scholarly journals Estimating a Finite Population Mean Using Transformed Data in Presence of Random Nonresponse

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nelson Kiprono Bii ◽  
Christopher Ouma Onyango ◽  
John Odhiambo
Keyword(s):  
Finite Population ◽  
Mean Squared Error ◽  
Simulated Data ◽  
Sampling Technique ◽  
Theory And Practice ◽  
Sample Survey ◽  
Population Mean ◽  
Data Weighting ◽  
Asymptotic Mean Squared Error ◽  
Mean Variance

Developing finite population estimators of parameters such as mean, variance, and asymptotic mean squared error has been one of the core objectives of sample survey theory and practice. Sample survey practitioners need to assess the properties of these estimators so that better ones can be adopted. In survey sampling, the occurrence of nonresponse affects inference and optimality of the estimators of finite population parameters. It introduces bias and may cause samples to deviate from the distributions obtained by the original sampling technique. To compensate for random nonresponse, imputation methods have been proposed by various researchers. However, the asymptotic bias and variance of the finite population mean estimators are still high under this technique. In this paper, transformation of data weighting technique is suggested. The proposed estimator is observed to be asymptotically consistent under mild assumptions. Simulated data show that the estimator proposed is much better than its rival estimators for all the different mean functions simulated.

scholarly journals A generalized exponential-type estimator for population mean using auxiliary attributes

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0246947
Author(s):  
Sohail Ahmad ◽  
Muhammad Arslan ◽  
Aamna Khan ◽  
Javid Shabbir
Keyword(s):  
Exponential Type ◽  
Random Sampling ◽  
Finite Population ◽  
Mean Squared Error ◽  
Simple Random Sampling ◽  
Stratified Random Sampling ◽  
Population Mean ◽  
First Order ◽  
Squared Error ◽  
Class Of Estimators

In this paper, we propose a generalized class of exponential type estimators for estimating the finite population mean using two auxiliary attributes under simple random sampling and stratified random sampling. The bias and mean squared error (MSE) of the proposed class of estimators are derived up to first order of approximation. Both empirical study and theoretical comparisons are discussed. Four populations are used to support the theoretical findings. It is observed that the proposed class of estimators perform better as compared to all other considered estimator in simple and stratified random sampling.

scholarly journals Estimation of Finite Population Mean in Multivariate Stratified Sampling under Cost Function Using Goal Programming

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Atta Ullah ◽  
Javid Shabbir ◽  
Zawar Hussain ◽  
Bander Al-Zahrani
Keyword(s):  
Cost Function ◽  
Sample Size ◽  
Goal Programming ◽  
Random Sampling ◽  
Finite Population ◽  
Sample Survey ◽  
Programming Approach ◽  
Stratified Random Sampling ◽  
Practical Utilization ◽  
Population Mean

In practical utilization of stratified random sampling scheme, the investigator meets a problem to select a sample that maximizes the precision of a finite population mean under cost constraint. An allocation of sample size becomes complicated when more than one characteristic is observed from each selected unit in a sample. In many real life situations, a linear cost function of a sample sizenhis not a good approximation to actual cost of sample survey when traveling cost between selected units in a stratum is significant. In this paper, sample allocation problem in multivariate stratified random sampling with proposed cost function is formulated in integer nonlinear multiobjective mathematical programming. A solution procedure is proposed using extended lexicographic goal programming approach. A numerical example is presented to illustrate the computational details and to compare the efficiency of proposed compromise allocation.

scholarly journals Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Nelson Kiprono Bii ◽  
Christopher Ouma Onyango ◽  
John Odhiambo
Keyword(s):  
Finite Population ◽  
Mean Squared Error ◽  
Auxiliary Information ◽  
Cluster Sampling ◽  
Sample Surveys ◽  
Population Mean ◽  
Second Stage ◽  
Kernel Weights ◽  
Squared Error ◽  
Interval Lengths

Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys.

scholarly journals Ratio in Ratio Type Exponential Strategy for the Estimation of Population Mean

2021 ◽  
Author(s):  
Anurag Gupta ◽  
Rajesh Tailor
Keyword(s):  
Unbiased Estimator ◽  
Finite Population ◽  
Mean Squared Error ◽  
Double Sampling ◽  
Ratio Estimator ◽  
Population Mean ◽  
Squared Error ◽  
Double Sampling For Stratification

This paper is an attempt to develop an estimator for finite population mean. Motivated by Kiregyera (1984), a ratio in ratio type exponential strategy is developed for estimation of population mean in double sampling for stratification. To compare with relevant considered estimators, expressions for bias and mean squared error of the developed estimator have been derived. The developed estimator has been compared with usual unbiased estimator, Ige and Tripathi (1987), ratio estimator and ratio type exponential estimator given by Tailor et al (2014) theoretically as well as empirically.

scholarly journals Threshold selection in univariate extreme value analysis

Extremes ◽  
2021 ◽  
Author(s):  
Laura Fee Schneider ◽  
Andrea Krajina ◽  
Tatyana Krivobokova
Keyword(s):  
Mean Squared Error ◽  
Extreme Value ◽  
Hill Estimator ◽  
Small Samples ◽  
Extreme Value Analysis ◽  
Threshold Selection ◽  
Value Analysis ◽  
Wide Range ◽  
Asymptotic Mean Squared Error ◽  
The Hill

AbstractThreshold selection plays a key role in various aspects of statistical inference of rare events. In this work, two new threshold selection methods are introduced. The first approach measures the fit of the exponential approximation above a threshold and achieves good performance in small samples. The second method smoothly estimates the asymptotic mean squared error of the Hill estimator and performs consistently well over a wide range of processes. Both methods are analyzed theoretically, compared to existing procedures in an extensive simulation study and applied to a dataset of financial losses, where the underlying extreme value index is assumed to vary over time.

scholarly journals An Optimal Tail Selection in Risk Measurement

Risks ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 70
Author(s):  
Małgorzata Just ◽  
Krzysztof Echaust
Keyword(s):  
Mean Squared Error ◽  
Real Data ◽  
Random Variable ◽  
Threshold Level ◽  
Middle Part ◽  
Risk Measurement ◽  
Practical Applications ◽  
Tuning Parameters ◽  
Path Stability ◽  
Asymptotic Mean Squared Error

The appropriate choice of a threshold level, which separates the tails of the probability distribution of a random variable from its middle part, is considered to be a very complex and challenging task. This paper provides an empirical study on various methods of the optimal tail selection in risk measurement. The results indicate which method may be useful in practice for investors and financial and regulatory institutions. Some methods that perform well in simulation studies, based on theoretical distributions, may not perform well when real data are in use. We analyze twelve methods with different parameters for forty-eight world indices using returns from the period of 2000–Q1 2020 and four sub-periods. The research objective is to compare the methods and to identify those which can be recognized as useful in risk measurement. The results suggest that only four tail selection methods, i.e., the Path Stability algorithm, the minimization of the Asymptotic Mean Squared Error approach, the automated Eyeball method with carefully selected tuning parameters and the Hall single bootstrap procedure may be useful in practical applications.

PENGARUH KOMPENSASI DAN KOMITMEN ORGANISASIONAL TERHADAP TURNOVER INTENTION PENDIDIK KELOMPOK BERMAIN PADA LEMBAGA PENDIDIKAN ANAK USIA DINI (PAUD) DI KOTA MATARAM

2015 ◽  
Vol 4 (4) ◽  
Author(s):  
Hendra Yuliana
Keyword(s):  
Early Childhood ◽  
Organizational Commitment ◽  
Linear Regression ◽  
Turnover Intention ◽  
Sampling Technique ◽  
Total Sample ◽  
Sample Survey ◽  
Random Probability ◽  
Play Group ◽  
Dominant Influence

This study was conducted to determine the effect of compensation and organizationalcommitment to turnover intention Educators Play Group at the Institute for Early ChildhoodEducation ( ECD ) in Mataram . The method of data collection is a sample survey and the populationin this study were all early childhood educators in play group in Mataram . The sample wasdetermined using random probability sampling technique with a total sample is 85 people . Theanalytical tool used to process the data and test the hypothesis is multiple linear regression .The results showed that compensation has a significant influence on turnover intention aswell as the commitment of the organization to have a significant effect on turnover intention . Effectof compensation and significant organizational commitment on the partial test or simultaneously .Compensation is more variable than the dominant influence of organizational commitment .Keywords : Compensation , Organizational Commitment , Turnover Intention

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