Statistical Modeling
Modeling concepts: identifiability, overfitting, hierarchy, reparameterization, model selection and expansion. Simple linear regression, Fit analysis, Study of residuals, Multiple regression, Bayesian Regression, Violations of basic hypotheses, Model selection, Multicollinearity, Variable transformations, Exponential family and Generalized linear models, Nonlinear regression
Basic Information
Workload
60 hours
Requirements
Statistical inference
Mandatory:
- Reinaldo Charnet, Clarice Azevedo de Luna Freire, Eugênia M. Reginato Charnet and Heloísa Bonvino. Analysis of Linear Regression Models with applications, 2011, 2nd Edition. Unicamp Publisher.
- Annette J. Dobson, An Introduction to Generalized Linear Model, 1990, Chapman & Hall.
- Morris DeGroot, Mark Schervish. Probability and Statistics. Fourth Edition, 2012
Complementary:
- Casella, G., Berger, R., Statistical Inference, 2010, Cengage Learning
- Montgomery, D.C. & Peck, E.A., Introduction to Linear Regression, 1982, John Wiley & Sons
- Draper, N.R. & Smith, H., Applied Regression Analysis - Third Edition, 1998, John Wiley & Sons
- Wasserman, Larry. All of statistics: a concise course in statistical inference. Springer Science & Business Media, 2013.
- Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. New York: Springer series in statistics, 2001.