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Jan Lanke

Bio: Jan Lanke is an academic researcher. The author has contributed to research in topics: Survey sampling. The author has an hindex of 1, co-authored 1 publications receiving 20 citations.

Papers
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Dissertation
01 Jan 1975

20 citations


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Journal ArticleDOI
TL;DR: In this article, post-stratification is studied in sampling models which result in independently and identically distributed observations, such as simple random sampling with replacement in the classical fixed unknown population vector (FOMV) setting, or simple Random Sampling without replacement in superpopulation models of independently-and identically-distributed individuals.
Abstract: Summary Post-stratification is studied in sampling models which result in independently and identically distributed observations. Examples of such models are simple random sampling with replacement in the classical fixed unknown population vector setting, or simple random sampling without replacement in superpopulation models of independently and identically distributed individuals. It is shown that post-stratified estimators are maximum likelihood and have a conditional Cramer-Rao property. They are compared to certain ratio estimators and studied asymptotically as sample size increases. Conditional inference is discussed and applications are made to opinion polls.

28 citations

Journal ArticleDOI
01 May 2011
TL;DR: In this article, the authors employ inverse sampling with equal probabilities with replacement and show certain advantages in estimation using randomized response data by Warner's device gathered through such a simple inverse sampling scheme.
Abstract: It is difficult to obtain trustworthy data on stigmatizing matters like habits of tax evasion, drunken driving, etc., through direct queries. To overcome this difficulty, Warner introduced randomized response techniques to estimate the proportion of people bearing such a stigmatizing or sensitive characteristic in a given community. For simplicity in estimation he restricted to SRSWR and since then, several researchers have extended and applied this technique in various ways. All these results involve sampling with a pre-fixed number of draws. In this paper we employ inverse sampling with equal probabilities with replacement and show certain advantages in estimation using randomized response data by Warner’s device gathered through such a simple inverse sampling scheme.

13 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed combining the sufficient bootstrap with the m/n bootstrap in order to both regain consistent estimation of sampling distributions and to reduce the computational burden of the bootstrap.
Abstract: Traditional resampling methods for estimating sampling distributions sometimes fail, and alternative approaches are then needed. For example, if the classical central limit theorem does not hold and the naive bootstrap fails, the m/n bootstrap, based on smaller-sized resamples, may be used as an alternative. An alternative to the naive bootstrap, the sufficient bootstrap, which uses only the distinct observations in a bootstrap sample, is another recently proposed bootstrap approach that has been suggested to reduce the computational burden associated with bootstrapping. It works as long as naive bootstrap does. However, if the naive bootstrap fails, so will the sufficient bootstrap. In this paper, we propose combining the sufficient bootstrap with the m/n bootstrap in order to both regain consistent estimation of sampling distributions and to reduce the computational burden of the bootstrap. We obtain necessary and sufficient conditions for asymptotic normality of the proposed method, and propose...

9 citations

Journal ArticleDOI
TL;DR: In this article, the optimality of estimators of finite population totals under various situations with single-phase sampling is demonstrated to carry over to double-sampling with suitable formulations, and certain well-known results concerning nonexistence of UMV (uniformly minimum variance) estimators are demonstrated.

6 citations

Journal ArticleDOI
TL;DR: In this article, an alternative estimator for the probability proportional to size with replacement sampling scheme when certain characteristics under study have low correlation with the size measured used for sample selection has been developed.
Abstract: We developed an alternative estimator for the probability proportional to size with replacement sampling scheme when certain characteristics under study have low correlation with the size measured used for sample selection. The performance of the proposed estimator has been studied with other related alternative estimators by comparing biases and the variances of respective alternative estimators. Most of the alternative estimators assume the knowledge of the product moment correlation coefficient. Therefore an empirical study, with the help of wide variety of populations, has been carried out to study their respective efficiency when correlation coefficient is departed from its true value.

5 citations