• Obiettivi e contenuti
    • OBIETTIVI FORMATIVI

      The course aims at providing a thorough account of classical statistical inference at an intermediate level. After an introduction to probability theory, the course will focus on point and interval estimation and on hypothesis testing. Particular attention will be given to likelihood based methods. Classes will be held in English.

      OBIETTIVI FORMATIVI (DETTAGLIO) E RISULTATI DI APPRENDIMENTO

      The course aims at providing a thorough account of classical statistical inference at an intermediate level. After an introduction to probability theory, the course will focus on point and interval estimation and on hypothesis testing. Particular attention will be given to likelihood based methods.

      Classes will be held in English.

      PROGRAMMA/CONTENUTO

      Introduction to probability theory.

      Random variables.

      Discrete random variables: Bernoulli, Binomial, Hypergeometric, Poisson, Geometric, Negative Binomial.

      Continuous random variables: Uniform, Gaussian, Exponential, Gamma, Weibull.

      Multivariate random variables.

      Convergence theorems: Central Limit Theorem, Weak Law of Large Numbers.

      Introduction to statistical inference: sampling, induction and sampling error.

      Statistics and sampling distributions.

      Likelihood.

      Theory of point estimation.

      Properties of estimators.

      Estimation methods.

      Maximum likelihood estimators.

      Interval estimation.

      Interval estimation of the mean and variance of the Normal distribution.

      Exact intervals for the mean of Bernoulli and Poisson distributions.

      Large sample approximations for Bernoulli and Poisson distributions.

      Hypothesis testing: errors and power function.

      Neyman-Pearson Lemma. Uniformly Most Powerful tests.

      Maximum likelihood ratio tests.

      Mean comparison in independent and paired samples.

      One way ANOVA.

      Chi squared test of independence.

      TESTI/BIBLIOGRAFIA

      The same textbook is available both in English and in Italian:

      English version

      Mood AM, Graybill FA and Boes DC, Introduction to the theory of statistics, 3rd edition (available on Aulaweb).

      Italian version

      Mood AM, Graybill FA and Boes DC, Introduzione alla statistica, Mc-Graw Hill.

       

      Further readings:

      Garthwaite OH, Jolliffe IT and Jones B, Statistical Inference, 2nd Edition, Oxford Science Publications.

      Casella G and Berger RL, Statistical Inference. Duxbury

       

      Additional course materials (both in Italian and in English) will be available on AulaWeb.

      URL Aula web
      STATISTICAL MODELS
      https://2018.aulaweb.unige.it/course/view.php?id=1350
      URL Orario lezioni
      STATISTICAL MODELS
      http://diec.unige.it/orario-lezioni
  • Chi
  • Come
  • Dove e quando
  • ALTRE INFORMAZIONI
    • Risultati di apprendimento previsti

      • Conoscenza e comprensione Students will acquire a deep knowledge of the principles of statistical inference and of the main inferential tools.
      • Capacità di applicare conoscenza e comprensione At the end of the course students will be able to use the most important probabilistic models, to choose the most appropriate estimator for the parameters of a probabilistic model on the basis of its properties, to estimate means and proportions, to compare mean and variances.
      • Autonomia di giudizio Gli studenti devono saper utilizzare sia sul piano concettuale che su quello operativo le conoscenze acquisite con autonoma capacità di valutazione e con abilità nei diversi contesti applicativi.
      • Abilità comunicative Gli studenti devono acquisire il linguaggio tecnico tipico della disciplina per comunicare in modo chiaro e senza ambiguità con interlocutori specialisti e non specialisti.
      • Capacità di apprendimento This course enables students to undertake advanced courses in statistical inference and econometrics.
  • Contatti