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Course, academic year 2014/2015
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Survey Sampling - NMST438
Title: Výběrová šetření
Guaranteed by: Department of Probability and Mathematical Statistics (32-KPMS)
Faculty: Faculty of Mathematics and Physics
Actual: from 2014 to 2014
Semester: winter
E-Credits: 5
Hours per week, examination: winter s.:2/2, C+Ex [HT]
Capacity: unlimited
Min. number of students: unlimited
4EU+: no
Virtual mobility / capacity: no
State of the course: taught
Language: Czech
Teaching methods: full-time
Teaching methods: full-time
Guarantor: doc. Ing. Marek Omelka, Ph.D.
Class: M Mgr. PMSE
M Mgr. PMSE > Povinně volitelné
Classification: Mathematics > Probability and Statistics
Co-requisite : NMSA407
Incompatibility : NSTP027
Interchangeability : NSTP027
Is interchangeable with: NSTP166, NSTP027
Annotation -
Last update: T_KPMS (12.05.2014)
Basic methods of probability sampling from finite populations. Estimation of characteristics of finite populations. Applications in sampling surveys.
Aim of the course -
Last update: RNDr. Jitka Zichová, Dr. (12.04.2018)

Basic concepts and methods of finite populations sampling,

estimation of finite population parameters

and applications to sample survey.

Literature - Czech
Last update: T_KPMS (12.05.2014)

Cochran, W. G. (1977). Sampling Techniques. Wiley, New York. Third Edition

Čermák, V.: Výběrové statistické zjišťování. SNTL Praha, 1980

Särndal, C.-E., Swensson, B., and Wretman, J. (1992). Model Assisted Survey Sampling. Springer, New York.

Vorlíčková, D. (1985). Výběry z konečných souborù. Univerzita Karlova. Skripta

Teaching methods -
Last update: T_KPMS (12.05.2014)

Lecture+exercises.

Syllabus -
Last update: T_KPMS (12.05.2014)

1. Basic concepts: Population, sampling frame. population vs. sampling total and mean.

2. Simple random sampling without replacement.

3. Systematic sampling.

4. Sampling with unequal probabilities - Poisson sampling and its modifications.

5. Stratified sampling and optimal allocation.

6. Model assisted estimation - ratio and regression estimators, calibration model.

7. Cluster and two-stage sampling.

8. Nonresponse.

 
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