis of the disorder. This paper presents an algorithm that combines the features of discrete wavelet transform and multivariate analysis in order to detect temporomandibular joint disorder. Support vector machine model is giving the better performance in terms of training, testing time and accuracy with 93% compared to other models. Multivariate analysis shows the significant difference in the feature variable chosen.

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40

Ma, Jie, Xin Ye, and Abdul Rawoof Pinjari. "Practical Method to Simulate Multiple Discrete-Continuous Generalized Extreme Value Model: Application to Examine Substitution Patterns of Household Transportation Expenditures." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 8 (April 16, 2019): 145–56. http://dx.doi.org/10.1177/0361198119842819.

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The multiple discrete-continuous generalized extreme value (MDCGEV) model has been derived from multivariate extreme value (MEV)-based stochastic specifications to relax the independence assumption in the multiple discrete-continuous extreme value (MDCEV) model. It is analogous to the situation where a generalized extreme value (GEV) model relaxes the same assumption in a multinomial logit (MNL) model. However, unlike the case of single discrete choice model where substitution patterns can be understood based on elasticity expressions for a change in the value of an explanatory variable, the MDCEV and its variants do not offer closed-form elasticity expressions. The predictions must be compared explicitly under the base case and policy case scenarios. To perform a prediction exercise with MDCEV or its variants, random samples have to be drawn from the relevant stochastic distributions, which is actually not a straightforward task. In this paper, a practical method is proposed for drawing from an MEV distribution and the method is demonstrated to examine substitution patterns in an MDCGEV model for household transportation expenditures. The empirical results show that the cross-elasticities of explanatory variables in the MDCGEV model exhibit more variations than those in MDCEV and multiple discrete-continuous nested extreme value (MDCNEV) models.

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41

Yasmirullah, Septia Devi Prihastuti, Bambang Widjanarko Otok, Jerry Dwi Trijoyo Purnomo, and Dedy Dwi Prastyo. "A hybrid model of spatial autoregressive-multivariate adaptive generalized Poisson regression spline." Decision Science Letters 12, no. 4 (2023): 721–28. http://dx.doi.org/10.5267/j.dsl.2023.7.004.

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Several Multivariate Adaptive Regression Spline (MARS) approaches are available to model categorical and numerical (especially continuous) data. Currently, there are other numerical data types—discrete or count data—that call for specific consideration in modeling. Additionally, spatially correlated count data is frequently observed. This has been seen in the case of health data, for example, the number of newborn fatalities, tuberculosis patients, hospital visitors, etc. However, currently no structurally consistent nonparametric regression and MARS model for count data incorporating spatial lag autocorrelation. The SAR-MAGPRS estimator (Spatial Autoregressive - Multivariate Adaptive Generalized Poisson Regression Spline) is developed to fill this gap. Although it can be applied to different count distributions, the estimator was developed in this study under the assumption of a Generalized Poisson distribution. This paper provides an information-theoretic framework for incorporating knowledge of the spatial structure and non-parametric regression models, especially MARS for the count data types. Moreover, the proposed method can assist in modeling the number of diseases while health policies are being developed. The framework presents an application of the Penalized Least Square (PLS) method to estimate the SAR – MAGPRS model.

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Pandey, Ranjita. "Posterior Analysis of State Space Model with Spherical Symmetricity." Journal of Probability and Statistics 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/612024.

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The present work investigates state space model with nonnormal disturbances when the deviation from normality has been observed only with respect to kurtosis and the distribution of disturbances continues to follow a symmetric family of distributions. Spherically symmetric distribution is used to approximate behavior of symmetric nonnormal disturbances for discrete time series. The conditional posterior densities of the involved parameters are derived, which are further utilized in Gibbs sampler scheme for estimating the marginal posterior densities. The state space model with disturbances following multivariate-tdistribution, which is a particular case of spherically symmetric distribution, is discussed.

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Hong, Shaoyong, Chun Yang, Shitong Ye, and Shaohong Chen. "Fusion Evaluation of College Cultivation by Adaptive Multivariate Neural Network Model." Computational Intelligence and Neuroscience 2022 (August 8, 2022): 1–10. http://dx.doi.org/10.1155/2022/1449753.

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The quality of graduates is the key factor in evaluating the cultivation effect of colleges and universities. Quantification of whether the graduates qualify for their working post in companies and industries provides conduction for further college cultivation reform enhancement. In this work, we proposed an adaptive multivariate neural network architecture for fusion evaluation of college student cultivation. Specifically, we designed a questionnaire to collect data on the current working status of 1231 graduates and recorded 32 in-school training items categorized into four different modules. For quantitative evaluation, 10 indices of career-require competence were set to describe the graduates’ job abilities. The fused contribution of the in-school training items to the career-required competence was predicted by the multivariate network model with the linking weights adaptively trained. A comprehensive contribution matrix was generated by discrete PCA multivariate transforming to provide a digital reference for the network training. A 7-level scoring system was designed for quantifying the contribution matrix. For model optimization, the network structure was tuned by testing a different number of hidden nodes. The model was trained and optimized to reveal the direct correlation between college cultivation and job-required abilities. Experimental results indicated that the methodology we proposed is feasible to evaluate the cultivation mode in colleges and universities, theoretically and technically providing positive directions for colleges and universities to make their cultivation reforming, as to enhance the quality of their graduates.

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44

Glasgow, Garrett. "Mixed Logit Models for Multiparty Elections." Political Analysis 9, no. 2 (2001): 116–36. http://dx.doi.org/10.1093/oxfordjournals.pan.a004867.

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Mixed logit (MXL) is a general discrete choice model thus far unexamined in the study of multicandidate and multiparty elections. Mixed logit assumes that the unobserved portions of utility are a mixture of an IID extreme value term and another multivariate distribution selected by the researcher. This general specification allows MXL to avoid imposing the independence of irrelevant alternatives (IIA) property on the choice probabilities. Further, MXL is a flexible tool for examining heterogeneity in voter behavior through random-coefficients specifications. MXL is a more general discrete choice model than multinomial probit (MNP) in several respects, and can be applied to a wider variety of questions about voting behavior than MNP. An empirical example using data from the 1987 British General Election demonstrates the utility of MXL in the study of multicandidate and multiparty elections.

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Kaiser, Klaus L. E., Stefan P. Niculescu, and Gerrit Schüürmann. "Feed Forward Backpropagation Neural Networks and their Use in Predicting the Acute Toxicity of Chemicals to the Fathead Minnow." Water Quality Research Journal 32, no. 3 (August 1, 1997): 637–58. http://dx.doi.org/10.2166/wqrj.1997.037.

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Abstract Various aspects connected to the use of feed forward backpropagation neural networks to build multivariate QSARs based on large data sets containing considerable amounts of important information are investigated. Based on such a model and a 419 compound data set, the explicit equation of one of the resulting multivariate QSARs for the computation of toxicity to the fathead minnow is presented as function of measured Microtox, logarithms of molecular weight and octanol/water partition coefficient, and 48 other functional group and discrete descriptors.

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Punzo, Antonio, and Antonello Maruotti. "Clustering Multivariate Longitudinal Observations: The Contaminated Gaussian Hidden Markov Model." Journal of Computational and Graphical Statistics 25, no. 4 (October 1, 2016): 1097–98. http://dx.doi.org/10.1080/10618600.2015.1089776.

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Csik, Valerie P., Michael Li, Adam F. Binder, and Nathan R. Handley. "Development of an Oncology Acute Care Risk Prediction Model." JCO Clinical Cancer Informatics, no. 5 (March 2021): 266–71. http://dx.doi.org/10.1200/cci.20.00146.

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PURPOSE Acute care utilization (ACU), including emergency department (ED) visits or hospital admissions, is common in patients with cancer and may be preventable. The Center for Medicare & Medicaid Services recently implemented OP-35, a measure in the Hospital Outpatient Quality Reporting Program focused on ED visits and inpatient admissions for 10 potentially preventable conditions that arise within 30 days of chemotherapy. This new measure exemplifies a growing focus on preventing unnecessary ACU. However, identifying patients at high risk of ACU remains a challenge. We developed a real-time clinical prediction model using a discrete point allocation system to assess risk for ACU in patients with active cancer. METHODS We performed a retrospective cohort analysis of patients with active cancer from a large urban academic medical center. The primary outcome, ACU, was evaluated using a multivariate logistic regression model with backward variable selection. We used estimates from the multivariate logistic model to construct a risk index using a discrete point allocation system. RESULTS Eight thousand two hundred forty-six patients were included in the analysis. ED utilization in the last 90 days, history of chronic obstructive pulmonary disease, congestive heart failure or renal failure, and low hemoglobin and low neutrophil count significantly increased risk for ACU. The model produced an overall C-statistic of 0.726. Patients defined as high risk (achieving a score of 2 or higher on the risk index) represented 10% of total patients and 46% of ACU. CONCLUSION We developed an oncology acute care risk prediction model using a risk index–based scoring system, the REDUCE (Reducing ED Utilization in the Cancer Experience) score. Further efforts to evaluate the effectiveness of our model in predicting ACU are ongoing.

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Wang, Yan Ming, Gou Qing Shi, Xiao Xing Zhong, and De Ming Wang. "PLS Regression on Coal Infrared Spectrum with Wavelet Pre-Processing." Applied Mechanics and Materials 80-81 (July 2011): 279–83. http://dx.doi.org/10.4028/www.scientific.net/amm.80-81.279.

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Study on multivariate calibration for infrared spectrum of coal was presented. The discrete wavelet transformation as pre-processing tool was carried out to decompose the infrared spectrum and compress the data set. The compressed data regression model was applied to simultaneous multi-component determination for coal contents. Compression performance with several wavelet functions at different resolution scales was studied, and prediction ability of the compressed regression model was investigated. Numerical experiment results show that the wavelet transform performs an effective compression preprocessing technique in multivariate calibration and enhances the ability in characteristic extraction of coal infrared spectrum. Using the compressed data regression model, the reconstructing results are almost identical compared to the original spectrum, and the original size of the data set has been reduced to about 5% while the computational time needed decreases significantly.

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Nakayama, Shoichiro, and Makoto Chikaraishi. "Doubly generalized logit: A closed-form discrete choice model system with multivariate generalized extreme value distributed utilities." Transportation Research Part C: Emerging Technologies 132 (November 2021): 103315. http://dx.doi.org/10.1016/j.trc.2021.103315.

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Have, Thomas R. Ten. "A Mixed Effects Model for Multivariate Ordinal Response Data Including Correlated Discrete Failure Times with Ordinal Responses." Biometrics 52, no. 2 (June 1996): 473. http://dx.doi.org/10.2307/2532888.

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