Literaturverz. S. 575 - 600

Inclui bibliografia e índice

1. Why? -- 1.1. What is multilevel regression modeling? -- 1.2. Some examples from our own research -- 1.3. Motivations for multilevel modeling -- 1.4. Distinctive features of this book -- 1.5. Computing 2. Concepts and methods from basic probability and statistics -- 2.1. Probability distributions -- 2.2. Statistical inference -- 2.3. Classical confidence intervals -- 2.4. Classical hypothesos testing -- 2.5. Problems with statistical significance -- 2.6. 55,000 residents desperately need your help! -- 2.7. Bibliographic note -- 2.8. Exercises Part 1A: Single-level regression 3. Linear regression: the basic -- 3.1. One predictor -- 3.2. Multiple predictors -- 3.3. Interactions -- 3.4. Statistical inference -- 3.5. Graphical displays of data and fitted model -- 3.6. Assumptions and diagnostics -- 3.7. Prediction and validation -- 3.8. Bibliographic note -- 3.9. Exercises 4. Linear regression: before and after fitting the model -- 4.1. Linear transformations -- 4.2. Centering and standardizing, especially for models with interactions -- 4.3. Correlation and "regression to be mean" -- 4.4. Logarithmic transformations -- 4.5. Other transformations -- 4.6. Building regression models for prediction -- 4.7. Fitting a series of regressions -- 4.8. Bibliographic note -- 4.9. Exercises 5. Logistic regression -- 5.1. Logistic regression with a single predictor -- 5.2. Interpreting the logistic regression coefficients -- 5.3. Latent-data formulation -- 5.4. Building a logistic regression model: wells in Bangladesh -- 5.5. Logistic regression with interactions -- 5.6. Evaluating, checking, and comparing fitted logistic regressions -- 5.7. Average predictive comparisons on the probability scale -- 5.8. Identifiability and separation -- 5.9. Bibliographic note -- 5.10. Exercises 6. Generalized linear models -- 6.1. Introduction -- 6.2. Poisson regression, exposure, and overdispersion -- 6.3. Logistic-binomial model -- 6.4. Probit regression: normally distributed latent data -- 6.5. Ordered and unordered categorical regression -- 6.6. Robust regression using the t model -- 6.7. Building more complex generalized linear models -- 6.8. Constructive choice models -- 6.9. Bibliographic note -- 6.10. Exercises Part 1B: Working with regression inferences 7. Simulation of probability models and statistical inferences -- 7.1. Simulation of probability models -- 7.2. Summarizing linear regressions using simulation: an informal Bayesian approach -- 7.3. Simulation for nonlinear predictions: congressional elections -- 7.4. Predictive simulation for generalized linear models -- 7.5. Bibliographic note -- 7.6. Exercises 8. Simulation for checking statisitcal procedures and model fits -- 8.1. Fake-data simulation -- 8.2. Example: using fake-data simulation to understand residual plots -- 8.3. Simulating from the fitted model and comparing to actual data -- 8.4. Using predictive simulation to check the fix of a time-series model -- 8.5. Bibliographic note -- 8.6. Exercises 9. Causal inference using regression on the treatment variable -- 9.1. Causal inference and predictive comparisons -- 9.2. The fundamental problem of causal inference -- 9.3. Randomized experiments -- 9.4. Treatment interections and poststratification -- 9.5. Observational studies -- 9.6. Understanding causal inference in observational studies -- 9.7. Do not control for post-treatment variables -- 9.8. Intermediate outcomes and causal paths -- 9.9. Bibliographic note -- 9.10. Exercises 10. Causal inference using more advanced models -- 10.1. Imbalance and lack of complete overlap -- 10.2. Subclassification: effects and estimates for different subpopulations -- 10.3. Matchaing: subsetting the data to get overlapping and balanced treatment and control groups -- 10.4. Lack of overlap when the assignment mechanism is known: regression discontinuity -- 10.5. Estimating causal effects indirectly using instrumental variables -- 10.6. Isntrumental variables in a regression framework -- 10.7. Identification strategies that make use of variation within or between groups -- 10.8. Bibliographic note -- 10.9. Exercises Part 2A: Multilevel structures 11. Multilevel Structures -- 11.1. Varying-intercept and Varying-slope models -- 11.2. Clustered data: child support enforcement in cities -- 11.3. Repeated measurements, time-series cross sections, and other non-nested structures -- 11.4. Indicator variables and fixed or random effects -- 11.5. Costs and benefits of multilevel modeling -- 11.6. Bibliographic note -- 11.7. Exercises 12. Multilevel linear models: the basics -- 12.1. Notation -- 12.2. Partical pooling with no predictos -- 12.3. Partical pooling with predictos -- 12.4. Quickly fitting multilevel models in R -- 12.5. Five ways to write the samo model -- 12.6. Group-level predictors -- 12.7. Model building and statistical significance -- 12.8. Predictions for new observations and new groups -- 12.9. How Many groups and how many observations per group are needed to fit a multilrvel model? -- 12.10. Bibliographic note -- 12.11 Exercises 13. Multilevel linear models: varying slopes, non-nested models, and other complexities -- 13.1. Varying intercepts and slopes -- 13.2. Varying slopes without Varying intercepts -- 13.3. Modeling multiple Varying coefficients using the scaled inverse-Wishart distribution -- 13.4. Understanding correlations between group-level intercepts and slopes -- 13.5. Non-nested models -- 13.6. Selecting, transforming, and combining regression inputs -- 13.7. More complex multilevel models -- 13.8. Bibliographic note -- 13.9. Exercises 14. Multilevel logistic regression -- 14.1. State-level opinions from national polls -- 14.2. Red states and bue states: what's the matter with Connecticut? -- 14.3. Item-response and ideal-point models -- 14.4. Non-nested overdispersed model for death sentence reversals -- 14.5. Bibliographic note -- 14.6. Exercises 15. Multilevel generalized linear models -- 15.1. Overdispersed Poisson regression: police stops and ethnicity -- 15.2. Ordered categorical regression: storable votes -- 15.3. Non-nested negative-binomial model of structure in social networks -- 15.4. Bibliographic note -- 15.5 Exercises Part 2B: Fitting multilevel models 16. Multilevel modeling in Bugs and R: the basics -- 16.1. Why you should learn Bugs -- 16.2. Bayesian inference and prior distributions -- 16.3. Fitting and understanding a verying-intercept multilevel model using R and Bugs -- 16.4. Step by step through a Bugs model, as called from R -- 16.5. Adding individual- and group-level predictors -- 16.6. Predictions for new observations and new groups -- 16.7. Fake-data simulation -- 16.8. The principles of modeling in Bugs -- 16.9. Practical issues of implementation -- 16.10. Open-ended modeling in Bug -- 16.11. Bibliographic note -- 16.12. Exercises 17. Fitting multilevel linear and generalized linear models in Bugs and R -- 17.1. Varying-intercept, Varying-slope models -- 17.2. Varying intercepts and slopes with group-level predictors -- 17.3. Non-nested models -- 17.4. Multilevel logistic regression -- 17.5. Multilevel poisson regression -- 17.6. Multilevel ordered categotical regression -- 17.7. Latent-data parameterizations of generalized linear models -- 17.8. Bibliographic note -- 17.19. Exercises 18. Likelihood and Bayesian inference and computation -- 18.1. Least squares and maximum likelihood estimation -- 18.2. Uncertainty estimates using the likelihood surface -- 18.3. Bayesian inference for classical and multilevel regression -- 18.4. Gibbs sampler for multilevel linear models -- 18.5. Likelihood inference, Bayesian inference, and the Gibbs sampler: the case of censored data -- 18.6. Metropolis algotihm for more general Bayesian computation -- 18.7. Specifying a log postrior density, Gibbs sampler, and Metropolis algorithm in R -- 18.8. Bibliographic note -- 18.9. Exercises 19. Debugging and speeding convergence -- 19.1. Debugging and confidence building -- 19.2. General methods for reducing compurational requirements -- 19.3. Simples linear tramsfprmations -- 19.4. Redundant parameters and intentionally nonidentifiable models -- 19.5. Parameter expansion: multiplicative redundant parameters -- 19.6. Using redundant parameters to creatr an informative prior distribution for multilevel variance parameters -- 19.7. Bibliographic note -- 19.8. Exercises Part 3: From data collection to model understanding to model checking 20. Sample size and power calculations -- 20.1. Choices in the design of data collection -- 20.2. Classical power calculations: general principles, as illustrated by estimates of proportions -- 20.3. Classical power calculations for continuous outcomes -- 20.4. Multilevel Power calculation for cluster sampling -- 20.5. Multilevel Power calculation using fake-data simulation -- 20.6. Bibliographic note -- 20.7. Exercises 21- Understandig and summarizing the fitted models -- 21.1. Understandig and cariability -- 21.2. Superpopulation and finite-population variances -- 21.3. Contrasts and comparisons of multilevel coefficients -- 21.4. Average predictive comparisons -- 21.5. R² and explained cariance -- 21.6. Summarizing the amount of partial pooling -- 21.7. Adding a predictor can increase the residual variance! -- 21.8. Multiple comparisons and statistical sigificance 21.9. Bibliographic note -- 21.10. Exercises 22. Analysis of variance -- 22.1. Classical analysis if variance -- 22.2. ANOVA and multilevel linear and generalized linear models -- 22.3. Summarizing multilevel models using ANOVA -- 22.4. Doing ANOVA using multilevel models -- 22.5. Adding predictors: analysis of covariance and contrast analysis -- 22.6. Modeling the variance parameters: a split-plot latin square -- 22.7. Bibliographic note -- 22.8. Exercises 23. Causal inference using multilevel models -- 23.1. Multilevel aspects of data collection -- 23.2. Estimating treatment effects ia a multilevel observational study -- 23.3. Treatments applied at different levels -- 23.4. Instrumental variables and multilevel modeling -- 23.5. Bibliographic note -- 23.6. Exercises 24. Model checking and comparison -- 24.1. Principles of predictive checking -- 24.2. Example: a behavioral learning experiment -- 24.3. Model comparison and deviance -- 24.4. Bibliographic note -- 24.5. Exercises 25. Missing-data imputation -- 25.1. Missing-data mechanisms -- 25.2. Missing-data methods that discard data -- 25.3. Simple Missing-data approaches that retain all the data -- 25.4. Random imputation of a single varible -- 25.5. Imputation of several missing varibles -- 25.6. Model-based imputation -- 25.7. Combining inferences from multiple imputations -- 25.8. Bibliographic note -- 25.9. Exercises