Courses

(i) The concepts of the sample, the unknown population parameters and the statistical function. (ii) General information on the estimation of measures. (iii) Criteria for selecting estimators: mean square error, unbiasedness. (iv) CramérRao inequality and Fisher statistical information. (v) Adequate statistical functions, completeness and AOED estimators. (vi) Estimation in exponential families of distributions. (vii) Basu's theorem, independence of sample mean and sample variance in normal populations. (viii) Sampling distributions (X2, t, F). (ix) Methods for finding point estimators: maximum likelihood, method of moments. (x) Elements of statistical decision theory, Bayes and minimax estimators. (xi) Confidence intervals. In order to highlight the special elements of pedagogical and teaching competence contained in the course, emphasis is placed on its historical evolution, the development of its cognitive subject, as well as its applications in technology and/or other sciences.