Categorical Data Analysis, 2/e (Hardcover)
暫譯: 類別資料分析，第二版 (精裝本)

Alan Agresti

Categorical Data Analysis, 2/e (Hardcover)
  • 出版商: Wiley
  • 出版日期: 2002-07-22
  • 售價: $1,401
  • 語言: 英文
  • 頁數: 734
  • 裝訂: Hardcover
  • ISBN: 0471360937
  • ISBN-13: 9780471360933
  • 相關分類: Data Science

    • 已過版

買這商品的人也買了...

相關主題

商品描述

Description

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Categorical Data Analysis was among those chosen.

A valuable new edition of a standard reference.
"A 'must-have' book for anyone expecting to do research and/or applications in categorical data analysis."
-Statistics in Medicine on Categorical Data Analysis, First Edition

The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. Responding to new developments in the field as well as to the needs of a new generation of professionals and students, this new edition of the classic Categorical Data Analysis offers a comprehensive introduction to the most important methods for categorical data analysis.

Designed for statisticians and biostatisticians as well as scientists and graduate students practicing statistics, Categorical Data Analysis, Second Edition summarizes the latest methods for univariate and correlated multivariate categorical responses. Readers will find a unified generalized linear models approach that connects logistic regression and Poisson and negative binomial regression for discrete data with normal regression for continuous data. Adding to the value in the new edition is coverage of:

  • Three new chapters on methods for repeated measurement and other forms of clustered categorical data, including marginal models and associated generalized estimating equations (GEE) methods, and mixed models with random effects
  • Stronger emphasis on logistic regression modeling of binary and multicategory data
  • An appendix showing the use of SAS for conducting nearly all analyses in the book
  • Prescriptions for how ordinal variables should be treated differently than nominal variables
  • Discussion of exact small-sample procedures
  • More than 100 analyses of real data sets to illustrate application of the methods, and more than 600 exercises

Table of Contents

Preface.

1. Introduction: Distributions and Inference for Categorical Data.

1.1 Categorical Response Data.

1.2 Distributions for Categorical Data.

1.3 Statistical Inference for Categorical Data.

1.4 Statistical Inference for Binomial Parameters.

1.5 Statistical Inference for Multinomial Parameters.

Notes.

Problems.

2. Describing Contingency Tables.

2.1 Probability Structure for Contingency Tables.

2.2 Comparing Two Proportions.

2.3 Partial Association in Stratified 2 x 2 Tables.

2.4 Extensions for I x J Tables.

Notes.

Problems.

3. Inference for Contingency Tables.

3.1 Confidence Intervals for Association Parameters.

3.2 Testing Independence in Two-Way Contingency Tables.

3.3 Following-Up Chi-Squared Tests.

3.4 Two-Way Tables with Ordered Classifications.

3.5 Small-Sample Tests of Independence.

3.6 Small-Sample Confidence Intervals for 2 x 2 Tables*.

3.7 Extensions for Multiway Tables and Nontabulated Responses.

Notes.

Problems.

4. Introduction to Generalized Linear Models.

4.1 Generalized Linear Model.

4.2 Generalized Linear Models for Binary Data.

4.3 Generalized Linear Models for Counts.

4.4 Moments and Likelihood for Generalized Linear Models*.

4.5 Inference for Generalized Linear Models.

4.6 Fitting Generalized Linear Models.

4.7 Quasi-likelihood and Generalized Linear Models*.

4.8 Generalized Additive Models*.

Notes.

Problems.

5. Logistic Regression.

5.1 Interpreting Parameters in Logistic Regression.

5.2 Inference for Logistic Regression.

5.3 Logit Models with Categorical Predictors.

5.4 Multiple Logistic Regression.

5.5 Fitting Logistic Regression Models.

Notes.

Problems.

6. Building and Applying Logistic Regression Models.

6.1 Strategies in Model Selection.

6.2 Logistic Regression Diagnostics.

6.3 Inference About Conditional Associations in 2 x 2 x K Tables.

6.4 Using Models to Improve Inferential Power.

6.5 Sample Size and Power Considerations*.

6.6 Probit and Complementary Log-Log Models*.

6.7 Conditional Logistic Regression and Exact

Distributions*.

Notes.

Problems.

7. Logit Models for Multinomial Responses.

7.1 Nominal Responses: Baseline-Category Logit Models.

7.2 Ordinal Responses: Cumulative Logit Models.

7.3 Ordinal Responses: Cumulative Link Models.

7.4 Alternative Models for Ordinal Responses*.

7.5 Testing Conditional Independence in I x J x K Tables*.

7.6 Discrete-Choice Multinomial Logit Models*.

Notes.

Problems.

8. Loglinear Models for Contingency Tables.

8.1 Loglinear Models for Two-Way Tables.

8.2 Loglinear Models for Independence and Interaction in Three-Way Tables.

8.3 Inference for Loglinear Models.

8.4 Loglinear Models for Higher Dimensions.

8.5 The Loglinear_Logit Model Connection.

8.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions*.

8.7 Loglinear Model Fitting: Iterative Methods and their Application*.

Notes.

Problems.

9. Building and Extending Loglinear/Logit Models.

9.1 Association Graphs and Collapsibility.

9.2 Model Selection and Comparison.

9.3 Diagnostics for Checking Models.

9.4 Modeling Ordinal Associations.

9.5 Association Models*.

9.6 Association Models, Correlation Models, and Correspondence Analysis*.

9.7 Poisson Regression for Rates.

9.8 Empty Cells and Sparseness in Modeling Contingency Tables.

Notes.

Problems.

10. Models for Matched Pairs.

10.1 Comparing Dependent Proportions.

10.2 Conditional Logistic Regression for Binary Matched Pairs.

10.3 Marginal Models for Square Contingency Tables.

10.4 Symmetry, Quasi-symmetry, and Quasiindependence.

10.5 Measuring Agreement Between Observers.

10.6 Bradley-Terry Model for Paired Preferences.

10.7 Marginal Models and Quasi-symmetry Models for Matched Sets*.

Notes.

Problems.

11. Analyzing Repeated Categorical Response Data.

11.1 Comparing Marginal Distributions: Multiple Responses.

11.2 Marginal Modeling: Maximum Likelihood Approach.

11.3 Marginal Modeling: Generalized Estimating Equations Approach.

11.4 Quasi-likelihood and Its GEE Multivariate Extension: Details*.

11.5 Markov Chains: Transitional Modeling.

Notes.

Problems.

12. Random Effects: Generalized Linear Mixed Models for Categorical Responses.

12.1 Random Effects Modeling of Clustered Categorical Data.

12.2 Binary Responses: Logistic-Normal Model.

12.3 Examples of Random Effects Models for Binary Data.

12.4 Random Effects Models for Multinomial Data.

12.5 Multivariate Random Effects Models for Binary Data.

12.6 GLMM Fitting, Inference, and Prediction.

Notes.

Problems.

13. Other Mixture Models for Categorical Data*.

13.1 Latent Class Models.

13.2 Nonparametric Random Effects Models.

13.3 Beta-Binomial Models.

13.4 Negative Binomial Regression.

13.5 Poisson Regression with Random Effects.

Notes.

Problems.

14. Asymptotic Theory for Parametric Models.

14.1 Delta Method.

14.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities.

14.3 Asymptotic Distributions of Residuals and Goodnessof-Fit Statistics.

14.4 Asymptotic Distributions for Logit/Loglinear Models.

Notes.

Problems.

15. Alternative Estimation Theory for Parametric Models.

15.1 Weighted Least Squares for Categorical Data.

15.2 Bayesian Inference for Categorical Data.

15.3 Other Methods of Estimation.

Notes.

Problems.

16. Historical Tour of Categorical Data Analysis*.

16.1 Pearson-Yule Association Controversy.

16.2 R. A. Fisher's Contributions.

16.3 Logistic Regression.

16.4 Multiway Contingency Tables and Loglinear Models.

16.5 Recent and Future? Developments.

Appendix A. Using Computer Software to Analyze Categorical Data.

A.1 Software for Categorical Data Analysis.

A.2 Examples of SAS Code by Chapter.

Appendix B. Chi-Squared Distribution Values.

References.

Examples Index.

Author Index.

Subject Index.

*Sections marked with an asterisk are less important for an overview.

商品描述(中文翻譯)

**描述**

《Amstat News》在2003年9月的期刊中請三位評審編輯評選他們最喜愛的五本書籍，其中《Categorical Data Analysis》被選中。

這是一本有價值的新版本標準參考書。「對於任何期望在類別數據分析中進行研究和/或應用的人來說，這是一本必備的書籍。」-《Statistics in Medicine》對《Categorical Data Analysis》第一版的評價。

使用統計方法分析類別數據的需求急劇增加，特別是在生物醫學和社會科學的應用中。這本經典的《Categorical Data Analysis》新版本回應了該領域的新發展以及新一代專業人士和學生的需求，提供了對類別數據分析最重要方法的全面介紹。

本書針對統計學家、生物統計學家以及實踐統計的科學家和研究生而設計，《Categorical Data Analysis》第二版總結了最新的單變量和相關的多變量類別反應方法。讀者將會發現一種統一的廣義線性模型方法，將邏輯回歸、泊松回歸和負二項回歸（針對離散數據）與連續數據的常規回歸相連接。新版本的價值還包括以下內容：

- 三個新章節，介紹重複測量和其他形式的聚類類別數據的方法，包括邊際模型及相關的廣義估計方程（GEE）方法，以及具有隨機效應的混合模型
- 更強調二元和多類別數據的邏輯回歸建模
- 附錄展示如何使用SAS進行書中幾乎所有的分析
- 對有序變量應如何與名義變量不同對待的說明
- 討論精確的小樣本程序
- 超過100個真實數據集的分析，以說明方法的應用，並提供超過600個練習題

**目錄**

前言。

1. 介紹：類別數據的分佈與推斷。
1.1 類別反應數據。
1.2 類別數據的分佈。
1.3 類別數據的統計推斷。
1.4 二項參數的統計推斷。
1.5 多項參數的統計推斷。
註解。
問題。

2. 描述列聯表。
2.1 列聯表的概率結構。
2.2 比較兩個比例。
2.3 分層2 x 2表中的部分關聯。
2.4 I x J表的擴展。
註解。
問題。

3. 列聯表的推斷。
3.1 關聯參數的置信區間。
3.2 測試二維列聯表中的獨立性。
3.3 跟進卡方檢驗。
3.4 有序分類的二維表。
3.5 小樣本獨立性檢驗。
3.6 2 x 2表的小樣本置信區間*。
3.7 多維表和非表格反應的擴展。
註解。
問題。

4. 廣義線性模型介紹。
4.1 廣義線性模型。
4.2 二元數據的廣義線性模型。
4.3 計數的廣義線性模型。
4.4 廣義線性模型的矩和似然*。
4.5 廣義線性模型的推斷。
4.6 廣義線性模型的擬合。
4.7 準似然和廣義線性模型*。
4.8 廣義加性模型*。
註解。
問題。

5. 邏輯回歸。
5.1 解釋邏輯回歸中的參數。
5.2 邏輯回歸的推斷。
5.3 具有類別預測變數的Logit模型。
5.4 多重邏輯回歸。
5.5 擬合邏輯回歸模型。
註解。
問題。

6. 建立和應用邏輯回歸模型。
6.1 模型選擇策略。
6.2 邏輯回歸診斷。
6.3 在2 x 2 x K表中對條件關聯的推斷。
6.4 使用模型提高推斷能力。
6.5 樣本大小和效能考量*。
6.6 Probit和補充Log-Log模型*。
6.7 條件邏輯回歸和精確分佈*。
註解。
問題。

7. 多項反應的Logit模型。
7.1 名義反應：基線類別Logit模型。
7.2 有序反應：累積Logit模型。
7.3 有序反應：累積鏈接模型。
7.4 有序反應的替代模型*。
7.5 在I x J x K表中測試條件獨立性*。
7.6 離散選擇多項Logit模型*。
註解。
問題。

8. 列聯表的對數線性模型。
8.1 二維表的對數線性模型。
8.2 三維表中的獨立性和交互作用的對數線性模型。
8.3 對數線性模型的推斷。
8.4 更高維度的對數線性模型。
8.5 對數線性模型與Logit模型的聯繫。
8.6 對數線性模型擬合：似然方程和漸近分佈*。
8.7 對數線性模型擬合：迭代方法及其應用*。
註解。
問題。

9. 建立和擴展對數線性/Logit模型。
9.1 關聯圖和可合併性。
9.2 模型選擇和比較。
9.3 檢查模型的診斷。
9.4 建模有序關聯。
9.5 關聯模型*。
9.6 關聯模型、相關模型和對應分析*。
9.7 率的泊松回歸。
9.8 列聯表建模中的空單元和稀疏性。
註解。
問題。

10. 配對模型。
10.1 比較依賴比例。
10.2 二元配對的條件邏輯回歸。
10.3 正方形列聯表的邊際模型。
10.4 對稱、準對稱和準獨立性。
10.5 測量觀察者之間的一致性。
10.6 Bradley-Terry模型的配對偏好。
10.7 配對集的邊際模型和準對稱模型*。
註解。
問題。

11. 分析重複的類別反應數據。
11.1 比較邊際分佈：多重反應。
11.2 邊際建模：最大似然方法。
11.3 邊際建模：廣義估計方程方法。
11.4 準似然及其GEE多變量擴展：細節*。
11.5 馬爾可夫鏈：轉移建模。
註解。
問題。

12. 隨機效應：類別反應的廣義線性混合模型。
12.1 聚類類別數據的隨機效應建模。
12.2 二元反應：邏輯-常態模型。
12.3 二元數據的隨機效應模型示例。
12.4 多項數據的隨機效應模型。
12.5 二元數據的多變量隨機效應模型。
12.6 GLMM擬合、推斷和預測。
註解。
問題。

13. 類別數據的其他混合模型*。
13.1 潛在類別模型。
13.2 非參數隨機效應模型。
13.3 Beta-二項模型。
13.4 負二項回歸。
13.5 具有隨機效應的泊松回歸。
註解。
問題。

14. 參數模型的漸近理論。
14.1 Delta方法。
14.2 模型參數和單元概率估計量的漸近分佈。
14.3 殘差和擬合優度統計的漸近分佈。
14.4 Logit/對數線性模型的漸近分佈。

類似商品