An introduction to categorical data analysis
Alan Agresti
Bok · Engelsk · 2019
| Omfang | xiii, 375 sider, : illustrasjoner
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| Utgave | Third edition.
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| Opplysninger | <p>Preface ix</p> <p>About the Companion Website xiii</p> <p><b>1 Introduction 1</b></p> <p>1.1 Categorical Response Data 1</p> <p>1.2 Probability Distributions for Categorical Data 3</p> <p>1.3 Statistical Inference for a Proportion 5</p> <p>1.4 Statistical Inference for Discrete Data 10</p> <p>1.5 Bayesian Inference for Proportions * 13</p> <p>1.6 Using R Software for Statistical Inference about Proportions * 17</p> <p>Exercises 21</p> <p><b>2 Analyzing Contingency Tables 25</b></p> <p>2.1 Probability Structure for Contingency Tables 26</p> <p>2.2 Comparing Proportions in 2 × 2 Contingency Tables 29</p> <p>2.3 The Odds Ratio 31</p> <p>2.4 Chi-Squared Tests of Independence 36</p> <p>2.5 Testing Independence for Ordinal Variables 42</p> <p>2.6 Exact Frequentist and Bayesian Inference * 46</p> <p>2.7 Association in Three-Way Tables 52</p> <p>Exercises 56</p> <p><b>3 Generalized Linear Models 65</b></p> <p>3.1 Components of a Generalized Linear Model 66</p> <p>3.2 Generalized Linear Models for Binary Data 68</p> <p>3.3 Generalized Linear Models for Counts and Rates 72</p> <p>3.4 Statistical Inference and Model Checking 76</p> <p>3.5 Fitting Generalized Linear Models 82</p> <p>Exercises 84</p> <p><b>4 Logistic Regression 89</b></p> <p>4.1 The Logistic Regression Model 89</p> <p>4.2 Statistical Inference for Logistic Regression 94</p> <p>4.3 Logistic Regression with Categorical Predictors 98</p> <p>4.4 Multiple Logistic Regression 102</p> <p>4.5 Summarizing Effects in Logistic Regression 107</p> <p>4.6 Summarizing Predictive Power: Classification Tables, ROC Curves, and Multiple Correlation 110</p> <p>Exercises 113</p> <p><b>5 Building and Applying Logistic Regression Models 123</b></p> <p>5.1 Strategies in Model Selection 123</p> <p>5.2 Model Checking 130</p> <p>5.3 Infinite Estimates in Logistic Regression 136</p> <p>5.4 Bayesian Inference, Penalized Likelihood, and Conditional Likelihood for Logistic Regression * 140</p> <p>5.5 Alternative Link Functions: Linear Probability and Probit Models * 145</p> <p>5.6 Sample Size and Power for Logistic Regression * 150</p> <p>Exercises 151</p> <p><b>6 Multicategory Logit Models 159</b></p> <p>6.1 Baseline-Category Logit Models for Nominal Responses 159</p> <p>6.2 Cumulative Logit Models for Ordinal Responses 167</p> <p>6.3 Cumulative Link Models: Model Checking and Extensions * 176</p> <p>6.4 Paired-Category Logit Modeling of Ordinal Responses * 184</p> <p>Exercises 187</p> <p><b>7 Loglinear Models for Contingency Tables and Counts 193</b></p> <p>7.1 Loglinear Models for Counts in Contingency Tables 194</p> <p>7.2 Statistical Inference for Loglinear Models 200</p> <p>7.3 The Loglinear – Logistic Model Connection 207</p> <p>7.4 Independence Graphs and Collapsibility 210</p> <p>7.5 Modeling Ordinal Associations in Contingency Tables 214</p> <p>7.6 Loglinear Modeling of Count Response Variables * 217</p> <p>Exercises 221</p> <p><b>8 Models for Matched Pairs 227</b></p> <p>8.1 Comparing Dependent Proportions for Binary Matched Pairs 228</p> <p>8.2 Marginal Models and Subject-Specific Models for Matched Pairs 230</p> <p>8.3 Comparing Proportions for Nominal Matched-Pairs Responses 235</p> <p>8.4 Comparing Proportions for Ordinal Matched-Pairs Responses 239</p> <p>8.5 Analyzing Rater Agreement * 243</p> <p>8.6 Bradley–Terry Model for Paired Preferences * 247</p> <p>Exercises 249</p> <p><b>9 Marginal Modeling of Correlated, Clustered Responses 253</b></p> <p>9.1 Marginal Models Versus Subject-Specific Models 254</p> <p>9.2 Marginal Modeling: The Generalized Estimating Equations (GEE) Approach 255</p> <p>9.3 Marginal Modeling for Clustered Multinomial Responses 260</p> <p>9.4 Transitional Modeling, Given the Past 263</p> <p>9.5 Dealing with Missing Data * 266</p> <p>Exercises 268</p> <p><b>10 Random Effects: Generalized Linear Mixed Models 273</b></p> <p>10.1 Random Effects Modeling of Clustered Categorical Data 273</p> <p>10.2 Examples: Random Effects Models for Binary Data 278</p> <p>10.3 Extensions to Multinomial Responses and Multiple Random Effect Terms 284</p> <p>10.4 Multilevel (Hierarchical) Models 288</p> <p>10.5 Latent Class Models * 291</p> <p>Exercises 295</p> <p><b>11 Classification and Smoothing * 299</b></p> <p>11.1 Classification: Linear Discriminant Analysis 300</p> <p>11.2 Classification: Tree-Based Prediction 302</p> <p>11.3 Cluster Analysis for Categorical Responses 306</p> <p>11.4 Smoothing: Generalized Additive Models 310</p> <p>11.5 Regularization for High-Dimensional Categorical Data (Large p) 313</p> <p>Exercises 321</p> <p><b>12 A Historical Tour of Categorical Data Analysis * 325</b></p> <p>Appendix: Software for Categorical Data Analysis 331</p> <p>A.1 R for Categorical Data Analysis 331</p> <p>A.2 SAS for Categorical Data Analysis 332</p> <p>A.3 Stata for Categorical Data Analysis 342</p> <p>A.4 SPSS for Categorical Data Analysis 346</p> <p>Brief Solutions to Odd-Numbered Exercises 349</p> <p>Bibliography 363</p> <p>Examples Index 365</p> <p>Subject Index 369</p>
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| Emner | Multivariate analysis. Statistiske analyser
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| Dewey | |
| ISBN | 9781119405269
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