000			02946nmm a22002535i 4500
005			20230705150641.0
008			121227s1989 xxu| s |||| 0|eng d
020			_a9781461210177 _9978-1-4612-1017-7
082			_a300.727 _223
100			_aSantner, Thomas J. _920694
245	1	4	_aThe Statistical Analysis of Discrete Data _h[electronic resource] / _cby Thomas J. Santner, Diane E. Duffy.
250			_a1st ed. 1989.
260			_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c1989.
300			_aXII, 372 p. _bonline resource.
505			_a1 Introduction -- 2 Univariate Discrete Responses -- 3 Loglinear Models -- 4 Cross-Classified Data -- 5 Univariate Discrete Data with Covariates -- Appendix 1. Some Results from Linear Algebra -- Appendix 2. Maximization of Concave Functions -- Appendix 3. Proof of Proposition 3.3.1 (ii) and (iii) -- Appendix 4. Elements of Large Sample Theory -- Problems -- References -- List of Notation -- Index to Data Sets -- Author Index.
520			_aThe Statistical Analysis of Discrete Data provides an introduction to cur­ rent statistical methods for analyzing discrete response data. The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data. The book's mathematical prereq­ uisites are linear algebra and elementary advanced calculus. It assumes a basic statistics course which includes some decision theory, and knowledge of classical linear model theory for continuous response data. Problems are provided at the end of each chapter to give the reader an opportunity to ap­ ply the methods in the text, to explore extensions of the material covered, and to analyze data with discrete responses. In the text examples, and in the problems, we have sought to include interesting data sets from a wide variety of fields including political science, medicine, nuclear engineering, sociology, ecology, cancer research, library science, and biology. Although there are several texts available on discrete data analysis, we felt there was a need for a book which incorporated some of the myriad recent research advances. Our motivation was to introduce the subject by emphasizing its ties to the well-known theories of linear models, experi­ mental design, and regression diagnostics, as well as to describe alterna­ tive methodologies (Bayesian, smoothing, etc. ); the latter are based on the premise that external information is available. These overriding goals, to­ gether with our own experiences and biases, have governed our choice of topics.
650			_aStatistics . _920695
650			_aProbabilities. _920696
650			_aStatistics in Business, Management, Economics, Finance, Insurance. _920697
650			_aProbability Theory. _920698
700			_aDuffy, Diane E. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _920699
856			_uhttps://doi.org/10.1007/978-1-4612-1017-7
942			_cEBK
999			_c13667 _d13667