Calculus I and II Probability I and II Statistical Inference Methods I and II Programming I and II Linear Algebra Advanced Mathematics Regression Methods Advanced Regression Methods Multivariate Analysis

* General competences - Description and data synthesis: Description of a set of data based on numeric and graphic measurements both at univariate and multivariate levels demonstrating possible relations between variables of interest. - Modelling Ability to identify or create the appropriate model for a specific problem arising from each business activity (finance, marketing, production planning and control etc.). - Model Analysis and Validation: Capacity to computationally manipulate established models, making the most of the power of statistical, optimisation methods etc. and analysing the results obtained. - Drawing conclusions: Ability to perceive the nature of problems and interpret solutions provided by the corresponding models in a useful way, in order to improve performance in the various areas of a business (finance, production, quality, marketing, etc.). - Presentation and communication of results: Ability to communicate results, conclusions of models and solutions proposed in a manner which is intelligible to the rest of the company, in order to ensure that they are accepted and implemented by decision makers. * Specific competences - Data description and synthesis. - Modelling and statistical analysis of both static and dynamic data. - Correct and rational use of software. - Ability to devise and construct models and validate them. - Graphic representation of data. - Interpretation of results based on statistical models.

ATTENTION: all the teaching materials are given in ENGLISH. Lessons are in Spanish. This course is designed to give a panoramic view of several tools available for nonparametric estimation, at an intermediate level. This view covers in-depth the main concepts in the estimation of the distribution, density and regression function. The focus is placed on providing the main insights on the statistical/mathematical foundations of nonparametric estimation and on showing its effective implementation with the use of the statistical software R. 1. Introduction and review 1.1. Why nonparametric statistics? 1.2. Review on statistical inference 1.3. Review on probability 1.4. Useful inequalities 1.5. Landau's notation 2. Nonparametric estimation of the distribution function 2.1. The empirical distribution function 2.2. Properties of the empirical distribution function 2.3. Applications 3. Nonparametric estimation of the density function 3.1. The histogram 3.2. The Parzen-Rosenblatt's estimator 3.3. Properties of the estimator 3.4. Selection of the smoothing parameter 3.5. Modifications 3.6. Multivariate density estimation 4. Nonparametric estimation of the regression function 4.1. The regressogram 4.2. The Nadaraya-Watson's estimator 4.3. The local polynomial estimator 4.4. Properties of the local polynomial estimator 4.5. Selection of the smoothing parameter The program is subject to modifications due to the course development and/or academic calendar.

The lessons combine theory sessions (methods description) and practical sessions (exercises, computational implementation and practical usage of methods). The implementation of the methods is done with the statistical language R, so a good knowledge of R is fundamental.