Longitudinal categorical data analysis / Brajendra C. Sutradhar.
Material type: TextSeries: Springer series in statisticsPublication details: New York : Springer, 2014.Description: xviii, 369 p. ; 25 cmISBN:- 9781493921362 (hbk. : acidfree paper)
- 000SA.051 23 Su966
Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
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Books | ISI Library, Kolkata | 000SA.051 Su966 (Browse shelf(Opens below)) | Available | 136463 |
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000SA.05 Su957 Statistical analysis of panel count data / | 000SA.05 V628 Analysis and modeling of complex data in behavioral and social sciences / | 000SA.051 N975 Categorical and nonparametric data analysis : | 000SA.051 Su966 Longitudinal categorical data analysis / | 000SA.051 Y22 Categorical data analysis / | 000SA.051 Y22 Categorical data analysis / | 000SA.051 Y22 Categorical data analysis / |
Includes bibliographical references and index.
1. Introduction --
2. Overview of Regression Models for Cross-sectional Univariate Categorical Data --
3. Regression Models for Univariate Longitudinal Stationary Categorical Data --
4. Regression Models for Univariate Longitudinal Non-stationary Categorical Data --
5. Multinomial Models for Cross-sectional Bivariate Categorical Data --
6. Multinomial Models for Longitudinal Bivariate Categorical Data --
Index.
This is the first book in longitudinal categorical data analysis with parametric correlation models developed based on dynamic relationships among repeated categorical responses. This book is a natural generalization of the longitudinal binary data analysis to the multinomial data setup with more than two categories. Thus, unlike the existing books on cross-sectional categorical data analysis using log linear models, this book uses multinomial probability models both in cross-sectional and longitudinal setups. A theoretical foundation is provided for the analysis of univariate multinomial responses, by developing models systematically for the cases with no covariates as well as categorical covariates, both in cross-sectional and longitudinal setups. In the longitudinal setup, both stationary and non-stationary covariates are considered. These models have also been extended to the bivariate multinomial setup along with suitable covariates. For the inferences, the book uses the generalized quasi-likelihood as well as the exact likelihood approaches. The book is technically rigorous, and, it also presents illustrations of the statistical analysis of various real life data involving univariate multinomial responses both in cross-sectional and longitudinal setups. This book is written mainly for the graduate students and researchers in statistics and social sciences, among other applied statistics research areas.
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