Author | Santner, Thomas J. author |
---|---|

Title | The Statistical Analysis of Discrete Data [electronic resource] / by Thomas J. Santner, Diane E. Duffy |

Imprint | New York, NY : Springer New York : Imprint: Springer, 1989 |

Connect to | http://dx.doi.org/10.1007/978-1-4612-1017-7 |

Descript | XII, 372 p. online resource |

SUMMARY

The Statistical Analysis of Discrete Data provides an introduction to curยญ 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 prereqยญ 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 apยญ 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, experiยญ mental design, and regression diagnostics, as well as to describe alternaยญ tive methodologies (Bayesian, smoothing, etc. ); the latter are based on the premise that external information is available. These overriding goals, toยญ gether with our own experiences and biases, have governed our choice of topics

CONTENT

1 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

Statistics
Probabilities
Statistics
Statistics for Business/Economics/Mathematical Finance/Insurance
Probability Theory and Stochastic Processes