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Statistical Inference Under Mixture Models
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Catalogue Information
Field name
Details
Dewey Class
519.5
Title
Statistical Inference Under Mixture Models (EBook /) / by Jiahua Chen.
Author
Chen, Jiahua
Other name(s)
SpringerLink (Online service)
Edition statement
1st ed. 2023.
Publication
Singapore : : Springer Nature Singapore : : Imprint: Springer, , 2023.
Physical Details
XIV, 327 p. 9 illus. : online resource.
Series
ICSA Book Series in Statistics
2199-0999
ISBN
9789819961412
Summary Note
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.:
Contents note
1. Introduction to mixture models -- 2. Nonparametric MLE and its consistency -- 3. Maximum likelihood estimation under finite mixture models -- 4. Estimation under finite normal mixture models -- 5. Consistent estimation under finite Gamma mixture -- 6. Geometric properties of nonparametric MLE and numerical solutions -- 7. EM-algorithm -- 8. Rate of convergence -- 9. Test of homogeneity -- 10. Likelihood ratio test for homogeneity -- 11. Modified likelihood ratio test -- 12. Modified likelihood ratio test for higher order -- 13 EM-test for homogeneity -- 14 EM-test for higher order -- 15 EM-test for univariate finite Gaussian mixture models -- 16 Order selection of the finite mixture models -- 17 A few key probability theory results employed -- References.
Mode of acces to digital resource
Digital reproduction.-
Cham :
Springer International Publishing,
2023. -
Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format.
System details note
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site
https://doi.org/10.1007/978-981-99-6141-2
Links to Related Works
Subject References:
Statistical Theory and Methods
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Statistics
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Authors:
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Chen, Jiahua
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Corporate Authors:
SpringerLink (Online service)
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Series:
ICSA Book Series in Statistics
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Classification:
519.5
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