Finite mixture biclustering of discrete type multivariate data
暂无分享,去创建一个
Daniel Fernández | Ivy Liu | Richard Arnold | Shirley Pledger | Roy Costilla | S. Pledger | Ivy Liu | D. Fernández | R. Arnold | Roy Costilla | R. Costilla
[1] Clifford M. Hurvich,et al. Regression and time series model selection in small samples , 1989 .
[2] Daniel Ståhl,et al. Model‐based cluster analysis , 2012 .
[3] Adrian E. Raftery,et al. How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..
[4] G. J. McLachlan,et al. 9 The classification and mixture maximum likelihood approaches to cluster analysis , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.
[5] C. Robert,et al. Estimating Mixtures of Regressions , 2003 .
[6] P. McCullagh,et al. How many clusters , 2008 .
[7] B. Everitt,et al. Cluster Analysis: Everitt/Cluster Analysis , 2011 .
[8] Margarida G. M. S. Cardoso,et al. Identifying the number of clusters in discrete mixture models , 2014, 1409.7419.
[9] G. Quinn,et al. Experimental Design and Data Analysis for Biologists , 2002 .
[10] J. A. Hartigan,et al. Mosaics for Contingency Tables , 1981 .
[11] K. Liang,et al. Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests under Nonstandard Conditions , 1987 .
[12] Sara Taskinen,et al. Model‐based approaches to unconstrained ordination , 2015 .
[13] Maurizio Vichi,et al. Two-mode multi-partitioning , 2008, Comput. Stat. Data Anal..
[14] Volodymyr Melnykov,et al. Finite mixture models and model-based clustering , 2010 .
[15] S. Pledger. Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures , 2000, Biometrics.
[16] S. Frühwirth-Schnatter. Markov chain Monte Carlo Estimation of Classical and Dynamic Switching and Mixture Models , 2001 .
[17] Gérard Govaert,et al. An EM algorithm for the block mixture model , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[18] B. McCune,et al. Analysis of Ecological Communities , 2002 .
[19] M. Stephens. Dealing with label switching in mixture models , 2000 .
[20] Volodymyr Melnykov,et al. Finite mixture modelling in mass spectrometry analysis , 2013 .
[21] Yu Hayakawa,et al. Capture–Recapture Estimation Using Finite Mixtures of Arbitrary Dimension , 2010, Biometrics.
[22] Tom Lodewyckx,et al. Bayesian Versus Frequentist Inference , 2008 .
[23] Julien Jacques,et al. Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study , 2015, IDA.
[24] Geoffrey J. McLachlan,et al. Mixture models : inference and applications to clustering , 1989 .
[25] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .
[26] Gérard Govaert,et al. A Comparison Between Block CEM and Two-Way CEM Algorithms to Cluster a Contingency Table , 2005, PKDD.
[27] A. Agresti. Analysis of Ordinal Categorical Data , 1985 .
[28] D. Fernández,et al. Mixture-based clustering for the ordered stereotype model , 2016, Comput. Stat. Data Anal..
[29] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[30] Catherine A. Sugar,et al. Finding the Number of Clusters in a Dataset , 2003 .
[31] A. Agresti,et al. Categorical Data Analysis , 1991, International Encyclopedia of Statistical Science.
[32] Ajay Jasra,et al. Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling , 2005 .
[33] Ivy Liu,et al. Biclustering Models for Two-Mode Ordinal Data , 2016, Psychometrika.
[34] Christian P. Robert,et al. Reversible Jump MCMC Converging to Birth-and-Death MCMC and More General Continuous Time Samplers , 2001 .
[35] Jeroen K. Vermunt,et al. The Use of Restricted Latent Class Models for Defining and Testing Nonparametric and Parametric Item Response Theory Models , 2001 .
[36] Richard Breen,et al. Mixture Models for Ordinal Data , 2010 .
[37] Daniel Fernández,et al. Categorising Count Data into Ordinal Responses with Application to Ecological Communities , 2016 .
[38] Rebecca A Betensky,et al. A penalized latent class model for ordinal data. , 2007, Biostatistics.
[39] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[40] Gérard Govaert,et al. Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[41] J. A. Hartigan,et al. A k-means clustering algorithm , 1979 .
[42] B. Manly. Multivariate Statistical Methods : A Primer , 1986 .
[43] Francesco Bartolucci,et al. Longitudinal analysis of self‐reported health status by mixture latent auto‐regressive models , 2014 .
[44] A. Agresti. Analysis of Ordinal Categorical Data: Agresti/Analysis , 2010 .
[45] David R. Anderson,et al. Model selection and multimodel inference : a practical information-theoretic approach , 2003 .
[46] Damien McParland,et al. Clustering Ordinal Data via Latent Variable Models , 2013, Algorithms from and for Nature and Life.
[47] Philip S. Yu,et al. Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.
[48] Jean-Michel Marin,et al. Bayesian Core: A Practical Approach to Computational Bayesian Statistics , 2010 .
[49] Marco Alfò,et al. Advances in Mixture Models , 2007, Comput. Stat. Data Anal..
[50] P. Green,et al. Corrigendum: On Bayesian analysis of mixtures with an unknown number of components , 1997 .
[51] Gilles Celeux,et al. Bayesian Inference for Mixture: The Label Switching Problem , 1998, COMPSTAT.
[52] Ryan P. Browne,et al. Model-based clustering, classification, and discriminant analysis of data with mixed type , 2012 .
[53] H. P. Friedman,et al. On Some Invariant Criteria for Grouping Data , 1967 .
[54] G. McLachlan,et al. The EM algorithm and extensions , 1996 .
[55] Gérard Govaert,et al. Clustering with block mixture models , 2003, Pattern Recognit..
[56] S. Frühwirth-Schnatter,et al. Model-based clustering of categorical time series , 2010 .
[57] A. Raftery,et al. Variable Selection for Model-Based Clustering , 2006 .
[58] Richard Arnold,et al. Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection , 2014, Comput. Stat. Data Anal..
[59] O. Cappé,et al. Reversible jump, birth‐and‐death and more general continuous time Markov chain Monte Carlo samplers , 2003 .
[60] Damien McParland,et al. Model based clustering for mixed data: clustMD , 2015, Advances in Data Analysis and Classification.
[61] F. Kianifard. Applied Multivariate Data Analysis: Volume II: Categorical and Multivariate Methods , 1994 .
[62] Irini Moustaki,et al. A Latent Variable Model for Ordinal Variables , 2000 .
[63] M. Stephens. Bayesian analysis of mixture models with an unknown number of components- an alternative to reversible jump methods , 2000 .
[64] Sylvia Frühwirth-Schnatter,et al. Finite Mixture and Markov Switching Models , 2006 .
[65] S. C. Johnson. Hierarchical clustering schemes , 1967, Psychometrika.
[66] Adrian E. Raftery,et al. Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .
[67] Halina Frydman. Estimation in the Mixture of Markov Chains Moving With Different Speeds , 2003 .
[68] L. A. Goodman. Exploratory latent structure analysis using both identifiable and unidentifiable models , 1974 .
[69] Alan D. Marrs. An Application of Reversible-Jump MCMC to Multivariate Spherical Gaussian Mixtures , 1997, NIPS.
[70] S. Frühwirth-Schnatter,et al. Labor Market Entry and Earnings Dynamics: Bayesian Inference Using Mixtures-of-Experts Markov Chain Clustering , 2012 .
[71] Richard Paap,et al. A Bayesian approach to two-mode clustering , 2009 .
[72] A. McCutcheon,et al. Latent Class Analysis , 2021, Encyclopedia of Autism Spectrum Disorders.
[73] A Agresti,et al. Quasi-symmetric latent class models, with application to rater agreement. , 1993, Biometrics.
[74] Robert Tibshirani,et al. Cluster Validation by Prediction Strength , 2005 .
[75] J. Hagenaars,et al. Methods in Human Growth Research: Ordinal longitudinal data analysis , 2004 .
[76] Daniel Fernández,et al. A goodness-of-fit test for the ordered stereotype model. , 2016, Statistics in medicine.
[77] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[78] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[79] P. Diggle,et al. Analysis of Longitudinal Data , 2003 .
[80] J. Anderson. Regression and Ordered Categorical Variables , 1984 .
[81] Petros Dellaportas,et al. Multivariate mixtures of normals with unknown number of components , 2006, Stat. Comput..
[82] Xin-Yuan Song,et al. A mixture of generalized latent variable models for mixed mode and heterogeneous data , 2011, Comput. Stat. Data Anal..
[83] B. Manly. Randomization, Bootstrap and Monte Carlo Methods in Biology , 2018 .
[84] G. McLachlan. On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture , 1987 .
[85] Vichi Maurizio. Double k-means Clustering for Simultaneous Classification of Objects and Variables , 2001 .
[86] Jeroen K. Vermunt,et al. A nonparametric random-coefficients approach : The latest class regression model , 2001 .
[87] Brian Everitt,et al. Cluster analysis , 1974 .
[88] Zhihua Zhang,et al. Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm , 2004, Stat. Comput..
[89] N. Gotelli,et al. NULL MODELS IN ECOLOGY , 1996 .
[90] P. McCullagh. Regression Models for Ordinal Data , 1980 .
[91] P. Green,et al. On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .
[92] R. Brant. Assessing proportionality in the proportional odds model for ordinal logistic regression. , 1990, Biometrics.
[93] Nial Friel,et al. Block clustering with collapsed latent block models , 2010, Stat. Comput..
[94] D. Fernández,et al. Model selection for mixture‐based clustering for ordinal data , 2016 .
[95] Jean-Michel Marin,et al. Bayesian Inference on Mixtures of Distributions , 2008, 0804.2413.
[96] S. Haberman. Analysis of qualitative data , 1978 .
[97] G. Govaert,et al. Latent Block Model for Contingency Table , 2010 .
[98] Adrian E. Raftery,et al. Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering , 2007, J. Classif..