Applying Generalized Linear Models
暫譯: 應用廣義線性模型

James K. Lindsey

Applying Generalized Linear Models
  • 出版商: Springer
  • 出版日期: 1997-06-20
  • 售價: $4,210
  • 貴賓價: 9.5 折 $4,000
  • 語言: 英文
  • 頁數: 276
  • ISBN: 0387982183
  • ISBN-13: 9780387982182

    • 海外代購書籍(需單獨結帳)

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商品描述

Description

This book describes how generalised linear modelling procedures can be used in many different fields, without becoming entangled in problems of statistical inference. The author shows the unity of many of the commonly used models and provides readers with a taste of many different areas, such as survival models, time series, and spatial analysis, and of their unity. As such, this book will appeal to applied statisticians and to scientists having a basic grounding in modern statistics. With many exercises at the end of each chapter, it will equally constitute an excellent text for teaching applied statistics students and non- statistics majors. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, being familiar at least with the analysis of the simpler normal linear models, regression and ANOVA.

 

Table of Contents

Generalized Linear Modelling: Statistical Modelling.- Exponential Dispersion Models.- Linear Structure.- Three Components of a GLM.- Possible Models.- Inference.- Exercises. Discrete Data: Log Linear Models.- Models of Change.- Overdispersion.- Exercises. Fitting and Comparing Probability Distributions: Fitting Distributions.- Setting Up the Model.- Special Cases.- Exercises. Growth Curves: Exponential Growth Curves.- Logistic Growth Curve.- Gomperz Growth Curve.- More Complex Models.- Exercises. Time Series: Poisson Processes.- Markov Processes.- Repeated Measurements.- Exercises. Survival Data: General Concepts.- "Nonparametric" Estimation.- Parametric Models.- "Semiparametric" Models.- Exercises. Event Histories: Event Histories and Survival Distributions.- Counting processes.- Modelling Event Histories.- Generalizations.- Exercises. Spatial data: Spatial Interaction.- Spatial Patterns.- Exercises. Normal Models: Linear Regression.- Analysis of Variance.- Nonlinear Regression.- Exercises. Dynamic Models: Dynamic Generalized Linear Models.- Normal Models.- Count Data.- Positive Response Data.- Continuous Time Nonlinear Models. Appendices: Inference.- Diagnostics.- References.- Index.

商品描述(中文翻譯)

**描述**

本書描述了如何在許多不同領域中使用廣義線性模型程序，而不會陷入統計推斷的問題。作者展示了許多常用模型的統一性，並為讀者提供了生存模型、時間序列和空間分析等多個不同領域的概覽及其統一性。因此，本書將吸引應用統計學家以及具備現代統計基礎的科學家。每章結尾都有許多練習題，這也使其成為教授應用統計學學生和非統計專業學生的優秀教材。讀者應具備基本的統計原則知識，無論是從貝葉斯、頻率主義或直接似然的角度出發，至少應熟悉較簡單的正態線性模型、回歸分析和變異數分析（ANOVA）。

**目錄**

廣義線性建模：統計建模。- 指數散佈模型。- 線性結構。- GLM的三個組成部分。- 可能的模型。- 推斷。- 練習。離散數據：對數線性模型。- 變化模型。- 過度散佈。- 練習。擬合和比較概率分佈：擬合分佈。- 設定模型。- 特殊情況。- 練習。增長曲線：指數增長曲線。- 邏輯斯增長曲線。- Gompertz增長曲線。- 更複雜的模型。- 練習。時間序列：泊松過程。- 馬可夫過程。- 重複測量。- 練習。生存數據：一般概念。- '非參數'估計。- 參數模型。- '半參數'模型。- 練習。事件歷史：事件歷史和生存分佈。- 計數過程。- 建模事件歷史。- 一般化。- 練習。空間數據：空間互動。- 空間模式。- 練習。正態模型：線性回歸。- 變異數分析。- 非線性回歸。- 練習。動態模型：動態廣義線性模型。- 正態模型。- 計數數據。- 正響應數據。- 連續時間非線性模型。附錄：推斷。- 診斷。- 參考文獻。- 索引。

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