The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform
Housila P. Singh - One of the best experts on this subject based on the ideXlab platform.
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A New Exponential Approach for Reducing the Mean Squared Errors of the Estimators of Population Mean Using Conventional and Non-Conventional Location Parameters
Journal of Modern Applied Statistical Methods, 2020Co-Authors: Housila P. Singh, Anita YadavAbstract:Classes of ratio-type estimators t (say) and ratio-type exponential estimators te (say) of the population mean are proposed, and their biases and mean squared errors under Large Sample Approximation are presented. It is the class of ratio-type exponential estimators te provides estimators more efficient than the ratio-type estimators.
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A class of estimators for estimating the population mean and variance using auxiliary information under adoptive cluster sampling in Sample surveys
Bulletin of Pure & Applied Sciences- Mathematics and Statistics, 2019Co-Authors: Housila P. Singh, Anita YadavAbstract:For estimating the mean of finite population using information on an auxiliary variable we define the classes of estimators under adoptive cluster sampling in this paper. Expressions for their biases and mean squared errors are obtained under Large Sample Approximation. The minimum mean squared errors of each class of estimators are also given. A similar class of estimators is defined for the variance of the estimator of the mean. A condition is obtained under which the proposed class of estimators of the variance of the estimator is minimum.
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A family of efficient estimators of the finite population mean in simple random sampling
Journal of Statistical Computation and Simulation, 2017Co-Authors: Surya K. Pal, Housila P. Singh, Sunil Kumar, Kiranmoy ChatterjeeAbstract:ABSTRACTIn this paper an estimator of the finite population mean using auxiliary information in Sample surveys has been proposed. The bias and mean squared error are obtained under Large Sample Approximation. It has been shown that the proposed estimator performs better than some recently published estimators.
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Estimation of finite population mean using auxiliary information in presence of non-response
Communications in Statistics - Simulation and Computation, 2017Co-Authors: Surya K. Pal, Housila P. SinghAbstract:ABSTRACTThis article considers the problem of estimating the finite population mean of the study variable y using information on an auxiliary variable x in presence of non-response. We have suggested two-parameter ratio-product-ratio estimator in two different situations and their properties are studied under Large Sample Approximation. We have presented comparisons of the proposed two-parameter ratio-product-ratio estimator with usual unbiased estimator and ratio estimators (tR1,tR2). An empirical study is carried out in support of the present study.
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estimation of population variance using known coefficient of variation of an auxiliary variable in Sample surveys
Journal of Statistics and Management Systems, 2017Co-Authors: Housila P. SinghAbstract:AbstractThis paper addresses the problem of estimating the population variance of the study variable y using information on coefficient of variation Cx of an auxiliary variable x. We have suggested a class of estimators for the population variance. Expressions of bias and mean squared error of the proposed class of estimators are obtained under Large Sample Approximation. It is identified that the usual unbiased estimator and Das and Tripathi’s (1978) estimators are members of the suggested class of estimators. It has been shown that proposed class of estimators is more efficient than usual unbiased estimator and Das and Tripathi’s (1978) estimators. An empirical study is given in support of the present study.
B.t. Sieskul - One of the best experts on this subject based on the ideXlab platform.
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An Asymptotic Maximum Likelihood for Joint Estimation of Nominal Angles and Angular Spreads of Multiple Spatially Distributed Sources
IEEE Transactions on Vehicular Technology, 2010Co-Authors: B.t. SieskulAbstract:This paper proposes a Large-Sample Approximation of the maximum likelihood (ML) criterion for the joint estimation of nominal directions and angular spreads in the presence of multiple spatially spread sources. The key idea is the concentration on the exact likelihood function by replacing the parametric nuisance estimate, which depends on all unknown parameters at the critical point, by another estimate relying on only the angles of interest, such as nominal angles and angular spreads. Rather than the (3NS + 1) -dimensional optimization required by the exact ML estimator, the proposed Large-Sample Approximation allows 2NS-dimensional search, where NS is the number of sources. To demonstrate the proposed estimator, numerical results are conducted for the illustration of estimation error variance. In the non-asymptotic region, the proposed estimator outperforms previous approaches adopting the 2NS-dimensional search.
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PIMRC - A Large-Sample Approximate Maximum Likelihood for Localizing A Spatially Distributed Source
2005 IEEE 16th International Symposium on Personal Indoor and Mobile Radio Communications, 1Co-Authors: B.t. Sieskul, S. JitapunkulAbstract:This paper proposes a Large-Sample Approximation of the maximum likelihood (ML) criterion for estimating the nominal direction of a spatially spread source. The likelihood function is concentrated on at the critical point. The parametric nuisance estimate, which depends on all model parameters, is replaced by one that relies only on the nominal angle of interest. Rather than the four-dimensional optimization required in the exact ML estimation, this Large-Sample Approximation allows us to obtain only one-dimensional search. Since it is an asymptotic Approximation of the exact ML estimator, the standard deviation of its estimate error attains the Cramer-Rao bound for a Large number of temporal snapshots. To validate the asymptotic efficiency, numerical simulations are performed and also compared with previous approaches. The well-behaved results show that the asymptotic ML estimator outperforms several sub-optimal criteria in non-asymptotic region, both extreme SNR situations, and for Large angular spread
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Asymptotic maximum likelihood for localizing multiple spatially-distributed sources
NSIP 2005. Abstracts. IEEE-Eurasip Nonlinear Signal and Image Processing 2005., 1Co-Authors: B.t. Sieskul, S. JitapunkulAbstract:Summary form only given. This paper proposes a Large-Sample Approximation of maximum likelihood (ML) criterion for joint estimation of nominal directions and angular spreads in the presence of multiple spatially spread sources. The key behind the idea is to concentrate on the exact likelihood function by replacing the parametric nuisance estimate, which depends itself at the critical point on all model parameters, with one that relies only on angles of interest. Rather than (3N/sub S/ + 1) dimensions as the exact ML estimation required, this Large-Sample Approximation allows us to only 2N/sub S/-dimensional search, where N/sub S/ signifies the number of sources. Since it is an asymptotic Approximation of the ML estimator, its standard deviation of estimate error is derivable to attain the Cramer-Rao bound in Large number of temporal snapshots. To validate the new estimator, numerical results are performed and also compared with other previous approaches.
Ramkrishna S. Solanki - One of the best experts on this subject based on the ideXlab platform.
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An Improved Class of Estimators for the General Population Parameters Using Auxiliary Information
Communications in Statistics - Theory and Methods, 2015Co-Authors: Ramkrishna S. Solanki, Housila P. SinghAbstract:This article suggests an improved class of estimators for estimating the general population parameter using information on an auxiliary variable. The properties of the suggested class of estimators have been studied under Large Sample Approximation. The general results are then applied to estimate the population coefficient of variation of study variable using auxiliary information. An empirical study is given in support of the theoretical results.
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Some Classes of Estimators for Median Estimation in Survey Sampling
Communications in Statistics - Theory and Methods, 2014Co-Authors: Ramkrishna S. Solanki, Housila P. SinghAbstract:In this article, we have suggested some classes of estimators for estimating finite population median using information on an auxiliary variable. To study the properties of suggested classes of estimators under Large Sample Approximation, a generalized class of estimators has been suggested with its properties. It has been shown that the suggested classes of estimators are more efficient than other existing estimators. The results have been illustrated through an empirical study.
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Some Classes of Estimators for the Population Median Using Auxiliary Information
Communications in Statistics - Theory and Methods, 2013Co-Authors: Housila P. Singh, Ramkrishna S. SolankiAbstract:This article considers some classes of estimators of the population median of the study variable using information on an auxiliary variable with their properties under Large Sample Approximation. Asymptotic optimum estimator (AOE) in each class of estimators has been investigated along with the approximate mean square error formulae. It has been shown that the proposed classes of estimators are better than these considered by Gross (1980), Kuk and Mak (1989), Singh et al. (2003a), and Al and Cingi (2009). An empirical study is carried out to judge the merits of the suggested class of estimators over other existing estimators.
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IMPROVED CLASS OF ESTIMATORS OF FINITE POPULATION MEAN USING SAMPLING FRACTION AND INFORMATION ON TWO AUXILIARY VARIABLES IN Sample SURVEYS
Statistica, 2013Co-Authors: Housila P. Singh, Anjana Rathour, Ramkrishna S. SolankiAbstract:This paper suggested a generalized class of estimators using information on two auxiliary va-riables and sampling fraction in simple random sampling. The bias and mean squared error for-mulae of suggested class have been derived under Large Sample Approximation and compared with usual unbiased estimator and Singh’s (1967) ratio-cum-product estimator. The theoretical findings have been satisfied with an empirical study.
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An Efficient Class of Estimators for the Population Mean Using Auxiliary Information in Systematic Sampling
Journal of Statistical Theory and Practice, 2012Co-Authors: Housila P. Singh, Ramkrishna S. SolankiAbstract:This article addresses the problem of estimating the population mean in systematic sampling using information on an auxiliary variable. A class of estimators for the population mean is defined with its properties under Large Sample Approximation. It has been shown that the proposed class of estimators is better than the usual unbiased estimator, Swain (1964) estimator, Shukla (1971) estimator, and usual regression estimator. The results have been illustrated through an empirical study.
Byeong U. Park - One of the best experts on this subject based on the ideXlab platform.
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Large Sample Approximation of the distribution for convex hull estimators of boundaries
Scandinavian Journal of Statistics, 2006Co-Authors: Seokoh Jeong, Byeong U. ParkAbstract:. Given n independent and identically distributed observations in a set G = {(x, y) ∈ [0, 1]p × ℝ : 0 ≤ y ≤ g(x)} with an unknown function g, called a boundary or frontier, it is desired to estimate g from the observations. The problem has several important applications including classification and cluster analysis, and is closely related to edge estimation in image reconstruction. The convex-hull estimator of a boundary or frontier is also very popular in econometrics, where it is a cornerstone of a method known as ‘data envelope analysis’. In this paper, we give a Large Sample Approximation of the distribution of the convex-hull estimator in the general case where p ≥ 1. We discuss ways of using the Large Sample Approximation to correct the bias of the convex-hull and the DEA estimators and to construct confidence intervals for the true function.
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Large Sample Approximation of the Distribution for Convex‐Hull Estimators of Boundaries
Scandinavian Journal of Statistics, 2006Co-Authors: S.-o. Jeong, Byeong U. ParkAbstract:. Given n independent and identically distributed observations in a set G = {(x, y) ∈ [0, 1]p × ℝ : 0 ≤ y ≤ g(x)} with an unknown function g, called a boundary or frontier, it is desired to estimate g from the observations. The problem has several important applications including classification and cluster analysis, and is closely related to edge estimation in image reconstruction. The convex-hull estimator of a boundary or frontier is also very popular in econometrics, where it is a cornerstone of a method known as ‘data envelope analysis’. In this paper, we give a Large Sample Approximation of the distribution of the convex-hull estimator in the general case where p ≥ 1. We discuss ways of using the Large Sample Approximation to correct the bias of the convex-hull and the DEA estimators and to construct confidence intervals for the true function.
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Limit Distribution of Convex-Hull Estimators of Boundaries
2004Co-Authors: Seokoh Jeong, Byeong U. ParkAbstract:Given n independent and identically distributed observations in a set G with an unknown function g, called a boundary or frontier, it is desired to estimate g from the observations. The problem has several important applications including classification and cluster analysis, and is closely related to edge estimation in image reconstruction. It is particularly important in econometrics. The convex-hull estimator of a boundary or frontier is very popular in econometrics, where it is a cornerstone of a method known as `data envelope analysis´ or DEA. In this paper we give a Large Sample Approximation of the distribution of the convex-hull estimator in the general case where p>=1. We discuss ways of using the Large Sample Approximation to correct the bias of the convex-hull and the DEA estimators and to construct confidence intervals for the true function.
Rohini Yadav - One of the best experts on this subject based on the ideXlab platform.
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Improved Ratio and Product Exponential Type Estimators
Journal of Statistical Theory and Practice, 2011Co-Authors: Lakshmi N. Upadhyaya, Housila P. Singh, S. Chatterjee, Rohini YadavAbstract:This paper addresses the problem of estimating the population mean using auxiliary information. Improved versions of Bahl and Tuteja (1991) ratio and product exponential type estimators have been proposed and their properties studied under Large Sample Approximation. It has been shown that the proposed ratio and product exponential type estimators are more efficient than those considered by Bahl and Tuteja (1991) estimators, conventional ratio and product estimators and the usual unbiased estimator under some realistic conditions. An empirical study has been carried out to judge the merits of the suggested estimators over others. Theoretical and empirical results are sound and quite illuminating compared to other estimators.
