The likelihood ratio test for general mixture models with or without structural parameter
ESAIM: PS, 13 (2009) 301-327
Published online: 2009-07-21
Articles citing this article
The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
Cited article:
This article has been cited by the following article(s):
The AdaptSgenoLasso, an extended version of the SgenoLasso, for gene mapping and for genomic prediction using the extremes
Charles-Elie Rabier and Céline DelmasStatistics 59 (3) 534 (2025)
https://doi.org/10.1080/02331888.2025.2456771
Asymptotics for Markov chain mixture detection
Matthew Fitzpatrick and Michael StewartEconometrics and Statistics 22 56 (2022)
https://doi.org/10.1016/j.ecosta.2021.11.004
The SgenoLasso and its cousins for selective genotyping and extreme sampling: application to association studies and genomic selection
Charles-Elie Rabier and Céline DelmasStatistics 55 (1) 18 (2021)
https://doi.org/10.1080/02331888.2021.1881785
Rate of divergence of the nonparametric likelihood ratio test for Gaussian mixtures
Wenhua Jiang and Cun-Hui ZhangBernoulli 25 (4B) (2019)
https://doi.org/10.3150/18-BEJ1094
Hypothesis testing for finite mixture models
Supawadee Wichitchan, Weixin Yao and Guangren YangComputational Statistics & Data Analysis 132 180 (2019)
https://doi.org/10.1016/j.csda.2018.05.005
Chi-square processes for gene mapping in a population with family structure
Charles-Elie Rabier, Jean-Marc Azaïs, Jean-Michel Elsen and Céline DelmasStatistical Papers 60 (1) 239 (2019)
https://doi.org/10.1007/s00362-016-0835-y
TESTING FOR HOMOGENEITY IN MIXTURE MODELS
Jiaying Gu, Roger Koenker and Stanislav VolgushevEconometric Theory 34 (4) 850 (2018)
https://doi.org/10.1017/S0266466617000299
Multidimensional two-component Gaussian mixtures detection
Béatrice Laurent, Clément Marteau and Cathy Maugis-RabusseauAnnales de l'Institut Henri Poincaré, Probabilités et Statistiques 54 (2) (2018)
https://doi.org/10.1214/17-AIHP823
Universal Coding and Order Identification by Model Selection Methods
Élisabeth GassiatSpringer Monographs in Mathematics, Universal Coding and Order Identification by Model Selection Methods 103 (2018)
https://doi.org/10.1007/978-3-319-96262-7_4
A Bayesian Information Criterion for Singular Models
Mathias Drton and Martyn PlummerJournal of the Royal Statistical Society Series B: Statistical Methodology 79 (2) 323 (2017)
https://doi.org/10.1111/rssb.12187
Non-asymptotic detection of two-component mixtures with unknown means
Béatrice Laurent, Clément Marteau and Cathy Maugis-RabusseauBernoulli 22 (1) (2016)
https://doi.org/10.3150/14-BEJ657
MCMC Confidence Sets for Identified Sets
Xiaohong Chen, Timothy Christensen and Elie T. TamerSSRN Electronic Journal (2016)
https://doi.org/10.2139/ssrn.2775253
Testing the Number of Components in Normal Mixture Regression Models
Hiroyuki Kasahara and Katsumi ShimotsuJournal of the American Statistical Association 110 (512) 1632 (2015)
https://doi.org/10.1080/01621459.2014.986272
Likelihood ratio test process for quantitative trait locus detection
Jean-Marc Azaïs, Céline Delmas and Charles-Elie RabierStatistics 48 (4) 787 (2014)
https://doi.org/10.1080/02331888.2012.760093
An asymptotic test for Quantitative Trait Locus detection in presence of missing genotypes
Charles-Elie RabierAnnales de la Faculté des sciences de Toulouse : Mathématiques 23 (4) 755 (2014)
https://doi.org/10.5802/afst.1423
On quantitative trait locus mapping with an interference phenomenon
Charles-Elie RabierTEST 23 (2) 311 (2014)
https://doi.org/10.1007/s11749-013-0349-z
On empirical processes for quantitative trait locus mapping under the presence of a selective genotyping and an interference phenomenon
C.E. RabierJournal of Statistical Planning and Inference 153 42 (2014)
https://doi.org/10.1016/j.jspi.2014.05.011
Integrating Sequence Variation and Protein Structure to Identify Sites under Selection
Austin G. Meyer and Claus O. WilkeMolecular Biology and Evolution 30 (1) 36 (2013)
https://doi.org/10.1093/molbev/mss217
Epistatic Clustering: A Model-Based Approach for Identifying Links Between Clusters
Jian ZhangJournal of the American Statistical Association 108 (504) 1366 (2013)
https://doi.org/10.1080/01621459.2013.835661
Identifying Loci Under Selection Against Gene Flow in Isolation-with-Migration Models
Vitor C Sousa, Miguel Carneiro, Nuno Ferrand and Jody HeyGenetics 194 (1) 211 (2013)
https://doi.org/10.1534/genetics.113.149211
Advances in Data Analysis, Data Handling and Business Intelligence
Denys PommeretStudies in Classification, Data Analysis, and Knowledge Organization, Advances in Data Analysis, Data Handling and Business Intelligence 251 (2009)
https://doi.org/10.1007/978-3-642-01044-6_23
Variable selection in model-based clustering using multilocus genotype data
Wilson Toussile and Elisabeth GassiatAdvances in Data Analysis and Classification 3 (2) 109 (2009)
https://doi.org/10.1007/s11634-009-0043-x