Logistic Regression ModelsLogistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models t |
Contents
| 1 | |
| 15 | |
Chapter 3 Estimation Methods | 51 |
Chapter 4 Derivation of the Binary Logistic Algorithm | 63 |
Chapter 5 Model Development | 73 |
Chapter 6 Inteactions | 189 |
Chapter 7 Analysis of Model Fit | 243 |
Chapter 8 Binomial Logistic Regression | 297 |
Conclusion | 559 |
Brief Guide to Using Stata Commands | 561 |
Stata and R Logistic Models | 589 |
Greek Letters and Major Functions | 591 |
Stata Binary Logistic Command | 593 |
Derivation of the Beta Binomial | 597 |
Likelihood Function of the Adaptive GaussHermite Quadrature Method of Estimation | 599 |
Data Sets | 601 |
Chapter 9 Overdispersion | 319 |
Chapter 10 Ordered Logistic Regression | 353 |
Chapter 11 Multinomial Logistic Regression | 385 |
Chapter 12 Alternative Categorical Response Models | 411 |
Chapter 13 Panel Models | 441 |
Chapter 14 Other Types of LogisticBased Models | 519 |
Chapter 15 Exact Logistic Regression | 543 |
Marginal Effects and Discrete Change | 605 |
| 613 | |
| 625 | |
| 629 | |
Back cover | 639 |
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Common terms and phrases
_cons adjusted age3 age4 agegrp AIC statistic algorithm Bernoulli binary logistic binary response byte calculated chi2 Pseudo R2 clustering coefficients command Conf confidence intervals continuous predictor correlation structure covariate pattern data set death anterior hcabg deviance statistic difference discussed displayed distribution effects models exp coef exponential family fam(bin family-binomial fit9 fixed effects fixed effects model GEE models glm death hcabg kk2-kk4 Hilbe interaction killip level likelihood ratio test linear models linear predictor Link function logistic model logistic regression model Logistic regression Number loglog matrix maximum likelihood method missing values multinomial multinomial probit Number of obs obs LR Odds Ratio Std overdispersion p-value panel parameter estimates parameterized patients percent predict Prob probability probit random effects random effects model reference relationship response models risk ratio robust Scale parameter standard errors Stata statisticians summary variable Variance function Wald whlo