Statistical Inference Under Mixture Models
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Beschreibung
This book puts its weight on theoretical issues related to finite mixture models. It shows that a good applicant, is an applicant who understands the issues behind each statistical method. This book is intended for applicants whose interests include some understanding of the procedures they are using, while they do not have to read the technical derivations.
At the same time, many researchers find most theories and techniques necessary for the development of various statistical methods, without chasing after one set of research papers, after another. Even though the book emphasizes the theory, it provides accessible numerical tools for data analysis. Readers with strength in developing statistical software, may find it useful.
This book puts its weight on theoretical issues related to finite mixture models. It shows that a good applicant, is an applicant who understands the issues behind each statistical method. This book is intended for applicants whose interests include some understanding of the procedures they are using, while they do not have to read the technical derivations.
At the same time, many researchers find most theories and techniques necessary for the development of various statistical methods, without chasing after one set of research papers, after another. Even though the book emphasizes the theory, it provides accessible numerical tools for data analysis. Readers with strength in developing statistical software, may find it useful.
Includes comprehensive introduction of finite mixture models Examines recent developments in the hypothesis test for the order of mixture model Provides insight on the foundations of inference procedures under finite mixture model
Autor*in
Jiahua Chen
Themen in »Statistical Inference Under Mixture Models«
EM-algorithm EM-test Hypothesis test Homogeneity Likelihood ratio test Maximum likelihood estimation Mixture model Modified likelihood Nonparametric MLE Optimal rate of convergence Penalized MLE