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Articles de revues sur le sujet « Mixed data types »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 11 mars 2023

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1

Wang, Lu, Dongxiao Zhu, and Ming Dong. "Clustering over-dispersed data with mixed feature types." Statistical Analysis and Data Mining: The ASA Data Science Journal 11, no. 2 (2018): 55–65. http://dx.doi.org/10.1002/sam.11369.

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Sánchez-Borrego, I., J. D. Opsomer, M. Rueda, and A. Arcos. "Nonparametric estimation with mixed data types in survey sampling." Revista Matemática Complutense 27, no. 2 (2013): 685–700. http://dx.doi.org/10.1007/s13163-013-0142-2.

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Jørgensen, Bent, Søren Lundbye-Christensen, Peter Xue-Kun Song, and Li Sun. "State-space models for multivariate longitudinal data of mixed types." Canadian Journal of Statistics 24, no. 3 (1996): 385–402. http://dx.doi.org/10.2307/3315747.

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Einarsson, Bo. "Mixed language programming realization and the provision of data types." ACM SIGNUM Newsletter 21, no. 1-2 (1986): 2–9. http://dx.doi.org/10.1145/15983.15984.

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Yoon, Grace, Raymond J. Carroll, and Irina Gaynanova. "Sparse semiparametric canonical correlation analysis for data of mixed types." Biometrika 107, no. 3 (2020): 609–25. http://dx.doi.org/10.1093/biomet/asaa007.

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Summary Canonical correlation analysis investigates linear relationships between two sets of variables, but it often works poorly on modern datasets because of high dimensionality and mixed data types such as continuous, binary and zero-inflated. To overcome these challenges, we propose a semiparametric approach to sparse canonical correlation analysis based on the Gaussian copula. The main result of this paper is a truncated latent Gaussian copula model for data with excess zeros, which allows us to derive a rank-based estimator of the latent correlation matrix for mixed variable types withou

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Amatya, Anup, and Hakan Demirtas. "Concurrent generation of multivariate mixed data with variables of dissimilar types." Journal of Statistical Computation and Simulation 86, no. 18 (2016): 3595–607. http://dx.doi.org/10.1080/00949655.2016.1177530.

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Sun, Jinhui, Pang Du, Hongyu Miao, and Hua Liang. "Robust feature screening procedures for single and mixed types of data." Journal of Statistical Computation and Simulation 90, no. 7 (2020): 1173–93. http://dx.doi.org/10.1080/00949655.2020.1719104.

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Vogl, Susanne. "Integrating and Consolidating Data in Mixed Methods Data Analysis: Examples From Focus Group Data With Children." Journal of Mixed Methods Research 13, no. 4 (2018): 536–54. http://dx.doi.org/10.1177/1558689818796364.

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The challenge in data analysis often lies in accounting for the multidimensionality and complexity of the data while simultaneously discovering patterns. Integrating and consolidating different types of data during analysis can broaden the perspective and permit obtaining complementary views. This methodological research study on data collection illustrates how one type of data collection generates different types of data, which can be linked and consolidated to reach a better understanding of the topic. Procedures and practicalities are illustrated to offer a good practice example for data in

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Harch, B. D., K. E. Basford, I. H. DeLacy, P. K. Lawrence, and A. Cruickshank. "Mixed data types and the use of pattern analysis on the Australian groundnut germplasm data." Genetic Resources and Crop Evolution 43, no. 4 (1996): 363–76. http://dx.doi.org/10.1007/bf00132957.

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Huang, Mingze, Christian Müller, and Irina Gaynanova. "latentcor: An R Package for estimating latent correlations from mixed data types." Journal of Open Source Software 6, no. 65 (2021): 3634. http://dx.doi.org/10.21105/joss.03634.

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Brus, D. J., P. A. Slim, G. Gort, A. H. Heidema, and H. van Dobben. "Monitoring habitat types by the mixed multinomial logit model using panel data." Ecological Indicators 67 (August 2016): 108–16. http://dx.doi.org/10.1016/j.ecolind.2016.02.043.

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Zhao, Jing Hua, and Jian'an Luan. "Mixed Modeling with Whole Genome Data." Journal of Probability and Statistics 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/485174.

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Objective. We consider the need for a modeling framework for related individuals and various sources of variations. The relationships could either be among relatives in families or among unrelated individuals in a general population with cryptic relatedness; both could be refined or derived with whole genome data. As with variations they can include oliogogenes, polygenes, single nucleotide polymorphism (SNP), and covariates.Methods. We describe mixed models as a coherent theoretical framework to accommodate correlations for various types of outcomes in relation to many sources of variations.

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Hasanpour, Hesam, Ramak Ghavamizadeh Meibodi, Keivan Navi, and Sareh Asadi. "Dealing with mixed data types in the obsessive-compulsive disorder using ensemble classification." Neurology, Psychiatry and Brain Research 32 (June 2019): 77–84. http://dx.doi.org/10.1016/j.npbr.2019.04.004.

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Tarsitano, Agostino, and Marianna Falcone. "Missing-Values Adjustment for Mixed-Type Data." Journal of Probability and Statistics 2011 (2011): 1–20. http://dx.doi.org/10.1155/2011/290380.

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We propose a new method of single imputation, reconstruction, and estimation of nonreported, incorrect, implausible, or excluded values in more than one field of the record. In particular, we will be concerned with data sets involving a mixture of numeric, ordinal, binary, and categorical variables. Our technique is a variation of the popular nearest neighbor hot deck imputation (NNHDI) where “nearest” is defined in terms of a global distance obtained as a convex combination of the distance matrices computed for the various types of variables. We address the problem of proper weighting of the

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Coombes, Caitlin E., Zachary B. Abrams, Samantha Nakayiza, Guy Brock, and Kevin R. Coombes. "Umpire 2.0: Simulating realistic, mixed-type, clinical data for machine learning." F1000Research 9 (March 5, 2021): 1186. http://dx.doi.org/10.12688/f1000research.25877.2.

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The Umpire 2.0 R-package offers a streamlined, user-friendly workflow to simulate complex, heterogeneous, mixed-type data with known subgroup identities, dichotomous outcomes, and time-to-event data, while providing ample opportunities for fine-tuning and flexibility. Here, we describe how we have expanded the core Umpire 1.0 R-package, developed to simulate gene expression data, to generate clinically realistic, mixed-type data for use in evaluating unsupervised and supervised machine learning (ML) methods. As the availability of large-scale clinical data for ML has increased, clinical data h

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Coombes, Caitlin E., Zachary B. Abrams, Samantha Nakayiza, Guy Brock, and Kevin R. Coombes. "Umpire 2.0: Simulating realistic, mixed-type, clinical data for machine learning." F1000Research 9 (October 1, 2020): 1186. http://dx.doi.org/10.12688/f1000research.25877.1.

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The Umpire 2.0 R-package offers a streamlined, user-friendly workflow to simulate complex, heterogeneous, mixed-type data with known subgroup identities, dichotomous outcomes, and time-to-event data, while providing ample opportunities for fine-tuning and flexibility. Here, we describe how we have expanded the core Umpire 1.0 R-package, developed to simulate gene expression data, to generate clinically realistic, mixed-type data for use in evaluating unsupervised and supervised machine learning (ML) methods. As the availability of large-scale clinical data for ML has increased, clinical data h

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Wang, Yurong, and Yannan Luo. "Research of Wind Power Correlation With Three Different Data Types Based on Mixed Copula." IEEE Access 6 (2018): 77986–95. http://dx.doi.org/10.1109/access.2018.2884539.

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Storlie, C. B., S. M. Myers, S. K. Katusic, et al. "Clustering and variable selection in the presence of mixed variable types and missing data." Statistics in Medicine 37, no. 19 (2018): 2884–99. http://dx.doi.org/10.1002/sim.7697.

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Zhang, Weiping, MengMeng Zhang, and Yu Chen. "A Copula-Based GLMM Model for Multivariate Longitudinal Data with Mixed-Types of Responses." Sankhya B 82, no. 2 (2019): 353–79. http://dx.doi.org/10.1007/s13571-019-00197-8.

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Mami, Ahmed M., and Ayman Ali Elberjo. "ON USING NONPARAMETRIC REGRESSION METHODS TO ESTIMATE CATEGORICAL OUTCOMES MODELS WITH MIXED DATA TYPES." EPH - International Journal of Applied Science 1, no. 3 (2015): 15–22. http://dx.doi.org/10.53555/eijas.v1i3.15.

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Many data analysis methods are sensitive to the type of data under study. When we begin any statistical data analysis, it is very important to recognize the different types of data. Data can take a variety of values or belong to various categories, whichever numerical or nominal. However, there are two types of data, quantitative and qualitative (Categorical) data. The general and powerful methodological approaches for the analysis of quantitative data have been widely taught for several decades. While the analysis for qualitative data analysis have blossomed only in the past 25 years. The nee

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Grando, Adela, Julia Ivanova, Megan Hiestand, et al. "Mental health professional perspectives on health data sharing: Mixed methods study." Health Informatics Journal 26, no. 3 (2020): 2067–82. http://dx.doi.org/10.1177/1460458219893848.

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This study explores behavioral health professionals’ perceptions of granular data. Semi-structured in-person interviews of 20 health professionals were conducted at two different sites. Qualitative and quantitative analysis was performed. While most health professionals agreed that patients should control who accesses their personal medical record (70%), there are certain types of health information that should never be restricted (65%). Emergent themes, including perceived reasons that patients might share or withhold certain types of health information (65%), care coordination (12%), patient

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Liu, Zhenyu, Tao Wen, Wei Sun, and Qilong Zhang. "A Novel Multiway Splits Decision Tree for Multiple Types of Data." Mathematical Problems in Engineering 2020 (November 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/7870534.

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Classical decision trees such as C4.5 and CART partition the feature space using axis-parallel splits. Oblique decision trees use the oblique splits based on linear combinations of features to potentially simplify the boundary structure. Although oblique decision trees have higher generalization accuracy, most oblique split methods are not directly conducive to the categorical data and are computationally expensive. In this paper, we propose a multiway splits decision tree (MSDT) algorithm, which adopts feature weighting and clustering. This method can combine multiple numerical features, mult

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Choi, Wonei, Hanlim Lee, and Jeonghyeon Park. "A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation." Remote Sensing 13, no. 4 (2021): 609. http://dx.doi.org/10.3390/rs13040609.

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A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately

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Bhat, Chandra R. "A new generalized heterogeneous data model (GHDM) to jointly model mixed types of dependent variables." Transportation Research Part B: Methodological 79 (September 2015): 50–77. http://dx.doi.org/10.1016/j.trb.2015.05.017.

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Choi, Y., Y. S. Ghim, and B. N. Holben. "Identification of column-integrated dominant aerosols using the archive of AERONET data set." Atmospheric Chemistry and Physics Discussions 13, no. 10 (2013): 26627–56. http://dx.doi.org/10.5194/acpd-13-26627-2013.

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Abstract. Dominant aerosols were distinguished from level 2 inversion products for the Anmyon Aerosol Robotic Network (AERONET) site between 1999 and 2007. Secondary inorganic ions, black carbon (BC) and organic carbon (OC) were separated from fine mode aerosols, and mineral dust (MD), MD mixed with carbon, mixed coarse particles were separated from coarse mode aerosols. Four parameters (aerosol optical depth, single scattering albedo, absorption Angstrom exponent, and fine mode fraction) were used for this classification. Monthly variation of the occurrence rate of each aerosol type reveals t

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Trouvé, Raphael, Ruizhu Jiang, Melissa Fedrigo, et al. "Combining Environmental, Multispectral, and LiDAR Data Improves Forest Type Classification: A Case Study on Mapping Cool Temperate Rainforests and Mixed Forests." Remote Sensing 15, no. 1 (2022): 60. http://dx.doi.org/10.3390/rs15010060.

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Predictive vegetation mapping is an essential tool for managing and conserving high conservation-value forests. Cool temperate rainforests (Rainforest) and cool temperate mixed forests (Mixed Forest, i.e., rainforest spp. overtopped by large remnant Eucalyptus trees) are threatened forest types in the Central Highlands of Victoria. Logging of these forest types is prohibited; however, the surrounding native Eucalyptus forests can be logged in some areas of the landscape. This requires accurate mapping and delineation of these vegetation types. In this study, we combine niche modelling, multisp

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CORTÉS, Antoni, Marta CASCANTE, María Luz CÁRDENAS, and Athel CORNISH-BOWDEN. "Relationships between inhibition constants, inhibitor concentrations for 50% inhibition and types of inhibition: new ways of analysing data." Biochemical Journal 357, no. 1 (2001): 263–68. http://dx.doi.org/10.1042/bj3570263.

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The concentration of an inhibitor that decreases the rate of an enzyme-catalysed reaction by 50%, symbolized i0.5, is often used in pharmacological studies to characterize inhibitors. It can be estimated from the common inhibition plots used in biochemistry by means of the fact that the extrapolated inhibitor concentration at which the rate becomes infinite is equal to −i0.5. This method is, in principle, more accurate than comparing the rates at various different inhibitor concentrations, and inferring the value of i0.5 by interpolation. Its reciprocal, 1/i0.5, is linearly dependent on v0/V,

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Dawadi, Saraswati, Sagun Shrestha, and Ram A. Giri. "Mixed-Methods Research: A Discussion on its Types, Challenges, and Criticisms." Journal of Practical Studies in Education 2, no. 2 (2021): 25–36. http://dx.doi.org/10.46809/jpse.v2i2.20.

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The article positions mixed-method research (MMR) as a principled complementary research method to the traditional quantitative and qualitative research approaches. By situating MMR in an analysis of some of the common research paradigms, the article presents it as a natural choice in order to complement and cater to the increasingly complex needs of contemporary researchers. It proffers MMR as a flexible and adaptive conceptual framework for designing and conducting mixed methods research in a simplified manner. By explaining fundamental principles and major theoretical tenets of a mixed-meth

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Sharma, Dr Lok Raj, Sandesh Bidari, Dinesh Bidari, Sushil Neupane, and Rambabu Sapkota. "Exploring the Mixed Methods Research Design: Types, Purposes, Strengths, Challenges, and Criticisms." Global Academic Journal of Linguistics and Literature 5, no. 1 (2023): 3–12. http://dx.doi.org/10.36348/gajll.2023.v05i01.002.

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A mixed methods research design, which is a complex approach, combines both quantitative and qualitative data in a single study or succession of studies. This design can be particularly functional for exploring complex research questions that cannot be fully answered by using a single research design. Moreover, a mixed methods design is necessary to examine the relationships between different variables because examining the relationships between diverse variables is not viable just through a single research design. This design is required to complement and cater to the increasingly multifariou

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Będowska-Sójka, Barbara, and Agata Kliber. "Do mixed-data sampling models help forecast liquidity and volatility?" Przegląd Statystyczny 69, no. 2 (2022): 1–19. http://dx.doi.org/10.5604/01.3001.0016.0363.

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This paper aims to contribute to the existing studies on the Granger-causal relationship between volatility and liquidity in the stock market. We examine whether liquidity improves volatility forecasts and whether volatility allows the improvement of liquidity forecasts. The forecasts based on the mixed-data sampling models, MIDAS, are compared to those obtained from models based on daily data. Our results show that volatility and liquidity forecasts from MIDAS models outperform naive forecasts. On the other hand, the application of mixed-data sampling models does not significantly improve the

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Pelt, Daniël, Kees Batenburg, and James Sethian. "Improving Tomographic Reconstruction from Limited Data Using Mixed-Scale Dense Convolutional Neural Networks." Journal of Imaging 4, no. 11 (2018): 128. http://dx.doi.org/10.3390/jimaging4110128.

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In many applications of tomography, the acquired data are limited in one or more ways due to unavoidable experimental constraints. In such cases, popular direct reconstruction algorithms tend to produce inaccurate images, and more accurate iterative algorithms often have prohibitively high computational costs. Using machine learning to improve the image quality of direct algorithms is a recently proposed alternative, for which promising results have been shown. However, previous attempts have focused on using encoder–decoder networks, which have several disadvantages when applied to large tomo

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Oksenoyd, E. E., V. A. Volkov, E. V. Oleynik, and G. P. Myasnikova. "KEROGEN TYPES OF BAZHENOV FORMATION BASED ON PYROLYSIS DATA AND THEIR COMPARISON WITH OIL PARAMETERS." Oil and Gas Studies, no. 5 (November 1, 2017): 34–43. http://dx.doi.org/10.31660/0445-0108-2017-5-34-43.

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Based on pyrolytic data (3 995 samples from 208 wells) organic matter types of Bazhenov Formation are identified in the central part of Western Siberian basin. Zones of kerogen types I, II, III and mixed I-II and II-III are mapped. Content of sulfur, paraffins, resins and asphaltenes, viscosity, density, temperature and gas content in oils from Upper Jurassic and Lower Cretaceous sediments (3 806 oil pools) are mapped. Oil gradations are identified and distributed. The alternative model of zones of kerogen II and IIS types is presented. The established distributions of organic matter types can

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Geserbaatar, N. E., E. Nasanbat, and O. Lkhamjav. "THE IMPACT OF FOREST FIRE ON FOREST COVER TYPES IN MONGOLIA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 693–98. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-693-2020.

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Abstract. The objective of this study was the impact of forest fire on forest cover types. This study has identified non-forest and forest area that has seven forest class are included with cedar, pine, larch, birch, birch-pine mixed, birch-larch mixed and cedar-larch mixed, additionally, remote sensing imagery is applied. In contrast, Landsat imagery has been used several classification approaches. Moreover, the current classification has developments in segmentation and object-oriented techniques offer the suitable analysis to classify satellite data. In the object-oriented classification ap

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Stolz, Jörg, and Anaïd Lindemann. "The Titanic Game: Introducing Game Heuristics to Mixed Methods Theorizing and Data Analysis." Journal of Mixed Methods Research 14, no. 4 (2019): 522–44. http://dx.doi.org/10.1177/1558689819885723.

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Despite tremendous interest in social games and game studies, the potential of game heuristics for the field of mixed methods remains unknown. This article introduces game heuristics to mixed methods research, showing how it was used in a specific study on the survival probabilities on the Titanic. Specifically, we describe how game heuristics was used to create the explanandum, code and interpret the qualitative material, and set up and interpret the quantitative model. Furthermore, we show and explicate how game heuristics was used to construct seven types of meta-inferences. The Titanic dat

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Ramdhan, Muhammad, Yulius Yulius, and Nindya Kania Oktaviana. "Distribution of Tide Type in Indonesian Waters Based on 7 Days Data Measurement of Ipasoet-BIG Station." Jurnal Segara 17, no. 2 (2021): 117. http://dx.doi.org/10.15578/segara.v17i2.9342.

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Tidal data is needed in the field of energy, marine navigation, coastal construction and other activities related to the oceans. Tidal phenomena occur due to the interaction of the earth with space objects. The sea level rise in coastal waters can be modeled by a harmonic function containing tidal constant numbers. From the constants formed can be calculated a Formzahl number that shows the type of tides that occur at the observation station. This paper tries to describe the distribution pattern of tidal types that exist in Indonesian waters based on data observation collected at station belon

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Baizid, A. R., and M. S. Alam. "Wigner Rotations of Different Types of Lorentz Transformations." Journal of Scientific Research 8, no. 3 (2016): 249–58. http://dx.doi.org/10.3329/jsr.v8i3.27033.

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We have studied Wigner rotations of different types of Lorenz Transformations according to the nature of movement of one inertial frame relative to the other inertial frame. When the motion is along any arbitrary direction then we can find the formulae for Wigner rotations using the velocity addition formulae for most general, mixed number, quaternion and geometric product Lorentz transformations. Finally we have used simulated data for applying the Wigner rotation formula in pion decay chainand concluded the result.

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Bhat, Chandra R., Abdul R. Pinjari, Subodh K. Dubey, and Amin S. Hamdi. "On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables." Transportation Research Part B: Methodological 94 (December 2016): 240–63. http://dx.doi.org/10.1016/j.trb.2016.09.002.

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RANI and R. B. MISRA. "ML ESTIMATES FOR CROW/AMSAA RELIABILITY GROWTH MODEL FOR GROUPED AND MIXED TYPES OF SOFTWARE FAILURE DATA." International Journal of Reliability, Quality and Safety Engineering 11, no. 04 (2004): 329–37. http://dx.doi.org/10.1142/s0218539304001555.

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A number of software reliability growth models have been proposed into the literature for estimating reliability during software testing. Duane's model,7 originally proposed for hardware reliability is also used in estimating reliability of the software during development testing. Graphical interpretation of Duane's postulate subsequently was given a concrete stochastic basis by Crow,3 and provided a comprehensive treatment of this model in the context of reliability growth and demonstrated its elegant inferential aspects. Parameters of the Crow model have physical interpretation and can yield

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Munn, Ian A., Yushun Zhai, and David L. Evans. "Modeling Forest Fire Probabilities in the South Central United States Using FIA Data." Southern Journal of Applied Forestry 27, no. 1 (2003): 11–17. http://dx.doi.org/10.1093/sjaf/27.1.11.

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Abstract Factors influencing the probability of fire occurrence in the south central United States were investigated using a geographic information system (GIS) and a multinomial logit model. Forest Inventory and Analysis (FIA) data at the plot level were merged with census data at the census-tract level to create a data set containing demographic, geographic, and timber-related characteristics. A multinomial logit model was employed to estimate the relationships between plot characteristics and the probability of wildfires, prescribed fires, and fires of unknown origins. Wildfires occurred mo

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Zhang, Xinyan, Boyi Guo, and Nengjun Yi. "Zero-Inflated gaussian mixed models for analyzing longitudinal microbiome data." PLOS ONE 15, no. 11 (2020): e0242073. http://dx.doi.org/10.1371/journal.pone.0242073.

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Motivation The human microbiome is variable and dynamic in nature. Longitudinal studies could explain the mechanisms in maintaining the microbiome in health or causing dysbiosis in disease. However, it remains challenging to properly analyze the longitudinal microbiome data from either 16S rRNA or metagenome shotgun sequencing studies, output as proportions or counts. Most microbiome data are sparse, requiring statistical models to handle zero-inflation. Moreover, longitudinal design induces correlation among the samples and thus further complicates the analysis and interpretation of the micro

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Cowpertwait, P. S. P. "Mixed rectangular pulses models of rainfall." Hydrology and Earth System Sciences 8, no. 5 (2004): 993–1000. http://dx.doi.org/10.5194/hess-8-993-2004.

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Abstract. A stochastic rainfall model, obtained as the superposition of independent Neyman-Scott Rectangular Pulses (NSRP), is proposed to provide a flexible parameterisation and general procedure for modelling rainfall. The methodology is illustrated using hourly data from Auckland, New Zealand, where the model is fitted to data collected for each calendar month over the period: 1966–1998. For data taken over the months April to August, two independent superposed NSRP processes are fitted, which may correspond to the existence of mixtures of convective and stratiform storm types for these mon

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Leech, Nancy L., Kathleen M. T. Collins, Qun G. Jiao, and Anthony J. Onwuegbuzie. "Mixed Research in Gifted Education." Journal for the Education of the Gifted 34, no. 6 (2011): 860–75. http://dx.doi.org/10.1177/0162353211425095.

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The purpose of this study was to identify the prevalence of mixed research techniques in empirical studies published in gifted education journals. During Phase 1, empirical full-text databases and relevant electronic bibliographic databases related to gifted education were searched during a time span of 10 to 18 years, resulting in the identification of 32 mixed research studies. During Phase 2, frequency data were compiled detailing the types of methods (quantitative, qualitative, mixed) implemented in empirical studies published in three leading gifted education research journals covering th

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Zhang, Xinyan, and Nengjun Yi. "Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data." Bioinformatics 36, no. 8 (2020): 2345–51. http://dx.doi.org/10.1093/bioinformatics/btz973.

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Abstract Motivation Longitudinal metagenomics data, including both 16S rRNA and whole-metagenome shotgun sequencing data, enhanced our abilities to understand the dynamic associations between the human microbiome and various diseases. However, analytic tools have not been fully developed to simultaneously address the main challenges of longitudinal metagenomics data, i.e. high-dimensionality, dependence among samples and zero-inflation of observed counts. Results We propose a fast zero-inflated negative binomial mixed modeling (FZINBMM) approach to analyze high-dimensional longitudinal metagen

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Li, Ziyi, Zhijin Wu, Peng Jin, and Hao Wu. "Dissecting differential signals in high-throughput data from complex tissues." Bioinformatics 35, no. 20 (2019): 3898–905. http://dx.doi.org/10.1093/bioinformatics/btz196.

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Abstract Motivation Samples from clinical practices are often mixtures of different cell types. The high-throughput data obtained from these samples are thus mixed signals. The cell mixture brings complications to data analysis, and will lead to biased results if not properly accounted for. Results We develop a method to model the high-throughput data from mixed, heterogeneous samples, and to detect differential signals. Our method allows flexible statistical inference for detecting a variety of cell-type specific changes. Extensive simulation studies and analyses of two real datasets demonstr

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Zheng, Y. C., L. L. Li, and Y. P. Wang. "AN AEROSOL TYPE CLASSIFICATION METHOD BASED ON REMOTE SENSING DATA IN GUANGDONG, CHINA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W9 (October 25, 2019): 239–43. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w9-239-2019.

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Abstract. This paper provides an aerosol classification method based on remote sensing data in Guangdong, China in year 2010 and 2011. Aerosol Optical Depth, Angstrom Exponent and Ultraviolet Aerosol Index, as important properties of aerosols, are introduced into classification. Data of these three aerosol properties are integrated to establish a 3-dimension dataset, and k-means clustering algorithm with Mahalanobis distance is used to find out four clusters of the dataset, which respectively represents four aerosol types of urban-industrial, dust, biomass burning and mixed type. Prior knowled

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Yang, Hanbing, Meichen Fu, Li Wang, and Feng Tang. "Mixed Land Use Evaluation and Its Impact on Housing Prices in Beijing Based on Multi-Source Big Data." Land 10, no. 10 (2021): 1103. http://dx.doi.org/10.3390/land10101103.

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The tense relationship between the supply and demand of land resources and the past spatial expansion of urban development in Beijing have brought many urban problems. Mixed land use is considered to be able to solve these urban problems as well as promote sustainable urban development. In this context, this study uses multi-source big data such as POI, OpenStreetMap and web crawler data to construct current land-use data of the area within the sixth ring road of Beijing, and then uses the entropy index and type number index to analyze the spatial distribution and aggregation characteristics o

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Dharmadhikari, Susheel, Riddhiman Raut, Asok Ray, and Amrita Basak. "A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations." Applied Sciences 13, no. 3 (2023): 1542. http://dx.doi.org/10.3390/app13031542.

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The article presents a mixed deep neural network (DNN) approach for detecting micron-scale fatigue damage in high-strength polycrystalline aluminum alloys. Fatigue testing is conducted using a custom-designed apparatus integrated with a confocal microscope and a moving stage to accurately pinpoint the instance of micron-scale crack emergence. The specimens are monitored throughout the duration of the experiment using a pair of high-frequency ultrasonic transducers. The mixed DNN is trained with ultrasonic time-series data that are obtained from two sets of specimens categorized by different st

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Sun, Meng, and Yi Lu. "A Generalized Linear Mixed Model for Data Breaches and Its Application in Cyber Insurance." Risks 10, no. 12 (2022): 224. http://dx.doi.org/10.3390/risks10120224.

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Data breach incidents result in severe financial loss and reputational damage, which raises the importance of using insurance to manage and mitigate cyber related risks. We analyze data breach chronology collected by Privacy Rights Clearinghouse (PRC) since 2001 and propose a Bayesian generalized linear mixed model for data breach incidents. Our model captures the dependency between frequency and severity of cyber losses and the behavior of cyber attacks on entities across time. Risk characteristics such as types of breach, types of organization, entity locations in chronology, as well as time

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Rokhman, Nur. "A Survey on Mixed-Attribute Outlier Detection Methods." CommIT (Communication and Information Technology) Journal 13, no. 1 (2019): 39. http://dx.doi.org/10.21512/commit.v13i1.5558.

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In the data era, outlier detection methods play an important role. The existence of outliers can provide clues to the discovery of new things, irregularities in a system, or illegal intruders. Based on the data, outlier detection methods can be classified into numerical, categorical, or mixed-attribute data. However, the study of the outlier detection methods is generally conducted for numerical data. Meanwhile, many real-life facts are presented in mixed-attribute data. In this paper, the researcher presents a survey of outlier detection methods for mixed-attribute data. The methods are class

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Lazar, Alina, Ling Jin, C. Anna Spurlock, Kesheng Wu, Alex Sim, and Annika Todd. "Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization." Journal of Data and Information Quality 11, no. 2 (2019): 1–22. http://dx.doi.org/10.1145/3301294.

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