Introduction to Statistical Methods for Complex Systems

[1]  Gilles Celeux,et al.  Variable selection in model-based clustering: A general variable role modeling , 2009, Comput. Stat. Data Anal..

[2]  Emmanuel Barillot,et al.  Classification of arrayCGH data using fused SVM , 2008, ISMB.

[3]  Geoffrey J McLachlan,et al.  Selection bias in gene extraction on the basis of microarray gene-expression data , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[4]  David B. Allison,et al.  A mixture model approach for the analysis of microarray gene expression data , 2002 .

[5]  C. Sutton Classification and Regression Trees, Bagging, and Boosting , 2005 .

[6]  Zoubin Ghahramani,et al.  A Bayesian approach to reconstructing genetic regulatory networks with hidden factors , 2005, Bioinform..

[7]  Charles M. Bishop,et al.  Variational Message Passing , 2005, J. Mach. Learn. Res..

[8]  G. Blanchard,et al.  Two simple sufficient conditions for FDR control , 2008, 0802.1406.

[9]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[10]  Gérard Govaert,et al.  Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  R. Tibshirani,et al.  Spatial smoothing and hot spot detection for CGH data using the fused lasso. , 2008, Biostatistics.

[12]  A. Raftery,et al.  Variable Selection for Model-Based Clustering , 2006 .

[13]  Pierre R. Bushel,et al.  Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models , 2001, J. Comput. Biol..

[14]  S. Dudoit,et al.  Multiple Hypothesis Testing in Microarray Experiments , 2003 .

[15]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[16]  Franck Picard,et al.  A mixture model for random graphs , 2008, Stat. Comput..

[17]  Stefan Michiels,et al.  Prediction of cancer outcome with microarrays: a multiple random validation strategy , 2005, The Lancet.

[18]  Wenguang Sun,et al.  Large‐scale multiple testing under dependence , 2009 .

[19]  B. Lindqvist,et al.  Estimating the proportion of true null hypotheses, with application to DNA microarray data , 2005 .

[20]  Hongzhe Li,et al.  Clustering of time-course gene expression data using a mixed-effects model with B-splines , 2003, Bioinform..

[21]  Robert J. Tempelman,et al.  Statistical Analysis of Efficient Unbalanced Factorial Designs for Two-Color Microarray Experiments , 2008, International journal of plant genomics.

[22]  L. Wasserman,et al.  Operating characteristics and extensions of the false discovery rate procedure , 2002 .

[23]  G. Churchill,et al.  Experimental design for gene expression microarrays. , 2001, Biostatistics.

[24]  William J. Byrne,et al.  Convergence Theorems for Generalized Alternating Minimization Procedures , 2005, J. Mach. Learn. Res..

[25]  P. Cornelius,et al.  Approximate F-tests of multiple degree of freedom hypotheses in generalized least squares analyses of unbalanced split-plot experiments , 1996 .

[26]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[27]  André I. Khuri,et al.  Statistical Texts for Mixed Linear Models: Khuri/Statistical , 1998 .

[28]  Peter Bühlmann,et al.  Supervised clustering of genes , 2002, Genome Biology.

[29]  Geoffrey J. McLachlan,et al.  A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays , 2006, Bioinform..

[30]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[31]  M. Kenward,et al.  Small sample inference for fixed effects from restricted maximum likelihood. , 1997, Biometrics.

[32]  Ash A. Alizadeh,et al.  'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns , 2000, Genome Biology.

[33]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[34]  John D. Storey,et al.  Empirical Bayes Analysis of a Microarray Experiment , 2001 .

[35]  John D. Storey A direct approach to false discovery rates , 2002 .

[36]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[37]  É. Moulines,et al.  Convergence of a stochastic approximation version of the EM algorithm , 1999 .

[38]  Stéphane Robin,et al.  A cross-validation based estimation of the proportion of true null hypotheses , 2010 .

[39]  T. Snijders,et al.  Estimation and Prediction for Stochastic Blockstructures , 2001 .

[40]  L. Wasserman,et al.  A stochastic process approach to false discovery control , 2004, math/0406519.

[41]  Chloé Friguet,et al.  A Factor Model Approach to Multiple Testing Under Dependence , 2009 .

[42]  William Stafford Noble,et al.  Large-scale prediction of protein-protein interactions from structures , 2010, BMC Bioinformatics.

[43]  Thomas G. Dietterich An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[44]  J. L. Hodges,et al.  Discriminatory Analysis - Nonparametric Discrimination: Consistency Properties , 1989 .

[45]  T. Hastie,et al.  Classification of gene microarrays by penalized logistic regression. , 2004, Biostatistics.

[46]  Jean-Jacques Daudin,et al.  A semi-parametric approach for mixture models: Application to local false discovery rate estimation , 2007, Comput. Stat. Data Anal..

[47]  Kyung In Kim,et al.  Effects of dependence in high-dimensional multiple testing problems , 2008, BMC Bioinformatics.

[48]  Patrik D'haeseleer,et al.  How does gene expression clustering work? , 2005, Nature Biotechnology.

[49]  Tristan Mary-Huard,et al.  Tailored Aggregation for Classification , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[50]  Korbinian Strimmer,et al.  fdrtool: a versatile R package for estimating local and tail area-based false discovery rates , 2008, Bioinform..