The Analysis of Stochastic Processes using GLIM

James K. Lindsey

ISBN 10: 0387977619 / ISBN 13: 9780387977614

Published by Springer New York, Springer US Apr 1992, 1992

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -The aim of this book is to present a survey of the many ways in which the statistical package GLIM may be used to model and analyze stochastic processes. Its emphasis is on using GLIM interactively to apply statistical techniques, and examples are drawn from a wide range of applications including medicine, biology, and the social sciences. It is based on the author's many years of teaching courses along these lines to both undergraduate and graduate students. The author assumes that readers have a reasonably strong background in statistics such as might be gained from undergraduate courses and that they are also familiar with the basic workings of GLIM. Topics covered include: the analysis of survival data, regression and fitting distributions, time series analysis (including both the time and frequency domains), repeated measurements, and generalized linear models.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 304 pp. Englisch.

Seller Inventory # 9780387977614

Bibliographic Details

• Title: The Analysis of Stochastic Processes using GLIM
• Publisher: Springer New York, Springer US Apr 1992
• Publication Date: 1992
• Language: English
• ISBN 10: 0387977619
• ISBN 13: 9780387977614
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 242W x 170H x 17D millimeters 242 millimeters width by 170 millimeters height by 17 millimeters depth
• Weight: 524 grams

About this title

Synopsis

The aim of this book is to present a survey of the many ways in which the statistical package GLIM may be used to model and analyze stochastic processes. Its emphasis is on using GLIM interactively to apply statistical techniques, and examples are drawn from a wide range of applications including medicine, biology, and the social sciences. It is based on the author's many years of teaching courses along these lines to both undergraduate and graduate students. The author assumes that readers have a reasonably strong background in statistics such as might be gained from undergraduate courses and that they are also familiar with the basic workings of GLIM. Topics covered include: the analysis of survival data, regression and fitting distributions, time series analysis (including both the time and frequency domains), repeated measurements, and generalized linear models.