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- Logit model for dichotomous data with single and multiple explanatory variables ,
- maximum likelihood (ML) estimation,
- large sample tests about parameters ,
- good of fit ,
- analysis of deviance ,
- variable selection ,
- extension to polytomous data ,
- introduction to Poisson regression.
- Linear models for two and three dimensional contingency tables:
- interpretation of parameters,
- comparison with ANOVA and regression.
- ML estimation of parameters.
- Likelihood ratio tests for various hypotheses,
- including independence,
- marginal and conditional independence,
- partial association .
- Models with quantitative levels.
- Nonparametric regression and generalized linear models.
- Interpolating and smoothing splines for simple regression.
- Use of cross-validation Applications to Logistic and Poisson regression.
- Additive models and generalized additive models.
E-Books (Full Text)
Regression Analysis of Count Data
by Cameron, A. Colin
Trivedi, Pravin K.
Publisher: Cambridge University Press
Log-Linear Models & Logistic Regression
by Christensen, Ronald
Nonparametric Statistical Methods And Related Topics
by Samaniego, F. j. Samaniego, Francisco J.
Roussas, George G.
Local Regression & Likelihood
by Loader, Clive. Chambers, J.
Elementary Introduction to Statistical Learning Theory
by Kulkarni, Sanjeev
Publisher: John Wiley & Sons