Author: Prayas Sharma
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 18
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Book Description
Chakrabarty (1979), Khoshnevisan et al. (2007), Sahai and Ray (1980), Ismail et al. (2011) and Solanki et al. (2012) proposed estimators for estimating population mean Y. Up to the first order of approximation and under optimum conditions, the minimum mean squared error (MSE) of all the above estimators is equal to the MSE of the regression estimator.
Author: Rajesh Singh
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 11
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Book Description
Singh et al. (20009) introduced a family of exponential ratio and product type estimators in stratified random sampling. Under stratified random sampling without replacement scheme, the expressions of bias and mean square error (MSE) of Singh et al. (2009) and some other estimators, up to the first- and second-order approximations are derived. Also, the theoretical findings are supported by a numerical example.
Author: Rajesh Singh
Publisher: Infinite Study
ISBN: 1599730464
Category : Mathematics
Languages : en
Pages : 75
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Book Description
This volume is a collection of six papers on the use of auxiliary information and a priori values in construction of improved estimators. The work included here will be of immense application for researchers and students who employ auxiliary information in any form.
Author: Rajesh Singh
Publisher: Infinite Study
ISBN: 1599732300
Category : Business & Economics
Languages : en
Pages : 66
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Book Description
Author: Rajesh Singh
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 12
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Book Description
Some ratio estimators for estimating the population mean of the variable under study, which make use of information regarding the population proportion possessing certain attribute, are proposed.
Author: Rajesh Singh
Publisher: Infinite Study
ISBN: 1599732750
Category : Population
Languages : en
Pages : 73
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Book Description
The purpose of writing this book is to suggest some improved estimators using auxiliary information in sampling schemes like simple random sampling, systematic sampling and stratified random sampling. This volume is a collection of five papers, written by nine co-authors (listed in the order of the papers): Rajesh Singh, Mukesh Kumar, Manoj Kr. Chaudhary, Cem Kadilar, Prayas Sharma, Florentin Smarandache, Anil Prajapati, Hemant Verma, and Viplav Kr. Singh. In first paper dual to ratio-cum-product estimator is suggested and its properties are studied. In second paper an exponential ratio-product type estimator in stratified random sampling is proposed and its properties are studied under second order approximation. In third paper some estimators are proposed in two-phase sampling and their properties are studied in the presence of non-response. In fourth chapter a family of median based estimator is proposed in simple random sampling. In fifth paper some difference type estimators are suggested in simple random sampling and stratified random sampling and their properties are studied in presence of measurement error.
Author: Qi Zhang
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 70
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Book Description
"Ratio estimation is a parameter estimation technique that uses a known auxiliary variable that is correlated with the study variable. In many situations, the primary variable of interest may be sensitive and it cannot be observed directly. However, we can observe directly a non-sensitive variable that is highly correlated with the study variable. In these cases, we have to rely on some Randomized Response Technique (RRT) models to obtain information on the study variable. In this thesis, we first review some RRT models, some general ratio and product estimation techniques, and two Kalucha et al. (2015) ratio estimators that are based on Gupta et al. (2010) additive optional RRT model. One of the Kalucha et al. (2015) estimators, the multiplicative ratio estimator, did not work efficiently and was abandoned. The main focus of this thesis is on fixing the Kalucha et al. (2015) abandoned multiplicative ratio estimator and reevaluating its performance. We discuss the Bias and the Mean Square Error (MSE) of our proposed multiplicative ratio estimator correct up to first order approximation, and present the comparisons with other estimators under the additive optional RRT model. A simulation study is also conducted to verify the theoretical result. Both the theoretical and the empirical results show that the proposed multiplicative ratio estimator is more efficient than the ordinary RRT estimator that does not utilize the auxiliary variable. It also compares well with the additive ratio estimator of Kalucha et al. (2015)."--Abstract from author supplied metadata.
Author: Peter I. Ogunyinka
Publisher: LAP Lambert Academic Publishing
ISBN: 9783659501562
Category :
Languages : en
Pages : 100
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Book Description
This book takes a full study on three estimators namely Simple random sampling without replacement (SRSWOR), double sampling for ratio and double sampling for regression estimators. These estimation procedures were applied to online software repository data obtained on www.download.com and www.sourceforge.net, hence, revealing the undiscovered but important application of Statistics to the improvement of software design and usage. Furthermore, primary data on household were collected with questionnaires which were administered by the staff and students of Nursing School of University College Hospital (UCH) in Nigeria. Empirical analysis was executed in studying the performances of these estimators in estimating the population mean. It was observed that double sampling with regression type estimator claims superiority over other estimators by possessing a minimum optimum variance under certain conditions. Relative efficiency and Coefficient of variation further confirmed this superiority of double sampling for regression with highest efficiency and precision respectively over double sampling for ratio and SRSWOR estimators.
Author: Tanja Zatezalo
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 117
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Book Description
"The first order approximation of the theoretical mean square error and assumption of bivariate normality are very often used for the ratio type estimators for the population mean and variance. We have examined the adequacy of the first order approximation and the robustness of various ratio type estimators. We observed that the first order approximation for ratio type mean estimators and ratio type variance estimators works well if the sampling fraction is small and that departure from the assumption of bivariate normality is not a problem for large samples. We have also proposed some generalized mixture estimators which are combinations of the commonly used estimators. We have also extended the proposed generalized mixture estimators to the case when the study variable is sensitive and a non sensitive auxiliary variable is available. We have shown that the proposed generalized mixture estimators are more efficient than other commonly used estimators. An extensive simulation study and numerical examples are also presented."--Abstract from author supplied metadata.
Author: Subhash Kumar Yadav
Publisher: Infinite Study
ISBN:
Category : Mathematics
Languages : en
Pages : 20
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Book Description
One of the disadvantages of the point estimate in survey sampling is that it fluctuates from sample to sample due to sampling error, as the estimator only provides a point value for the parameter under discussion. The neutrosophic approach, pioneered by Florentin Smarandache, is an excellent tool for estimating the parameters under consideration in sampling theory since it yields interval estimates in which the parameter lies with a very high probability. As a result, the neutrosophic technique, which is a generalization of classical approach, is used to deal with ambiguous, indeterminate, and uncertain data. In this investigation, we suggest a new general family of ratio and exponential ratio type estimators for the elevated estimation of neutrosophic population mean of the primary variable utilizing known neutrosophic auxiliary parameters. For the first degree approximation, the bias and Mean Squared Error (MSE) of the suggested estimators are computed. The neutrosophic optimum values of the characterizing constants are determined, as well as the minimum value of the neutrosophic MSE of the suggested estimator is obtained for these optimum values of the characterizing scalars. Because the minimum MSE of the classical estimators of population mean lies inside the estimated interval of the neutrosophic estimators, the neutrosophic estimators are better than the equivalent classical estimators. The empirical investigation, which used both real and simulated data sets, backs up the theoretical findings. For practical utility in various areas of applications, the estimator with the lowest MSE or highest Percentage Relative Efficiency (PRE) is recommended.
