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Basic methods of probability sampling from finite populations. Estimation of
characteristics of finite populations. Applications in sampling surveys.
Last update: G_M (10.10.2001)
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Basic concepts and methods of finite populations sampling, estimation of finite population parameters and applications to sample survey. Last update: G_M (30.05.2008)
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Čermák, V.: Výběrové statistické zjišťování. SNTL Praha, 1980
Hájek, J.: Teorie pravděpodobnostního výběru s aplikacemi na výběrová šetření. ČSAV Praha, 1960
Hájek, J.: Sampling from a Finite Population. M.Dekker, New York, 1981 Last update: Zakouřil Pavel, RNDr., Ph.D. (05.08.2002)
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Lecture+exercises. Last update: G_M (27.05.2008)
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1. Sampling surveys, their goals and steps of their praparation. Application of mathematical theory in individual steps.
2. Population, sampling frame. Population quantities: population unit, characteristics of a unit (quantitative and qualitative), total and average of a characteristic, measures of variability of a characteristic (variance, variation coefficient), measures of dependence of two characteristics, quantiles.
3. Sampling design, quote sampling. Probability sampling: inclusion indicators, their moments and covariances, probabilities of inclusion, fixed and random sample size.
4. Horwitz-Thompson estimator of the population total, its moments for samples with random or fixed sizes. Confidence interval for the population total.
5. Linear estimates of the total., unbiasedness, equivariance with respect to the shift and scale. Horwitz-Thompson estimate, Hájek's estimate.
6. Simple random sampling with replacement and without replacement, their comparison.
7. Poisson sampling, entropy of sampling design, optimality of Poisson sampling.
8. Rejective Poisson sampling and rejective sampling wit replacement, their relations and probabilities of rejection. The Sampford modification of rejective sampling.
9. Succesive sampling, systematic sampling, multiple sampling, stratified sampling.
10. Estimation of total using an auxiliary information: regression estimate, differention and ratio correction of the linear estimate. Ratio estimate, its bias and mean square error.
11. Stratification, proportional and optimal allocations and their conmparisons.
Last update: T_KPMS (09.05.2003)
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