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NONPARAMETRIC MIXED RATIO ESTIMATOR FOR A FINITE POPULATION TOTAL IN STRATIFIED SAMPLING  [cached]
George Otieno Orwa,Romanus Odhiambo Otieno,Peter Nyamuhanga Mwita
Pakistan Journal of Statistics and Operation Research , 2010, DOI: 10.1234/pjsor.v6i1.149
Abstract: We propose a nonparametric regression approach to the estimation of a finite population total in model based frameworks in the case of stratified sampling. Similar work has been done, by Nadaraya and Watson (1964), Hansen et al (1983), and Breidt and Opsomer (2000). Our point of departure from these works is at selection of the sampling weights within every stratum, where we treat the individual strata as compact Abelian groups and demonstrate that the resulting proposed estimator is easier to compute. We also make use of mixed ratios but this time not in the contexts of simple random sampling or two stage cluster sampling, but in stratified sampling schemes, where a void still exists.
An improved estimator for population mean using auxiliary information in stratified random sampling  [PDF]
Sachin Malik,Viplav Kumar Singh,Rajesh Singh
Statistics , 2014,
Abstract: In the present study, we propose a new estimator for population mean of the study variable y in the case of stratified random sampling using the information based on auxiliary variable x. Expression for the mean squared error (MSE) of the proposed estimators is derived up to the first order of approximation. The theoretical conditions have also been verified by a numerical example. An empirical study is carried out to show the efficiency of the suggested estimator over sample mean estimator, usual separate ratio, separate product estimator and other proposed estimators.
Multivariate Ratio Estimator of the Population Total under Stratified Random Sampling  [PDF]
Oscar O. Ngesa, G. O. Orwa, R. O. Otieno, H. M. Murray
Open Journal of Statistics (OJS) , 2012, DOI: 10.4236/ojs.2012.23036
Abstract: Olkin [1] proposed a ratio estimator considering p auxiliary variables under simple random sampling. As is expected, Simple Random Sampling comes with relatively low levels of precision especially with regard to the fact that its variance is greatest amongst all the sampling schemes. We extend this to stratified random sampling and we consider a case where the strata have varying weights. We have proposed a Multivariate Ratio Estimator for the population mean in the presence of two auxiliary variables under Stratified Random Sampling with L strata. Based on an empirical study with simulations in R statistical software, the proposed estimator was found to have a smaller bias as compared to Olkin’s estimator.
IMPROVED EXPONENTIAL ESTIMATOR IN STRATIFIED RANDOM SAMPLING
Rajesh Singh
Pakistan Journal of Statistics and Operation Research , 2010, DOI: 10.1234/pjsor.v5i2.118
Abstract: In this article we have considered the problem of estimating the population mean in the stratified random sampling using the information of an auxiliary variable x which is correlated with y and suggested improved exponential ratio estimators in the stratified random sampling. The mean square error (MSE) equations for the proposed estimators have been derived and it is shown that the proposed estimators under optimum condition performs better than estimators suggested by Singh et al. (2008). Theoretical and empirical findings are encouraging and support the soundness of the proposed estimators for mean estimation.
Dual To Ratio Cum Product Estimator In Stratified Random Sampling  [PDF]
Rajesh Singh,Mukesh Kumar,Manoj K. Chaudhary,Cem Kadilar
Statistics , 2013,
Abstract: Tracy et al.[8] have introduced a family of estimators using Srivenkataramana and Tracy ([6],[7]) transformation in simple random sampling. In this article, we have proposed a dual to ratio-cum-product estimator in stratified random sampling. The expressions of the mean square error of the proposed estimators are derived. Also, the theoretical findings are supported by a numerical example.
A New Estimator Using Auxiliary Information in Stratified Adaptive Cluster Sampling  [PDF]
Nipaporn Chutiman, Monchaya Chiangpradit, Sujitta Suraphee
Open Journal of Statistics (OJS) , 2013, DOI: 10.4236/ojs.2013.34032
Abstract: In this paper, we study the estimators of the population mean in stratified adaptive cluster sampling by using the information of the auxiliary variable. Simulations showed that if the variable of interest (y) and the auxiliary variables (x,z) have high positive correlation then the estimate of the mean square error of the ratio estimators is less than the estimate of the mean square error of the product estimator. The estimators which use only one auxiliary variable were better than the estimators which use two auxiliary variables.
A New Estimator For Population Mean Using Two Auxiliary Variables in Stratified random Sampling  [PDF]
Rajesh Singh,Sachin Malik
Statistics , 2014,
Abstract: In this paper, we suggest an estimator using two auxiliary variables in stratified random sampling. The propose estimator has an improvement over mean per unit estimator as well as some other considered estimators. Expressions for bias and MSE of the estimator are derived up to first degree of approximation. Moreover, these theoretical findings are supported by a numerical example with original data. Key words: Study variable, auxiliary variable, stratified random sampling, bias and mean squared error.
Improved estimator of finite population mean using auxiliary attribute in stratified random sampling  [PDF]
Hemant K. Verma,Prayas Sharma,Rajesh Singh
Statistics , 2014,
Abstract: The present study discuss the problem of estimating the finite population mean using auxiliary attribute in stratified random sampling. In this paper taking the advantage of point bi-serial correlation between the study variable and auxiliary attribute, we have improved the estimation of population mean in stratified random sampling. The expressions for Bias and Mean square error have been derived under stratified random sampling. In addition, an empirical study has been carried out to examine the merits of the proposed estimator over the existing estimators.
A New Regression Type Estimator with Two Auxiliary Variables for Single-Phase Sampling  [PDF]
Everline Chemutai Tum, John Kung’u, Leo Odongo
Open Journal of Statistics (OJS) , 2014, DOI: 10.4236/ojs.2014.49074
Abstract: In this paper, we have proposed an estimator of finite population mean using a new regression type estimator with two auxiliary variables for single-phase sampling and investigated its finite sample properties. An empirical study has been carried out to compare the performance of the proposed estimator with the existing estimators that utilize auxiliary variables for finite population mean. It has been found that the new regression type estimator with two auxiliary variables for to be more efficient than mean per unit, ratio and product estimator and exponential ratio and exponential product estimators and exponential ratio-product estimator.
Polynomial (Non Linear) Regression Method for Improved Estimation Based on Sampling  [PDF]
Emmanuel John Ekpenyong,Mfon Ime Okonnah,Eno Donatus John
Journal of Applied Sciences , 2008,
Abstract: We seek to proffer estimates of the parameters of the dependent variable having a polynomial relationship with the independent variable, specifically that of order two. The variances of the mean and total estimates of the dependent variable are derived. The efficiency and precision of the method are shown by comparing its estimates with those of linear regression and elemental sampling methods through the use of sampled data by simple random sampling without replacement from simulated data.
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