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 A modified class of exponential-type estimator of population-mean in simple random sampling

2017 ◽  
Vol 5 (2) ◽  
pp. 70
Author(s):  
Ekaette Enang ◽  
Joy Uket ◽  
Emmanuel Ekpenyong
Keyword(s):  
Optimality Conditions ◽  
Exponential Type ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Survey Sampling ◽  
Numerical Comparison ◽  
Mean Square ◽  
Population Means ◽  
Class Of Estimators ◽  
Ratio Estimators

The problem of obtaining better ratio estimators of the population means are dominating in survey sampling. This paper provides a modified class of exponential type estimators using combinations of some existing estimators. Expressions for the bias and Mean Square Error (MSE) with the optimality conditions for this class of estimators have been established. Both analytical and numerical comparison with some existing estimators shows better performances from members of the proposed class.

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.

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.

Analyses of Data from Complex Survey Sampling

2018 ◽  
Author(s):  
Peter Miksza ◽  
Kenneth Elpus
Keyword(s):  
Survey Data ◽  
Random Sampling ◽  
Sampling Methods ◽  
Simple Random Sampling ◽  
Survey Sampling ◽  
Cluster Sampling ◽  
Complex Survey ◽  
Complex Survey Data ◽  
Survey Sample ◽  
Proper Analysis

This chapter introduces the specialized techniques necessary for analyzing data that have been gathered in a complex or multistage survey sample. The chapter details the methods most commonly used to collect complex survey data and then explains the specific statistical tools that must be employed to correctly analyze complex survey data. First, an overview of the various types of sampling methods is presented, beginning with simple random sampling and moving through other methods to finally discuss the commonly employed research techniques of cluster sampling. The chapter continues with a discussion of survey weights—what they mean and how they are derived. The chapter concludes with software-based suggestions on the proper analysis of survey data.

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 Bias Correction Technique for Estimating Quantiles of Finite Populations under Simple Random Sampling without Replacement

2021 ◽  
Vol 11 (05) ◽  
pp. 854-869
Author(s):  
Nicholas Makumi ◽  
Romanus Odhiambo Otieno ◽  
George Otieno Orwa ◽  
Festus Were ◽  
Habineza Alexis
Keyword(s):  
Bias Correction ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Finite Populations ◽  
Sampling Without Replacement ◽  
Correction Technique

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.

Peran Penilaian Formatif terhadap Motivasi Belajar Mahasiswa, Sebuah Tinjauan Pustaka

2014 ◽  
Vol 5 (1) ◽  
pp. 56
Author(s):  
Putri Zalika Laila M.K
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Cross Sectional

Penyakit Jantung Koroner (PJK) adalah sekelompok sindrom yang berkaitan erat yang disebabkan oleh ketidakseimbangan antara kebutuhan oksigen miokardium dan aliran darah. Pada umumnya faktor risiko terjadinya penyakit jantung koroner adalah hipertensi. Penelitian ini bertujuan untuk menentukan hubungan tekanan darah dengan kejadian penyakit jantung koroner di Rumah Sakit Muhammadiyah Palembang dan Rumah Sakit Umum Daerah Palembang BARI periode Januari-Desember 2012. Penelitian ini menggunakan metode deskriptif analitik dengan rancangan cross sectional di bagian ilmu penyakit dalam Rumah Sakit Umum Daerah Palembang BARI dan Rumah Sakit Muhammadiyah Palembang dengan cara pengambilan sampel yaitu simple random sampling. Dari 200 subjek penelitian, penyakit jantung yang mempunyai hipertensi sebanyak 100 dan yang tidak hipertensi sebanyak 100. Hasil analisis didapatkan jumlah pada subjek hipertensi yang terkena penyakit jantung koroner sebesar 64(64%) sedangkan pada non hipertensi yang terkena penyakit jantung koroner didapatkan sebanyak 32(32%). Rasio prevalensi didapatkan adalah 2,00 dengan interval kepercayaan 95% antara 1,450-2,758. Hasil analisis chi-squeare didapatkan nilai X2 didapatkan hasil 19,251 dan nilai p: 0,000 yang artinya ada hubungan faktor risiko antara hipertensi dengan penyakit jantung koroner dengan taraf significant sangat bermakna. Hipertensi merupakan faktor risiko untuk terjadinya penyakit jantung koroner, penderita hipertensi berisiko 2 kali lebih besar terkena penyakit jantung koroner.

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