scholarly journals Approximation by Normal Distribution for a Sample Sum in Sampling Without Replacement from a Finite Population

Sankhya A ◽  
2016 ◽  
Vol 78 (2) ◽  
pp. 188-220
Author(s):  
Ibrahim Bin Mohamed ◽  
Sherzod M. Mirakhmedov
Keyword(s):  
Normal Distribution ◽  
Finite Population ◽  
Sampling Without Replacement ◽  
Sample Sum

Simultaneous Estimation of Proportions in a Finite Population

1993 ◽  
Vol 43 (1-2) ◽  
pp. 65-74
Author(s):  
N. Mukhopadhyay ◽  
S. Chattopadhyay
Keyword(s):  
Random Sampling ◽  
Finite Population ◽  
Simple Random Sampling ◽  
Subject Classification ◽  
Simultaneous Estimation ◽  
Sampling Strategies ◽  
Sampling Without Replacement ◽  
First Order ◽  
Multistage Sampling

Sequential and multistage sampling strategies via simple random sampling without replacement, are proposed for simultaneously estimating several proportions in a finite population. Various asymptotic first-order properties are addressed, while some limited moderate sample performance have also been included. AMS (1980) Subject Classification: Primary 62L99; Secondary 62L12

scholarly journals Sampling Without Replacement: Approximation to the Probability Distribution

1988 ◽  
Vol 44 (2) ◽  
pp. 197-213 ◽  
Author(s):  
J. N. Darroch ◽  
M. Jirina ◽  
T. P. Speed
Keyword(s):  
Probability Distribution ◽  
Linear Combination ◽  
Finite Population ◽  
Uniform Norm ◽  
Sample Space ◽  
Sampling Without Replacement ◽  
Uniform Probability ◽  
Product Measures

AbstractLet P be the probability distribution of a sample without replacement of size n from a finite population represented by the set N={1,2,…N}. For each r=0, 1, …, an approximation Pr is described such that the uniform norm ‖P − Pr‖ is of order (n2/N)r+1 if n2/N→0. The approximation Pr is a linear combination of uniform probability product-measures concentrated on certain subspaces of the sample space Nn.

The Negative Hypergeometric Probability Distribution: Sampling without Replacement from a Finite Population

1998 ◽  
Vol 86 (1) ◽  
pp. 207-210 ◽  
Author(s):  
Kenneth J. Berry ◽  
Paul W. Mielke
Keyword(s):  
Probability Distribution ◽  
Sample Size ◽  
Finite Population ◽  
Fortran Program ◽  
Minimum Sample Size ◽  
Sampling Without Replacement ◽  
Minimum Sample

The negative hypergeometric probability distribution is defined and its relationship to the inverse hypergeometric probability distribution is clarified. A FORTRAN program is described which computes negative hypergeometric probability values and, for a specified probability, the minimum sample size needed to attain a given number of successes.

scholarly journals Estimation of the current population total for a four-phase sampling design

2016 ◽  
Vol 21 (2) ◽  
pp. 241-260
Author(s):  
Viktoras Chadyšas ◽  
Danutė Krapavickaitė
Keyword(s):  
Sampling Design ◽  
Finite Population ◽  
Auxiliary Information ◽  
Real Data ◽  
Simple Random Sampling ◽  
Two Phase ◽  
Successive Sampling ◽  
Population Total ◽  
Sampling Without Replacement ◽  
Phase Sampling

The combined ratio-type estimators of the finite population total and their variances in the case of sample rotation for two-phase and four-phase sampling schemes are constructed in the paper. Combined estimators of the finite population total without and with the use of auxiliary information known from the previous survey are built. Two types of sampling design are used for sample selection in each of the phases: simple random sampling without replacement and successive sampling without replacement with probabilities proportional to size. A simulation study, based on the real data, is performed, and the accuracy of the estimators proposed is compared.

scholarly journals Bayesian Inference of Finite Population Quantiles for Skewed Survey Data Using Skew-Normal Penalized Spline Regression

2019 ◽  
Vol 8 (4) ◽  
pp. 792-816
Author(s):  
Yutao Liu ◽  
Qixuan Chen
Keyword(s):  
Normal Distribution ◽  
Survey Data ◽  
Finite Population ◽  
Location Parameter ◽  
Penalized Splines ◽  
Sample Survey ◽  
Skew Normal Distribution ◽  
Scale Parameters ◽  
Penalized Spline ◽  
Skew Normal

Abstract Skewed data are common in sample surveys. In probability proportional to size sampling, we propose two Bayesian model-based predictive methods for estimating finite population quantiles with skewed sample survey data. We assume the survey outcome to follow a skew-normal distribution given the probability of selection and model the location and scale parameters of the skew-normal distribution as functions of the probability of selection. To allow a flexible association between the survey outcome and the probability of selection, the first method models the location parameter with a penalized spline and the scale parameter with a polynomial function, while the second method models both the location and scale parameters with penalized splines. Using a fully Bayesian approach, we obtain the posterior predictive distributions of the nonsampled units in the population and thus the posterior distributions of the finite population quantiles. We show through simulations that our proposed methods are more efficient and yield shorter credible intervals with better coverage rates than the conventional weighted method in estimating finite population quantiles. We demonstrate the application of our proposed methods using data from the 2013 National Drug Abuse Treatment System Survey.

scholarly journals Modified Ratio-Cum-Product Estimators of Population Mean Using Two Auxiliary Variables

2020 ◽  
pp. 55-65
Author(s):  
J. O. Muili ◽  
E. N. Agwamba ◽  
Y. A. Erinola ◽  
M. A. Yunusa ◽  
A. Audu ◽  
...  
Keyword(s):  
Random Sampling ◽  
Finite Population ◽  
Simple Random Sampling ◽  
Degree Of Approximation ◽  
Ratio Estimator ◽  
Auxiliary Variables ◽  
Sampling Without Replacement ◽  
Sample Mean ◽  
Population Mean ◽  
Product Estimator

A percentile is one of the measures of location used by statisticians showing the value below which a given percentage of observations in a group of observations fall. A family of ratio-cum-product estimators for estimating the finite population mean of the study variable when the finite population mean of two auxiliary variables are known in simple random sampling without replacement (SRSWOR) have been proposed. The main purpose of this study is to develop new ratio-cum-product estimators in order to improve the precision of estimation of population mean in sample random sampling without replacement using information of percentiles with two auxiliary variables. The expressions of the bias and mean square error (MSE) of the proposed estimators were derived by Taylor series method up to first degree of approximation. The efficiency conditions under which the proposed ratio-cum-product estimators are better than sample man, ratio estimator, product estimator and other estimators considered in this study have been established. The numerical and empirical results show that the proposed estimators are more efficient than the sample mean, ratio estimator, product estimator and other existing estimators.

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