| Categorical Data Analysis by Aic 1992 Edition Contributor(s): Sakamoto, Y. (Author) |
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ISBN: 0792314298 ISBN-13: 9780792314295 Publisher: Springer OUR PRICE: $52.24 Product Type: Hardcover Published: July 1992 Annotation: This volume presents a practical and unified approach to categorical data analysis based on the Akaike Information Criterion (AIe and the Akaike Bayesian Information Criterion (ABIe.Conventional procedures for categorical data analysis are often inappropriate because the classical test procedures employed are too closely related to specific models. The approach described in this volume enables actual problems encountered by data analysts to be handled much more successfully. Amongst various topics explicitly dealt with are the problem of variable selection for categorical data, a Bayesian binary regression, and a nonparametric density estimator and its application to nonparametric test problems. The practical utility of the procedure developed is demonstrated by considering its application to the analysis of various data.This volume complements the volume Akaike Information Criterion Statistics which has already appeared in this series.For statisticians working in mathematics, the social, behavioural, and medical sciences, and engineering. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - Multivariate Analysis - Mathematics | Applied |
| Dewey: 519.535 |
| LCCN: 91029237 |
| Series: Mathematics and Its Applications |
| Physical Information: 0.73" H x 6.94" W x 9.1" (1.03 lbs) 214 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: This volume presents a practical and unified approach to categorical data analysis based on the Akaike Information Criterion (AIC) and the Akaike Bayesian Information Criterion (ABIC). Conventional procedures for categorical data analysis are often inappropriate because the classical test procedures employed are too closely related to specific models. The approach described in this volume enables actual problems encountered by data analysts to be handled much more successfully. Amongst various topics explicitly dealt with are the problem of variable selection for categorical data, a Bayesian binary regression, and a nonparametric density estimator and its application to nonparametric test problems. The practical utility of the procedure developed is demonstrated by considering its application to the analysis of various data. This volume complements the volume Akaike Information Criterion Statistics which has already appeared in this series. For statisticians working in mathematics, the social, behavioural, and medical sciences, and engineering. |