Laboratory of Structural Methods of Data Analysis in Predictive
Modeling Moscow Institute of Physics and Technology
ENG
Логин:
Пароль:

Лекция Modern Parametric Statistics

Преподаватели:
Spokoiny Vladimir
Должность: Руководитель

Описание курса

Final Exam
Place: IITP. room 404
Dates: 27-28 February 2014

 

Vladimir Spokoiny (PreMoLab MIPT)

Venue: Independent University of Moscow, Bolshoj Vasilievsky per.11., lecture room will be announced
 

Exam dates
26-28 February 2014

Course Materials:

  • Textbook: http://premolab.ru/sites/default/files/stat.pdf
  • Video: http://www.mathnet.ru/php/conference.phtml?option_lang=rus&eventID=30&confid=394

 

Application areas: 
Data mining


Research topics: 
Statistical methods
Structural adaptive inference
 

Даты проведения и расписание:
Дата Расписание

25.02.2014

Choice of the bandwidth in local parametric estimation using propagation approach.
- Sizer and Intersection-of-confidence-intervals idea and local model selection.
- Choice of tuning parameters by propagation.
- Propagation property and oracle risk bound.

24.02.2014

Local parametric approach:
- Applications to regression, generalized regression, density models.
- Examples of local constant and local linear approximation.
- Local Fisher and Wilks results.

18.02.2014

Penalized maximum likelihood and the problem of choosing the penalty:
- Fisher and Wilks for penalized maximum likelihood.
- Uniform confidence bands and concentration of the empirical risk.
- Choice of tuning parameters by propagation idea.

17.02.2014

19-00 - 20-30
Nonparametric function estimation:
- White noise and regression models, generalized regression, density models.
- Sieve approximation, modeling bias, bias-variance decomposition.
- Model selection via unbiased risk estimation.

11.02.2014

19-00 - 20-30
Few chapters of modern parametrics:
- Concentration and large deviations,
- Fisher and Wilks expansions and corollaries.

10.02.2014

19-00 - 20-30
Basics of parametric statistics: maximum likelihood approach, exponential family, linear model.
Properties of maximum likelihood, concentration and confidence sets, Gauss-Markov, Cramer-Rao and van Trees results.

Дополнительные материалы

111-3