The Experts below are selected from a list of 303 Experts worldwide ranked by ideXlab platform
Pushpakanthie Wijekoon - One of the best experts on this subject based on the ideXlab platform.
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Modified Almost UnBiased Liu Estimator in Linear Regression Model
Communications in Mathematics and Statistics, 2017Co-Authors: Sivarajah Arumairajan, Pushpakanthie WijekoonAbstract:In this paper, we propose a new Biased Estimator namely modified almost unBiased Liu Estimator by combining almost unBiased Liu Estimator (AULE) and ridge Estimator (RE) in a linear regression model when multicollinearity presents among the independent variables. Necessary and sufficient conditions for the proposed Estimator over the ordinary least square Estimator, RE, AULE and Liu Estimator (LE) in the mean squared error matrix sense are derived, and the optimal biasing parameters are obtained. To illustrate the theoretical findings, a Monte Carlo simulation study is carried out and a numerical example is used.
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optimal generalized Biased Estimator in linear regression model
Open Journal of Statistics, 2015Co-Authors: Sivarajah Arumairajan, Pushpakanthie WijekoonAbstract:The paper introduces a new Biased Estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed Estimator were derived, and the proposed Estimator was compared with other existing Biased Estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.
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improvement of the liu Estimator in linear regression model
Statistical Papers, 2006Co-Authors: M H Hubert, Pushpakanthie WijekoonAbstract:In the presence of stochastic prior information, in addition to the sample, Theil and Goldberger (1961) introduced a Mixed Estimator \(\hat \beta _m \) for the parameter vector β in the standard multiple linear regression model (T,Xβ,σ2 I). Recently, the Liu Estimator which is an alternative Biased Estimator for β has been proposed by Liu (1993).
Joseph E Cavanaugh - One of the best experts on this subject based on the ideXlab platform.
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an improved akaike information criterion for state space model selection
Computational Statistics & Data Analysis, 2006Co-Authors: Thomas Bengtsson, Joseph E CavanaughAbstract:Following the work of Hurvich, Shumway, and Tsai [1990, Improved Estimators of Kullback-Leibler information for autoregressive model selection in small samples. Biometrika 77, 709-719], we propose an ''improved'' variant of the Akaike information criterion, AICi, for state-space model selection. The variant is based on Akaike's [1973, Information theory and an extension of maximum likelihood principle. Second International Symposium on Information Theory, Akademia Kiado, pp. 267-281] objective of estimating the Kullback-Leibler information [Kullback, 1968, Information Theory and Statistics. Dover, New York] between the densities corresponding to the fitted model and the generating or true model. The development of AICi proceeds by decomposing the expected information into two terms. The first term suggests that the empirical log likelihood can be used to form a Biased Estimator of the information; the second term provides the bias adjustment. Exact computation of the bias adjustment requires the values of the true model parameters, which are inaccessible in practical applications. Yet for fitted models in the candidate class that are correctly specified or overfit, the adjustment is asymptotically independent of the true parameters. Thus, in certain settings, the adjustment may be estimated via Monte Carlo simulations by using conveniently chosen simulation parameters as proxies for the true parameters. We present simulation results to evaluate the performance of AICi both as an Estimator of the Kullback-Leibler information and as a model selection criterion. Our results indicate that AICi estimates the information with less bias than traditional AIC. Furthermore, AICi serves as an effective tool for selecting a model of appropriate dimension.
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an improved akaike information criterion for state space model selection
Computational Statistics & Data Analysis, 2006Co-Authors: Thomas Bengtsson, Joseph E CavanaughAbstract:Following the work of Hurvich, Shumway, and Tsai [1990, Improved Estimators of Kullback-Leibler information for autoregressive model selection in small samples. Biometrika 77, 709-719], we propose an ''improved'' variant of the Akaike information criterion, AICi, for state-space model selection. The variant is based on Akaike's [1973, Information theory and an extension of maximum likelihood principle. Second International Symposium on Information Theory, Akademia Kiado, pp. 267-281] objective of estimating the Kullback-Leibler information [Kullback, 1968, Information Theory and Statistics. Dover, New York] between the densities corresponding to the fitted model and the generating or true model. The development of AICi proceeds by decomposing the expected information into two terms. The first term suggests that the empirical log likelihood can be used to form a Biased Estimator of the information; the second term provides the bias adjustment. Exact computation of the bias adjustment requires the values of the true model parameters, which are inaccessible in practical applications. Yet for fitted models in the candidate class that are correctly specified or overfit, the adjustment is asymptotically independent of the true parameters. Thus, in certain settings, the adjustment may be estimated via Monte Carlo simulations by using conveniently chosen simulation parameters as proxies for the true parameters. We present simulation results to evaluate the performance of AICi both as an Estimator of the Kullback-Leibler information and as a model selection criterion. Our results indicate that AICi estimates the information with less bias than traditional AIC. Furthermore, AICi serves as an effective tool for selecting a model of appropriate dimension.
Daniel R Smith - One of the best experts on this subject based on the ideXlab platform.
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the distribution of the sample minimum variance frontier
Management Science, 2008Co-Authors: Raymond Kan, Daniel R SmithAbstract:In this paper, we present a finite sample analysis of the sample minimum-variance frontier under the assumption that the returns are independent and multivariate normally distributed. We show that the sample minimum-variance frontier is a highly Biased Estimator of the population frontier, and we propose an improved Estimator of the population frontier. In addition, we provide the exact distribution of the out-of-sample mean and variance of sample minimum-variance portfolios. This allows us to understand the impact of estimation error on the performance of in-sample optimal portfolios.
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the distribution of the sample minimum variance frontier
QUT Business School, 2008Co-Authors: Raymond Kan, Daniel R SmithAbstract:In this paper, we present a finite sample analysis of the sample minimum-variance frontier under the assumption that the returns are independent and multivariate normally distributed. We show that the sample minimum-variance frontier is a highly Biased Estimator of the population frontier, and we propose an improved Estimator of the population frontier. In addition, we provide the exact distribution of the out-of-sample mean and variance of sample minimum-variance portfolios. This allows us to understand the impact of estimation error on the performance of in-sample optimal portfolios. Key Words: minimum-variance frontier; efficiency set constants; finite sample distribution
Sivarajah Arumairajan - One of the best experts on this subject based on the ideXlab platform.
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Modified Almost UnBiased Liu Estimator in Linear Regression Model
Communications in Mathematics and Statistics, 2017Co-Authors: Sivarajah Arumairajan, Pushpakanthie WijekoonAbstract:In this paper, we propose a new Biased Estimator namely modified almost unBiased Liu Estimator by combining almost unBiased Liu Estimator (AULE) and ridge Estimator (RE) in a linear regression model when multicollinearity presents among the independent variables. Necessary and sufficient conditions for the proposed Estimator over the ordinary least square Estimator, RE, AULE and Liu Estimator (LE) in the mean squared error matrix sense are derived, and the optimal biasing parameters are obtained. To illustrate the theoretical findings, a Monte Carlo simulation study is carried out and a numerical example is used.
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optimal generalized Biased Estimator in linear regression model
Open Journal of Statistics, 2015Co-Authors: Sivarajah Arumairajan, Pushpakanthie WijekoonAbstract:The paper introduces a new Biased Estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed Estimator were derived, and the proposed Estimator was compared with other existing Biased Estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.
Thomas Bengtsson - One of the best experts on this subject based on the ideXlab platform.
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an improved akaike information criterion for state space model selection
Computational Statistics & Data Analysis, 2006Co-Authors: Thomas Bengtsson, Joseph E CavanaughAbstract:Following the work of Hurvich, Shumway, and Tsai [1990, Improved Estimators of Kullback-Leibler information for autoregressive model selection in small samples. Biometrika 77, 709-719], we propose an ''improved'' variant of the Akaike information criterion, AICi, for state-space model selection. The variant is based on Akaike's [1973, Information theory and an extension of maximum likelihood principle. Second International Symposium on Information Theory, Akademia Kiado, pp. 267-281] objective of estimating the Kullback-Leibler information [Kullback, 1968, Information Theory and Statistics. Dover, New York] between the densities corresponding to the fitted model and the generating or true model. The development of AICi proceeds by decomposing the expected information into two terms. The first term suggests that the empirical log likelihood can be used to form a Biased Estimator of the information; the second term provides the bias adjustment. Exact computation of the bias adjustment requires the values of the true model parameters, which are inaccessible in practical applications. Yet for fitted models in the candidate class that are correctly specified or overfit, the adjustment is asymptotically independent of the true parameters. Thus, in certain settings, the adjustment may be estimated via Monte Carlo simulations by using conveniently chosen simulation parameters as proxies for the true parameters. We present simulation results to evaluate the performance of AICi both as an Estimator of the Kullback-Leibler information and as a model selection criterion. Our results indicate that AICi estimates the information with less bias than traditional AIC. Furthermore, AICi serves as an effective tool for selecting a model of appropriate dimension.
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an improved akaike information criterion for state space model selection
Computational Statistics & Data Analysis, 2006Co-Authors: Thomas Bengtsson, Joseph E CavanaughAbstract:Following the work of Hurvich, Shumway, and Tsai [1990, Improved Estimators of Kullback-Leibler information for autoregressive model selection in small samples. Biometrika 77, 709-719], we propose an ''improved'' variant of the Akaike information criterion, AICi, for state-space model selection. The variant is based on Akaike's [1973, Information theory and an extension of maximum likelihood principle. Second International Symposium on Information Theory, Akademia Kiado, pp. 267-281] objective of estimating the Kullback-Leibler information [Kullback, 1968, Information Theory and Statistics. Dover, New York] between the densities corresponding to the fitted model and the generating or true model. The development of AICi proceeds by decomposing the expected information into two terms. The first term suggests that the empirical log likelihood can be used to form a Biased Estimator of the information; the second term provides the bias adjustment. Exact computation of the bias adjustment requires the values of the true model parameters, which are inaccessible in practical applications. Yet for fitted models in the candidate class that are correctly specified or overfit, the adjustment is asymptotically independent of the true parameters. Thus, in certain settings, the adjustment may be estimated via Monte Carlo simulations by using conveniently chosen simulation parameters as proxies for the true parameters. We present simulation results to evaluate the performance of AICi both as an Estimator of the Kullback-Leibler information and as a model selection criterion. Our results indicate that AICi estimates the information with less bias than traditional AIC. Furthermore, AICi serves as an effective tool for selecting a model of appropriate dimension.