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2021
In case of simple random sampling without replacement, the sampling variance of the sample mean is \(V(\bar{y}_n)=\left( \frac{1}{n}-\frac{1}{N}\right) S^{2}_{y}\).
Raosaheb Latpate +3 more
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In case of simple random sampling without replacement, the sampling variance of the sample mean is \(V(\bar{y}_n)=\left( \frac{1}{n}-\frac{1}{N}\right) S^{2}_{y}\).
Raosaheb Latpate +3 more
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Restricted stratified random sampling
International Journal of Mineral Processing, 1989Abstract A method of sampling is described which is a compromise between systematic sampling and stratified random sampling. It has less potential for bias than systematic sampling and also avoids the practical problems associated with stratified random sampling.
Ian W. Saunders, Geoffrey K. Robinson
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On stratified randomized response sampling
Model Assisted Statistics and Applications, 2005In this paper, we propose a new quantitative randomized response model based on Mangat and Singh [7] two-stage randomized response model. We derive the estimator of the sensitive variable mean, and show that our method is more efficient than other randomized response models suggested by Greenberg et al. [3] and Gupta et al. [4] estimators.
Ryu, Jea-Bok +3 more
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Randomized response in stratified sampling
Journal of Statistical Planning and Inference, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christofides, Tasos C. +1 more
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Ratio estimators using stratified random sampling and stratified ranked set sampling
Life Cycle Reliability and Safety Engineering, 2018The aim of present study is to propose ratio estimators for the population mean using auxiliary information efficiently under stratified random sampling (SRS) and stratified ranked set sampling (SRSS). Here, bias and mean square error (MSE) for the proposed estimators have been obtained and find that the proposed estimator under SRSS is more efficient ...
Monika Saini, Ashish Kumar
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Calibration Weighting in Stratified Random Sampling
Communications in Statistics - Simulation and Computation, 2014A new calibration estimator is proposed to estimate the population mean in the stratified random sampling. The corrected expression of Tracy et al. (2003) calibrated weights are presented and new improved calibration weights are introduced. Theoretical variance of the suggested estimator is discussed.
Nursel Koyuncu, Cem Kadilar
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Ratio Estimators in Stratified Random Sampling
Biometrical Journal, 2003AbstractThis paper considers some ratio‐type estimators and their properties are studied in stratified random sampling. The results are supported by an application with original data.
C. Kadilar, H. Cingi
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1986
In forestry practice we often have to deal with populations that can be split up into various sub-populations that in some respect or other are mutually different. Each subpopulation (called stratum, plural strata) in itself then is more homogeneous than the population as a whole. Generally, we wish to obtain information for each stratum separately.
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In forestry practice we often have to deal with populations that can be split up into various sub-populations that in some respect or other are mutually different. Each subpopulation (called stratum, plural strata) in itself then is more homogeneous than the population as a whole. Generally, we wish to obtain information for each stratum separately.
openaire +1 more source