Logistic model tree

About: Logistic model tree is a research topic. Over the lifetime, 1041 publications have been published within this topic receiving 78759 citations.

Classification and regression trees

Abstract: Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Prediction Technologies > Statistical Fundamentals

Logistic Regression Models

Abstract: Preface Introduction The Normal Model Foundation of the Binomial Model Historical and Software Considerations Chapter Profiles Concepts Related to the Logistic Model 2 x 2 Table Logistic Model 2 x k Table Logistic Model Modeling a Quantitative Predictor Logistic Modeling Designs Estimation Methods Derivation of the IRLS Algorithm IRLS Estimation Maximum Likelihood Estimation Derivation of the Binary Logistic Algorithm Terms of the Algorithm Logistic GLM and ML Algorithms Other Bernoulli Models Model Development Building a Logistic Model Assessing Model Fit: Link Specification Standardized Coefficients Standard Errors Odds Ratios as Approximations of Risk Ratios Scaling of Standard Errors Robust Variance Estimators Bootstrapped and Jackknifed Standard Errors Stepwise Methods Handling Missing Values Modeling an Uncertain Response Constraining Coefficients Interactions Introduction Binary X Binary Interactions Binary X Categorical Interactions Binary X Continuous Interactions Categorical X Continuous Interaction Thoughts about Interactions Analysis of Model Fit Traditional Fit Tests for Logistic Regression Hosmer-Lemeshow GOF Test Information Criteria Tests Residual Analysis Validation Models Binomial Logistic Regression Overdispersion Introduction The Nature and Scope of Overdispersion Binomial Overdispersion Binary Overdispersion Real Overdispersion Concluding Remarks Ordered Logistic Regression Introduction The Proportional Odds Model Generalized Ordinal Logistic Regression Partial Proportional Odds Multinomial Logistic Regression Unordered Logistic Regression Independence of Irrelevant Alternatives Comparison to Multinomial Probit Alternative Categorical Response Models Introduction Continuation Ratio Models Stereotype Logistic Model Heterogeneous Choice Logistic Model Adjacent Category Logistic Model Proportional Slopes Models Panel Models Introduction Generalized Estimating Equations Unconditional Fixed Effects Logistic Model Conditional Logistic Models Random Effects and Mixed Models Logistic Regression Other Types of Logistic-Based Models Survey Logistic Models Scobit-Skewed Logistic Regression Discriminant Analysis Exact Logistic Regression Exact Methods Alternative Modeling Methods Conclusion Appendix A: Brief Guide to Using Stata Commands Appendix B: Stata and R Logistic Models Appendix C: Greek Letters and Major Functions Appendix D: Stata Binary Logistic Command Appendix E: Derivation of the Beta-Binomial Appendix F: Likelihood Function of the Adaptive Gauss-Hermite Quadrature Method of Estimation Appendix G: Data Sets Appendix H: Marginal Effects and Discrete Change References Author Index Subject Index Exercises and R Code appear at the end of most chapters.