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CODICE 41601
ANNO ACCADEMICO 2017/2018
CFU
SETTORE SCIENTIFICO DISCIPLINARE SECS-S/01
LINGUA Inglese
SEDE
PERIODO 2° Semestre
PROPEDEUTICITA
Propedeuticità in uscita
Questo insegnamento è propedeutico per gli insegnamenti:
  • ECONOMIA E ISTITUZIONI FINANZIARIE 8700 (coorte 2017/2018)
  • FINANCIAL ECONOMETRICS 85554
MATERIALE DIDATTICO AULAWEB

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.

MODALITA' DIDATTICHE

Modalità didattiche

Classes, computer sessions.

Presente su Aulaweb

Yes   ☒ 

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.

DOCENTI E COMMISSIONI

Commissione d'esame

CORRADO LAGAZIO (Presidente)

ENRICO DI BELLA

LUCA PERSICO

LEZIONI

INIZIO LEZIONI

Sem: 2°

27 febbraio - 1 giugno 2017

Orari delle lezioni

STATISTICAL MODELS

ESAMI

MODALITA' D'ESAME

Scritto

MODALITA' DI ACCERTAMENTO

Modalità di accertamento

Esame    ☐ scritto ☐ orale   ☒   altro:

Final evaluation will be based on:

Homework:      20%

Written exam:  30%

Oral exam:       50%

Homework assignments are open also to students that do not attend the course and are not mandatory: students that do not do their homework will be evaluated only on the basis of the final exam with no penalties.

Students are free to take the exam in Italian.

Ripetizione dell’esame

The exam cannot be taken in two consecutive dates in the same session.

 

Calendario appelli

Dati Ora Luogo Tipologia Note
09/01/2018 09:00 GENOVA Scritto
06/02/2018 09:00 GENOVA Scritto
08/06/2018 09:00 GENOVA Scritto
22/06/2018 09:00 GENOVA Scritto
09/07/2018 09:00 GENOVA Scritto
23/07/2018 09:00 GENOVA Scritto
12/09/2018 09:00 GENOVA Scritto

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.