How Randomized Response Techniques Need not Be Confined to Simple Random Sampling but Liberally Applicable to General Sampling Schemes

2016 ◽  
pp. 17-27
Author(s):  
A. Chaudhuri
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Randomized Response ◽  
Sampling Schemes ◽  
Randomized Response Techniques

scholarly journals Bayesian Analysis of Finite Populations under Simple Random Sampling

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 318
Author(s):  
Manuel Mendoza ◽  
Alberto Contreras-Cristán ◽  
Eduardo Gutiérrez-Peña
Keyword(s):  
Bayesian Analysis ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Survey Sampling ◽  
Finite Populations ◽  
Population Means ◽  
Sampling Schemes ◽  
Nonparametric Approach ◽  
Conventional Procedure ◽  
Continuous And Discrete Variables

Statistical methods to produce inferences based on samples from finite populations have been available for at least 70 years. Topics such as Survey Sampling and Sampling Theory have become part of the mainstream of the statistical methodology. A wide variety of sampling schemes as well as estimators are now part of the statistical folklore. On the other hand, while the Bayesian approach is now a well-established paradigm with implications in almost every field of the statistical arena, there does not seem to exist a conventional procedure—able to deal with both continuous and discrete variables—that can be used as a kind of default for Bayesian survey sampling, even in the simple random sampling case. In this paper, the Bayesian analysis of samples from finite populations is discussed, its relationship with the notion of superpopulation is reviewed, and a nonparametric approach is proposed. Our proposal can produce inferences for population quantiles and similar quantities of interest in the same way as for population means and totals. Moreover, it can provide results relatively quickly, which may prove crucial in certain contexts such as the analysis of quick counts in electoral settings.

scholarly journals Maximum likelihood estimation in location-scale families using varied L ranked set sampling

2020 ◽  
Author(s):  
Amer Al-Omari
Keyword(s):  
Maximum Likelihood ◽  
Random Sampling ◽  
Maximum Likelihood Estimators ◽  
Likelihood Estimation ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Sampling Schemes ◽  
Scale Parameters ◽  
Insight Into ◽  
Family Of Distributions

Recently, a generalized ranked set sampling (RSS) scheme has been introduced which encompasses several existing RSS schemes, namely varied L RSS (VLRSS), and it provides more precise estimators of the population mean than the estimators with the traditional simple random sampling (SRS) and RSS schemes. In this paper, we extend the work and consider the maximum likelihood estimators (MLEs) of the location and scale parameters when sampling from a location-scale family of distributions. In order to give more insight into the performance of VLRSS with respect to SRS and RSS schemes, the asymptotic relative precisions of the MLEs using VLRSS relative to that using SRS and RSS are compared for some usual location-scale distributions. It turns out that the MLEs with VLRSS are more precise than those with the existing sampling schemes.

scholarly journals A Modified Forced Randomized Response Model

2020 ◽  
pp. 36-50
Author(s):  
Adefemi Adeniran ◽  
A. A. Sodipo ◽  
C. G. Udomboso
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Sampling Techniques ◽  
Response Model ◽  
Randomized Response ◽  
Numerical Illustration ◽  
Proposed Model ◽  
Efficiency Comparison ◽  
Randomized Response Model ◽  
Sensitive Group

In this paper, we proposed a new Randomized Response Model (RRM) that estimate proportion of people in a population (P) belonging to a sensitive group (S) under study. Simple random sampling with replacement and stratified simple random sampling scheme were adopted. Maximum likelihood and Bayesian estimation procedures of the proposed model were developed and compared. The sampling distribution (expectation and variance) of the proposed estimator under the two sampling techniques, efficiency comparison of the proposed model with some existing models, and numerical illustration of all the compared models were also explored. We found that the proposed model outperformed other existing RRMs in terms of efficiency and it proved to be more protective in designing survey for sensitive related issues.

On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design

2022 ◽  
pp. 42-61
Author(s):  
Agustin Santiago Moreno ◽  
Khalid Ul Islam Rather
Keyword(s):  
Empirical Evidence ◽  
Simulation Study ◽  
Random Sampling ◽  
Relative Efficiency ◽  
Convex Combination ◽  
Real Data ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Population Means ◽  
Sampling Schemes

In this chapter, the authors consider the problem of estimating the population means of two sensitive variables by making use ranked set sampling. The final estimators are unbiased and the variance expressions that they derive show that ranked set sampling is more efficient than simple random sampling. A convex combination of the variance expressions of the resultant estimators is minimized in order to suggest optimal sample sizes for both sampling schemes. The relative efficiency of the proposed estimators is then compared to the corresponding estimators for simple random sampling based on simulation study and real data applications. SAS codes utilized in the simulation to collect the empirical evidence and application are included.

Item Count Technique in Ranked Set Sampling

2022 ◽  
pp. 26-41
Author(s):  
Beatriz Cobo ◽  
Elvira Pelle
Keyword(s):  
Simulation Study ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Alternative Methods ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Sampling Without Replacement ◽  
Questioning Techniques ◽  
Item Count Technique

In situations where the estimation of the proportion of sensitive variables relies on the observations of real measurements that are difficult to obtain, there is a need to combine indirect questioning techniques. In the present work, the authors will focus on the item count technique, with alternative methods of sampling, such as the ranked set sampling. They are based on the idea proposed by Santiago et al., which combines the randomized response technique proposed by Warner together with ranked set sampling. The authors will carry out a simulation study to compare the item count technique under ranked set sampling and under simple random sampling without replacement.

Extending Takahasi‐Sakasegawa’s Indirect Response Technique to Cover Sensitive Surveys in Unequal Probability Sampling

2007 ◽  
Vol 59 (3-4) ◽  
pp. 265-276 ◽  
Author(s):  
Sanghamitra Pal
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Subject Classification ◽  
Randomized Response ◽  
Probability Sampling ◽  
Indirect Response ◽  
Variance Estimators ◽  
Unequal Probability Sampling ◽  
Unequal Probability ◽  
Sensitive Characteristics

Abstract: Randomized Response (RR) Technique (RRT), introduced by Warner (1965), is a well‐known way to unbiasedly estimate proportions of people bearing sensitive characteristics. Takahasi and Sakasegawa (1977) narrated a novel procedure avoiding any particular RR device unlike most researchers in this field. Most RRT’s in the literature give estimators for population totals and variance estimators thereof, allowing exclusively simple random sampling with replacement (SRSWR) schemes, including as well the above too. We present formulae applicable to general varying probability sampling, even without replacement applying Takahasi‐ Sakasegawa device. AMS (2000) Subject Classification: 62D05.

On Estimating Population Means of Two-Sensitive Variables With Ranked Set Sampling Design

2022 ◽  
pp. 104-140
Author(s):  
Shivacharan Rao Chitneni ◽  
Stephen A. Sedory ◽  
Sarjinder Singh
Keyword(s):  
Empirical Evidence ◽  
Simulation Study ◽  
Random Sampling ◽  
Relative Efficiency ◽  
Convex Combination ◽  
Real Data ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Population Means ◽  
Sampling Schemes

In the chapter, the authors consider the problem of estimating the population means of two sensitive variables by making use of ranked set sampling. The final estimators are unbiased and the variance expressions that they derive show that ranked set sampling is more efficient than simple random sampling. A convex combination of the variance expressions of the resultant estimators is minimized in order to suggest optimal sample sizes for both sampling schemes. The relative efficiency of the proposed estimators is then compared to the corresponding estimators for simple random sampling based on simulation study and real data applications. SAS codes utilized in the simulation to collect the empirical evidence and application are included.

scholarly journals Accuracy of Subsampling for Height Measurements in Loblolly Pine Plots

2009 ◽  
Vol 33 (3) ◽  
pp. 145-149 ◽  
Author(s):  
Colleen A. Carlson ◽  
Thomas R. Fox ◽  
Harold E. Burkhart ◽  
H. Lee Allen ◽  
Timothy J. Albaugh
Keyword(s):  
Loblolly Pine ◽  
Random Sampling ◽  
Southeastern United States ◽  
Simple Random Sampling ◽  
Stratified Sampling ◽  
The Other ◽  
Diameter Distribution ◽  
Systematic Sampling ◽  
Sampling Strategies ◽  
Sampling Schemes

Abstract Estimating heights in research and inventory plots is costly. We examined the feasibility of subsampling tree heights as opposed to measuring all trees. Four sampling intensities (75, 50, 25, and 10%) and four sampling strategies (systematic sampling, simple random sampling without replacement, stratified sampling across the diameter distribution, and sampling the first trees in each plot) were investigated. Data from 600 loblolly pine plots in fertilizer trials in the southeastern United States were used. The application of a height–dbh regression to predict the heights of unmeasured trees was also investigated. Sampling the first trees generally resulted in poorer estimates than the other sampling schemes. Systematic and simple random sampling performed similarly. A 50% sampling intensity with either systematic or simple random sampling and a height–dbh regression predicting the heights of unmeasured trees estimated more than 90% of plots to within 2.2% of the observed plot height and more than 94% of plots to within 2.5% of the observed volume, and they were more accurate than the stratified sampling at the same intensity. Systematic sampling is easy to implement, requiring no prior plot knowledge. We conclude that a 50% systematic sampling combined with a height–dbh regression will reduce costs without compromising accuracy.

ON EXTROPY PROPERTIES AND DISCRIMINATION INFORMATION OF DIFFERENT STRATIFIED SAMPLING SCHEMES

2021 ◽  
pp. 1-16
Author(s):  
Abbas Eftekharian ◽  
Guoxin Qiu
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Ranked Set Sampling ◽  
Stochastic Orders ◽  
Data Sets ◽  
Sampling Schemes ◽  
Discrimination Information ◽  
Imperfect Ranking ◽  
Stratified Ranked Set Sampling ◽  
Better Than

Ranked set sampling (RSS) and some of its variants are sampling designs that are applied widely in different areas. When the underlying population contains different subpopulations, we can use stratified ranked set sampling (SRSS) which combines the advantages of stratification with RSS. In the present paper, we consider the information content of SRSS in terms of extropy measure. Some results using stochastic orders properties are obtained. The effect of imperfect ranking on discrimination information is analytically investigated. It is proved that discrimination information between the perfect SRSS and simple random sampling (SRS) data sets performs better than that of between the imperfect SRSS and SRS data sets.

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