The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Subhadeep Mukhopadhyay - One of the best experts on this subject based on the ideXlab platform.
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a penalized empirical likelihood method in high dimensions
Annals of Statistics, 2012Co-Authors: Soumendra N Lahiri, Subhadeep MukhopadhyayAbstract:This paper formulates a penalized empirical likelihood (PEL) method for inference on the population mean when the dimension of the observations may grow faster than the sample size. Asymptotic distributions of the PEL Ratio Statistic is derived under different component-wise dependence structures of the observations, namely, (i) non-Ergodic, (ii) long-range dependence and (iii) short-range dependence. It follows that the limit distribution of the proposed PEL Ratio Statistic can vary widely depending on the correlation structure, and it is typically different from the usual chi-squared limit of the empirical likelihood Ratio Statistic in the fixed and finite dimensional case. A unified subsampling based calibRation is proposed, and its validity is established in all three cases, (i)-(iii). Finite sample properties of the method are investigated through a simulation study.
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a penalized empirical likelihood method in high dimensions
Annals of Statistics, 2012Co-Authors: Soumendra N Lahiri, Subhadeep MukhopadhyayAbstract:We formulate a penalized empirical likelihood (PEL) method for inference on the population mean when the dimension of the observations become unbounded with the sample size. We derive the asymptotic distribution of the PEL Ratio Statistic. We show that the limit distribution of the proposed PEL Ratio Statistic can vary widely depending on the correlation structure of the components of the observations that we classify as (i) non-Ergodic, (ii) long range dependent, and (iii) short range dependent. The limit laws differ from the usual chi-squared limit of the empirical likelihood Ratio Statistic in the finite dimensional case. We propose a subsampling approximation for calibrating the PEL Ratio test Statistic and establish its validity. Finite sample properties of the method are investigated through a simulation study. ∗Joint work with Deep Mukhopadhyay .
Jens Ledet Jensen - One of the best experts on this subject based on the ideXlab platform.
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Tests and Confidence Intervals for an Extended Variance Component Using the Modified Likelihood Ratio Statistic
Scandinavian Journal of Statistics, 2007Co-Authors: Ole F. Christensen, Jens Ledet Jensen, Morten Frydenberg, Jørgen Granfeldt PedersenAbstract:The large deviation modified likelihood Ratio Statistic is studied for testing a variance component equal to a specified value. Formulas are presented in the general balanced case, whereas in the unbalanced case only the one-way random effects model is studied. Simulation studies are presented, showing that the normal approximation to the large deviation modified likelihood Ratio Statistic gives confidence intervals for variance components with coverage probabilities very close to the nominal confidence coefficient. Copyright 2007 Board of the Foundation of the Scandinavian Journal of Statistics..
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Tests and confidence intervals for an extended variance component using the modified likelihood Ratio Statistic
Scandinavian Journal of Statistics, 2007Co-Authors: Ole F. Christensen, Jens Ledet Jensen, Morten Frydenberg, Jørgen Granfeldt PedersenAbstract:. The large deviation modified likelihood Ratio Statistic is studied for testing a variance component equal to a specified value. Formulas are presented in the general balanced case, whereas in the unbalanced case only the one-way random effects model is studied. Simulation studies are presented, showing that the normal approximation to the large deviation modified likelihood Ratio Statistic gives confidence intervals for variance components with coverage probabilities very close to the nominal confidence coefficient.
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A Historical Sketch and Some New Results on the Improved Log Likelihood Ratio Statistic
Scandinavian Journal of Statistics, 1993Co-Authors: Jens Ledet JensenAbstract:In the first part we discuss the known results concerning the reduction of the error of the chi-squared approximation when using the Bartlett adjusted log likelihood Ratio Statistic. In the second part we state some new results for the case where the first four log likelihood derivatives have both continuous and lattice variables. The latter results are of interest for example in connection with censored life times and in logistic regression. Finally, in the last section we discuss how to perform the algebraic manipulations using REDUCE.
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The modified signed likelihood Statistic and saddlepoint approximations
Biometrika, 1992Co-Authors: Jens Ledet JensenAbstract:SUMMARY For a number of tests in exponential families we show that the use of a normal approximation to the modified signed likelihood Ratio Statistic r* is equivalent to the use of a saddlepoint approximation. This is also true in a large deviation region where the signed likelihood Ratio Statistic r is of order In.
Soumendra N Lahiri - One of the best experts on this subject based on the ideXlab platform.
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a penalized empirical likelihood method in high dimensions
Annals of Statistics, 2012Co-Authors: Soumendra N Lahiri, Subhadeep MukhopadhyayAbstract:This paper formulates a penalized empirical likelihood (PEL) method for inference on the population mean when the dimension of the observations may grow faster than the sample size. Asymptotic distributions of the PEL Ratio Statistic is derived under different component-wise dependence structures of the observations, namely, (i) non-Ergodic, (ii) long-range dependence and (iii) short-range dependence. It follows that the limit distribution of the proposed PEL Ratio Statistic can vary widely depending on the correlation structure, and it is typically different from the usual chi-squared limit of the empirical likelihood Ratio Statistic in the fixed and finite dimensional case. A unified subsampling based calibRation is proposed, and its validity is established in all three cases, (i)-(iii). Finite sample properties of the method are investigated through a simulation study.
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a penalized empirical likelihood method in high dimensions
Annals of Statistics, 2012Co-Authors: Soumendra N Lahiri, Subhadeep MukhopadhyayAbstract:We formulate a penalized empirical likelihood (PEL) method for inference on the population mean when the dimension of the observations become unbounded with the sample size. We derive the asymptotic distribution of the PEL Ratio Statistic. We show that the limit distribution of the proposed PEL Ratio Statistic can vary widely depending on the correlation structure of the components of the observations that we classify as (i) non-Ergodic, (ii) long range dependent, and (iii) short range dependent. The limit laws differ from the usual chi-squared limit of the empirical likelihood Ratio Statistic in the finite dimensional case. We propose a subsampling approximation for calibrating the PEL Ratio test Statistic and establish its validity. Finite sample properties of the method are investigated through a simulation study. ∗Joint work with Deep Mukhopadhyay .
Augustine C. M. Wong - One of the best experts on this subject based on the ideXlab platform.
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On standardizing the signed root log likelihood Ratio Statistic
Statistics & Probability Letters, 2012Co-Authors: L. Jiang, Augustine C. M. WongAbstract:A simple connection between the Bartlett adjustment factor of the log likelihood Ratio Statistic and the normalizing constant of the p∗ formula–an approximate conditional density for the maximum likelihood estimate given an exact or an approximate ancillary Statistic–was established in Barndorff-Nielsen and Cox (1984). In this paper, the explicit form of the normalizing constant of the p∗ formula for the scalar parameter model is derived. By change of variables, the mean and variance of the signed root log likelihood Ratio Statistic are obtained explicitly, and, hence, tail probabilities can be calculated from the standardized signed root log likelihood Ratio Statistic. Examples are used to illustrate the implementation and accuracy of the proposed method.
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Approximating the F distribution via a general version of the modified signed log-likelihood Ratio Statistic
Computational Statistics & Data Analysis, 2008Co-Authors: Augustine C. M. WongAbstract:A simple normal approximation for the cumulative distribution function of the F distribution is obtained via a general version of the modified signed log-likelihood Ratio Statistic. This approximation exhibits remarkable accuracy even when the degrees of freedom are small. Using the same methodology, but with a simpler set up, simple and accurate normal approximations to the cumulative distribution functions of the Student t and @g^2 distributions can also be obtained.
Jørgen Granfeldt Pedersen - One of the best experts on this subject based on the ideXlab platform.
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Tests and Confidence Intervals for an Extended Variance Component Using the Modified Likelihood Ratio Statistic
Scandinavian Journal of Statistics, 2007Co-Authors: Ole F. Christensen, Jens Ledet Jensen, Morten Frydenberg, Jørgen Granfeldt PedersenAbstract:The large deviation modified likelihood Ratio Statistic is studied for testing a variance component equal to a specified value. Formulas are presented in the general balanced case, whereas in the unbalanced case only the one-way random effects model is studied. Simulation studies are presented, showing that the normal approximation to the large deviation modified likelihood Ratio Statistic gives confidence intervals for variance components with coverage probabilities very close to the nominal confidence coefficient. Copyright 2007 Board of the Foundation of the Scandinavian Journal of Statistics..
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Tests and confidence intervals for an extended variance component using the modified likelihood Ratio Statistic
Scandinavian Journal of Statistics, 2007Co-Authors: Ole F. Christensen, Jens Ledet Jensen, Morten Frydenberg, Jørgen Granfeldt PedersenAbstract:. The large deviation modified likelihood Ratio Statistic is studied for testing a variance component equal to a specified value. Formulas are presented in the general balanced case, whereas in the unbalanced case only the one-way random effects model is studied. Simulation studies are presented, showing that the normal approximation to the large deviation modified likelihood Ratio Statistic gives confidence intervals for variance components with coverage probabilities very close to the nominal confidence coefficient.
