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Asymptotic Inference Methods - NMST533

Title:	Asymptotické metody inference
Guaranteed by:	Department of Probability and Mathematical Statistics (32-KPMS)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2024
Semester:	winter
E-Credits:	3
Hours per week, examination:	winter s.:2/0, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	not taught
Language:	Czech
Teaching methods:	full-time

Guarantor:	prof. RNDr. Marie Hušková, DrSc.
Class:	M Mgr. PMSE M Mgr. PMSE > Povinně volitelné
Classification:	Mathematics > Probability and Statistics
Pre-requisite :	NMST424

Annotation -

The course concerns statistical inference (tests and estimators) based on limit theorems (central limit theorems, laws of large numbers).

Last update: T_KPMS (13.05.2014)

Aim of the course -

To give information on some statistical procedures based on

probability limit theorems.

Last update: T_KPMS (13.05.2014)

Literature -

E.L.Lehmann: Elements of Large sample theory, Springer 1999.

P.K. Sen a J.M. Singer: Large sample methods in Statistics: An

Introduction With Applications. Chapman & Hall, 1993.

Some recent papers on the topic.

Last update: Omelka Marek, doc. Ing., Ph.D. (21.06.2022)

Teaching methods -

Lecture.

Last update: T_KPMS (02.06.2016)

Syllabus -

Survey of needed probability limit theorems (central limit theorems, laws

of large numbers ),

tests on means, variances and further statistical characteristics based

on limit theorems both for independent as well as dependent observations,

asymptotic powers of tests. Local alternatives, some goodness-of-fit tets

with nuisance parameters, some sequential tests and sequential

estimators eventually other procedures

Last update: T_KPMS (13.05.2014)