The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Fikri Akdeniz - One of the best experts on this subject based on the ideXlab platform.
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The distribution of the Liu-Type Estimator of the biasing parameter in elliptically contoured models
Communications in Statistics - Theory and Methods, 2016Co-Authors: Mohammad Arashi, Saralees Nadarajah, Fikri AkdenizAbstract:ABSTRACTWe derive the density function of the stochastic shrinkage parameters of the Liu-Type Estimator in elliptical models. The correctness of derivation is checked by simulations. A real data application is also provided.
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Efficiency of a Liu-Type Estimator in semiparametric regression models
Journal of Computational and Applied Mathematics, 2011Co-Authors: Esra Akdeniz Duran, Fikri AkdenizAbstract:In this paper we consider the semiparametric regression model, y=X@b+f+@e. Recently, Hu [11] proposed ridge regression Estimator in a semiparametric regression model. We introduce a Liu-Type (combined ridge-Stein) Estimator (LTE) in a semiparametric regression model. Firstly, Liu-Type Estimators of both @b and f are attained without a restrained design matrix. Secondly, the LTE Estimator of @b is compared with the two-step Estimator in terms of the mean square error. We describe the almost unbiased Liu-Type Estimator in semiparametric regression models. The almost unbiased Liu-Type Estimator is compared with the Liu-Type Estimator in terms of the mean squared error matrix. A numerical example is provided to show the performance of the Estimators.
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Efficiency of the modified jackknifed Liu-Type Estimator
Statistical Papers, 2010Co-Authors: Esra Akdeniz Duran, Fikri AkdenizAbstract:In this article, we proposed a new Estimator namely, modified jackknifed generalized Liu-Type Estimator (MJGLE). It is based on the criterion that it combines the ideas underlying both the generalized Liu Estimator (GLE) and jackknifed generalized Liu Estimator (JGLE). The performance of this Estimator (MJGLE) is compared to that of the GLE and the JGLE. The ideas in the article are illustrated and evaluated using a real data example and simulations.
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Liu-Type Estimator in semiparametric regression models
Journal of Statistical Computation and Simulation, 2010Co-Authors: Fikri Akdeniz, Esra Akdeniz DuranAbstract:In this paper, we introduced a Liu-Type Estimator for the vector of parameters β in a semiparametric regression model. We also obtained the semiparametric restricted Liu-Type Estimator for the parametric component in a semiparametric regression model. The ideas in the paper are illustrated in a real data example and in a Monte Carlo simulation study.
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The Efficiency of Modified Jackknifed Liu-Type Estimator
2009Co-Authors: Esra Akdeniz Duran, Fikri AkdenizAbstract:In this paper, we proposed a new Estimator namely, modified jackknifed generalized Liu-Type Estimator (MJGLE). It is based on the criterion that it combines the ideas underlying both the generalized Liu Estimator (GLE) and the jackknifed generalized Liu Estimator (JGLE). The performance of MJGLE is compared to that of the GLE and JGLE. The ideas in the paper are illustrated and evaluated using simulations.
Muhammad Q. Shahbaz - One of the best experts on this subject based on the ideXlab platform.
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Generalized ratio-product-Type Estimator for variance using auxiliary information in simple random sampling
kuwait journal of science, 2018Co-Authors: Muhammad Ismail, Nazia Kawnal, Muhammad Q. ShahbazAbstract:This paper suggests a new generalized ratio-product-Type Estimator for population variance of study variable utilizing information obtained from two auxiliary variables. Efficiency of the new Estimator has been compared mathematically with the generalized ratio-product-Type Estimator based on information from auxiliary variable under simple random sampling without replacement. Empirically, the Estimator proves more efficient than the usual unbiased Estimator and some previously existing biased variance Estimators under the derived conditions and for suitable choice of scalars and constants at which bias is also smaller in comparison. It is also worth-mentioning that all the Estimators under discussion are the special cases of the new generalized ratio-product-Type Estimator for population variance.
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a modified regression Type Estimator in survey sampling
2009Co-Authors: Muhammad Hanif, Naqvi Hamad, Muhammad Q. ShahbazAbstract:A new Estimator of population mean has been proposed for single phase sampling using information on two auxiliary variables. The new Estimator has been proposed by combining the regression Estimator with ratio and product Estimators. The mean square error of the proposed Estimator has been obtained to first order approximation. Efficiency comparison of the proposed Estimator has been made with the Estimator proposed by Samiuddin and Hanif (11). It has been seen that the proposed Estimator performs reasonably well as compared with the existing Estimators of population mean.
William E Strawderman - One of the best experts on this subject based on the ideXlab platform.
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a james stein Type Estimator for combining unbiased and possibly biased Estimators
Journal of the American Statistical Association, 1991Co-Authors: Edwin J Green, William E StrawdermanAbstract:Abstract We present a method for combining unbiased sample data with possibly biased auxiliary information. The Estimator we derive is similar in spirit to the James–Stein Estimator. We prove that the Estimator dominates the sample mean under quadratic loss. When the auxiliary information is unbiased, our Estimator has risk slightly greater than the usual combined Estimator. As the bias increases, however, the risk of the usual Estimator is unbounded, while the risk of our Estimator is bounded by the risk of the sample mean. We show how our Estimator can be considered an approximation to the best linear combination of the sample data and the auxiliary information, allude to how it can be derived as an empirical Bayes Estimator, and suggest a method for constructing confidence sets. Finally, the performance of our Estimator is compared to that of the sample mean and the usual combined Estimator using real forestry data.
Esra Akdeniz Duran - One of the best experts on this subject based on the ideXlab platform.
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Efficiency of a Liu-Type Estimator in semiparametric regression models
Journal of Computational and Applied Mathematics, 2011Co-Authors: Esra Akdeniz Duran, Fikri AkdenizAbstract:In this paper we consider the semiparametric regression model, y=X@b+f+@e. Recently, Hu [11] proposed ridge regression Estimator in a semiparametric regression model. We introduce a Liu-Type (combined ridge-Stein) Estimator (LTE) in a semiparametric regression model. Firstly, Liu-Type Estimators of both @b and f are attained without a restrained design matrix. Secondly, the LTE Estimator of @b is compared with the two-step Estimator in terms of the mean square error. We describe the almost unbiased Liu-Type Estimator in semiparametric regression models. The almost unbiased Liu-Type Estimator is compared with the Liu-Type Estimator in terms of the mean squared error matrix. A numerical example is provided to show the performance of the Estimators.
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Efficiency of the modified jackknifed Liu-Type Estimator
Statistical Papers, 2010Co-Authors: Esra Akdeniz Duran, Fikri AkdenizAbstract:In this article, we proposed a new Estimator namely, modified jackknifed generalized Liu-Type Estimator (MJGLE). It is based on the criterion that it combines the ideas underlying both the generalized Liu Estimator (GLE) and jackknifed generalized Liu Estimator (JGLE). The performance of this Estimator (MJGLE) is compared to that of the GLE and the JGLE. The ideas in the article are illustrated and evaluated using a real data example and simulations.
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Liu-Type Estimator in semiparametric regression models
Journal of Statistical Computation and Simulation, 2010Co-Authors: Fikri Akdeniz, Esra Akdeniz DuranAbstract:In this paper, we introduced a Liu-Type Estimator for the vector of parameters β in a semiparametric regression model. We also obtained the semiparametric restricted Liu-Type Estimator for the parametric component in a semiparametric regression model. The ideas in the paper are illustrated in a real data example and in a Monte Carlo simulation study.
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The Efficiency of Modified Jackknifed Liu-Type Estimator
2009Co-Authors: Esra Akdeniz Duran, Fikri AkdenizAbstract:In this paper, we proposed a new Estimator namely, modified jackknifed generalized Liu-Type Estimator (MJGLE). It is based on the criterion that it combines the ideas underlying both the generalized Liu Estimator (GLE) and the jackknifed generalized Liu Estimator (JGLE). The performance of MJGLE is compared to that of the GLE and JGLE. The ideas in the paper are illustrated and evaluated using simulations.
Rajesh Singh - One of the best experts on this subject based on the ideXlab platform.
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ESTIMATION OF POPULATION VARIANCE IN LOG – PRODUCT Type EstimatorS UNDER DOUBLE SAMPLING SCHEME
Journal of Reliability and Statistical Studies, 2019Co-Authors: Prabhakar Mishra, Rajesh Singh, Supriya KhareAbstract:It is experienced that auxiliary information when suitably incorporated yields more efficient and precise estimates. Mishra et al. (2017) have introduced a log Type Estimator for estimating unknown population mean using ancillary information in simple random sampling. Here we propose an improved log-product Type Estimator for population variance under double sampling. Properties of the Estimators are studied both mathematically and numerically.
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estimation of population mean in chain ratio Type Estimator under systematic sampling
Journal of Probability and Statistics, 2015Co-Authors: Mursala Khan, Rajesh SinghAbstract:A chain ratio-Type Estimator is proposed for the estimation of finite population mean under systematic sampling scheme using two auxiliary variables. The mean square error of the proposed Estimator is derived up to the first order of approximation and is compared with other relevant existing Estimators. To illustrate the performances of the different Estimators in comparison with the usual simple Estimator, we have taken a real data set from the literature of survey sampling.
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improved ratio Type Estimator for variance using auxiliary information
2001Co-Authors: Housila P. Singh, Rajesh SinghAbstract:Suppose we have a population of N identifiable units on which (k + 1) characteristics y, xJ, X2..." Xk are defined. Here y is the characteristic of interest and Xi. (i = 1, 2, ... , k) are auxiliary characteristics whose population variances or, (i =1,2, ... , k) are assumed to be known. Let (Yh, Xih) denote the values ofy and Xi on the unit h. Assume that a simple random sample of size n is drawn with replacement, (Yh, Xih), (h =1, 2, ...• n) are observed. Now assume that the problem is to estimate the population variance