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Semiparametric maximum likelihood estimation for a two-sample density ratio model with right-censored data
Wei, Wenhua1,2; Zhou, Yong1,3
2016-03-01
Source PublicationCANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
ISSN0319-5724
Volume44Issue:1Pages:58-81
AbstractIn this paper we investigate a broader semiparametric two-sample density ratio model based on two groups of right-censored data. A semiparametric maximum likelihood estimator for the unknown finite and infinite dimensional parameters of the model is proposed and obtained by an EM algorithm. By using empirical process theory, we establish the uniform consistency and asymptotic normality of the proposed estimator. We moreover employ a Kolmogorov-Smirnov type test statistic to evaluate the model validity and a likelihood ratio test statistic to examine the treatment effects between the two groups. Simulation studies are conducted to assess the finite sample performance of the proposed estimator and to compare it with its alternatives. Finally a real data example is analyzed to illustrate its application. The Canadian Journal of Statistics 44: 58-81; 2016 (c) 2015 Statistical Society of Canada Resume Les auteurs explorent un modele semi-parametrique plus general pour le ratio des densites de deux echantillons base sur deux groupes de donnees censurees a droite. Ils proposent un estimateur semi-parametrique au maximum de vraisemblance pour les parametres de dimensions finies et infinies du modele et utilisent l'algorithme EM pour le calculer. l'aide de la theorie des processus empiriques, les auteurs etablissent la convergence uniforme et la normalite asymptotique de l'estimateur propose. De plus, ils emploient une statistique de type Kolmogorov-Smirnov pour evaluer la validite du modele et un test au rapport de vraisemblance pour examiner l'effet du traitement entre les deux groupes. Les auteurs procedent a des simulations afin d'evaluer la performance de l'estimateur propose sur des echantillons de taille finie, et de le comparer aux autres approches connues. Finalement, ils illustrent la mise en OEuvre de leur methode a l'aide de donnees reelles. La revue canadienne de statistique 44: 58-81; 2016 (c) 2015 Societe statistique du Canada
KeywordDensity ratio model EM algorithm Empirical process right-censored data semiparametric maximum likelihood estimation
DOI10.1002/cjs.11272
Language英语
Funding ProjectNational Natural Science Foundation of China ; State Key Program of National Natural Science Foundation of China ; Key Laboratory of RCSDS, AMSS, CAS ; Shanghai First-class Discipline A and IRTSHUFE, PCSIRT
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000371485700004
PublisherWILEY-BLACKWELL
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/22261
Collection应用数学研究所
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
2.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China
3.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China
Recommended Citation
GB/T 7714
Wei, Wenhua,Zhou, Yong. Semiparametric maximum likelihood estimation for a two-sample density ratio model with right-censored data[J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE,2016,44(1):58-81.
APA Wei, Wenhua,&Zhou, Yong.(2016).Semiparametric maximum likelihood estimation for a two-sample density ratio model with right-censored data.CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE,44(1),58-81.
MLA Wei, Wenhua,et al."Semiparametric maximum likelihood estimation for a two-sample density ratio model with right-censored data".CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 44.1(2016):58-81.
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