The course is an introduction to the inference methods in case of statistical models, the evaluation and the selection of models:

1. Classical inference for linear and nonlinear models (Wald tests; tests based on maximum likelihood)
2. Evaluation and models’ selection (Bias, variance and complexity; selection criteria; cross-validation)
3. Resampling methods (Introduction to bootstrap; tests and confidence intervals; linear regression model)
4. Algorithm EM

The theoretical concepts and methods are illustrated by practical applications. R is the software used.

Moodle

The course is part of the supplied of Master courses in applied statistics, which is a packet of four half-yearly ones of 4.5 ECTS (one by semester) over two years:

1. Topics in multivariate statistics
2. Introduction to Bayesian statistics
3. Inference, evaluation, and selection of models
4. Classification methods

Although they complete each other, they can be chosen separately. Of general interest, the set of courses gives a broad view of problems and applied statistical methods, and of the data science. A  server of Jupyter notebooks completes the course.

There is no specific public. Although the courses are primarily conceived for students of the Faculty of Management, Economics and Social Sciences, they can be attended by other students. 

The student will benefit not only of theoretical knowledge but is also skilled in the use of the methods presented during the course.

A script with a listing of references is provided. The student will find on the course's Moodle platform other resources.