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On Conditional Simple Random Sample

100%
Wywiał J. L.
Statistics in Transition new series
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2014
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vol. 15
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issue 4
525–534
EN
Estimation of the population average in a finite and fixed population on the basis of the conditional simple random sampling design dependent on order statistics of the auxiliary variable is studied. The sampling scheme implementing the sampling design is proposed. The inclusion probabilities are derived. The well known Horvitz-Thompson statistic under the conditional simple random sampling designs is considered as the estimator of population mean. Moreover, it was shown that the Horvitz-Thompson estimator under some particular cases of the conditional simple random sampling design is more accurate than the ordinary mean from the simple random sample.
2
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Estimation of Mean on The Basis of Conditional Simple Random Sample

84%
Wywiał J. L.
Statistics in Transition new series
|
2016
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vol. 17
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issue 3
411-428
EN
Estimation of the population mean in a finite and fixed population on the basis of the conditional simple random sampling design dependent on order statistics (quantiles) of an auxiliary variable is considered. Properties of the well-known Horvitz-Thompson and ratio type estimators as well as the sample mean are taken into account under the conditional simple random sampling designs. The considered examples of empirical analysis lead to the conclusion that under some additional conditions the proposed estimation strategies based on the conditional simple random sample are usually more accurate than the mean from the simple random sample drawn without replacement.
3
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Sampling Designs Proportionate to Sum of Two Order Statistics of Auxiliary Variable

84%
Wywiał J. L.
Statistics in Transition new series
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2013
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vol. 14
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issue 2
231-248
EN
In this paper the case of a conditional sampling design proportional to the sum of two order statistics is considered. Several strategies including the Horvitz-Thompson estimator and ratio-type estimators are discussed. The accuracy of these estimators is analyzed on the basis of computer simulation experiments.
4
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A Stopping Rule for Simulation‑Based Estimation of Inclusion Probabilities

67%
Gamrot W.
Acta Universitatis Lodziensis. Folia Oeconomica
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2020
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vol. 4
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issue 349
67-80
PL
Estymacja parametrów populacji skończonych i ustalonych, prowadzona w ramach podejścia randomizacyjnego, zazwyczaj wymaga znajomości prawdopodobieństw inkluzji charakteryzujących schemat losowania próby. Są one bezpośrednio wykorzystywane w celu wyznaczenia wag przypisanych poszczególnym wylosowanym jednostkom i uwzględniane podczas obliczania estymatorów. Jednak dla pewnych użytecznych schematów losowania pozostają nieznane. W takim wypadku możliwe jest ich wyznaczenie na drodze symulacyjnej, poprzez wielokrotne losowanie prób z wykorzystaniem tego samego schematu losowania i zliczanie wystąpień poszczególnych jednostek populacji. Zastępując nieznane prawdopodobieństwa inkluzji oszacowaniami uzyskanymi w wyniku takiego eksperymentu, otrzymuje się oszacowania wartości globalnej badanej cechy populacji. Szczególnym wyzwaniem podczas takiego postępowania jest wyznaczenie liczby replikacji próby, zapewniającej wymaganą precyzję estymacji. W niniejszym artykule proponowana jest nowa procedura, która może przyczynić się do zmniejszenia złożoności obliczeniowej eksperymentu symulacyjnego.
EN
Design‑based estimation of finite population parameters such as totals usually relies on the knowledge of inclusion probabilities characterising the sampling design. They are directly incorporated into sampling weights and estimators. However, for some useful sampling designs, these probabilities may remain unknown. In such a case, they may often be estimated in a simulation experiment which is carried out by repeatedly generating samples using the same sampling scheme and counting occurrences of individual units. By replacing unknown inclusion probabilities with such estimates, design‑based population total estimates may be computed. The calculation of required sample replication numbers remains an important challenge in such an approach. In this paper, a new procedure is proposed that might lead to the reduction in computational complexity of simulations.
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