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020 _a9781493921379
_9978-1-4939-2137-9
024 7 _a10.1007/978-1-4939-2137-9
_2doi
035 _ato000541732
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aSutradhar, Brajendra C.
_eauthor.
_9447294
245 1 0 _aLongitudinal Categorical Data Analysis
_helectronic resource
_cby Brajendra C. Sutradhar.
260 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aXVIII, 369 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aSpringer Series in Statistics,
_x0172-7397
505 0 _aIntroduction -- Overview of Regression Models for Cross-sectional Univariate Categorical Data -- Regression Models for Univariate Longitudinal Stationary Categorical Data -- Regression Models for Univariate Longitudinal Non-stationary Categorical Data -- Multinomial Models for Cross-sectional Bivariate Categorical Data -- Multinomial Models for Longitudinal Bivariate Categorical Data -- Index.
520 _aThis 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. However, the rest of the book, specifically the chapters from 1 to 3, may also be used for a senior undergraduate course in statistics. Brajendra Sutradhar is a University Research Professor at Memorial University in St. John's, Canada. He is author of the book Dynamic Mixed Models for Familial Longitudinal Data, published in 2011 by Springer, New York. Also, he edited the special issue of the Canadian Journal of Statistics (2010, Vol. 38, June Issue, John Wiley) and the Lecture Notes in Statistics (2013, Vol. 211, Springer), with selected papers from two symposiums: ISS-2009 and ISS-2012, respectively.
650 0 _aStatistics.
_9124796
650 0 _aMathematical statistics.
_9566264
650 1 4 _aStatistics.
_9124796
650 2 4 _aStatistical Theory and Methods.
_9303276
650 2 4 _aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
_9303278
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_9265982
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aSpringer Series in Statistics,
_9297514
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4939-2137-9
912 _aZDB-2-SMA
999 _c399533