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S B Kang - One of the best experts on this subject based on the ideXlab platform.
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OPEN ACCESS www.sciforum.net/conference/ecea-1 Conference Proceedings Paper – Entropy Bayesian Estimation of the Entropy of the Half-Logistic Distribution Based on Type-II Censored Samples
2016Co-Authors: J. I. Seo, S B KangAbstract:Abstract: This paper estimates the entropy of the half-Logistic Distribution with the scale parameter based on Type-II censored samples. The maximum likelihood estimator and the approximate confidence interval are derived for entropy. For Bayesian inferences, a hierarchical Bayesian estimation method is developed using the hierarchical structure of the gamma prior Distribution which induces a noninformative prior. The random-walk Metropolis algorithm is employed to generate Markov chain Monte Carlo samples from the posterior Distribution of entropy. The proposed estimation methods are compared through Monte Carlo simulations for various Type-II censoring schemes. Finally, real data are analyzed for illustration purposes
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Estimation and testing procedures for the reliability functions of generalized half Logistic Distribution
Journal of the Korean Statistical Society, 2016Co-Authors: Ajit Chaturvedi, S B Kang, Anupam PathakAbstract:Two measuresofreliability are considered, R ( t )= P ( X > t ) and P = P ( X > Y ). Estimation and testing procedures are developed for R ( t ) and P under Type II cesoring and a sampling scheme of Bartholomew (1963). Two types of point estimators are considered (i) uniformly minimum variance unbiased estimators (UMVUEs) and (ii) maximum likelihood estimators (MLEs). A new technique of obtaining these estimators is introduced. A comparative study of different methods of estimation is done. Testing procedures are developed for the hypotheses related to different parametric functions.
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pivotal inference for the scaled half Logistic Distribution based on progressively type ii censored samples
Statistics & Probability Letters, 2015Co-Authors: J. I. Seo, S B KangAbstract:Abstract This paper proposes a new method based on a pivotal quantity for estimating the scale parameter of the half Logistic Distribution based on progressively Type-II censored samples, which provides a simpler estimation equation than the likelihood equation.
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Notes on the exponentiated half Logistic Distribution
Applied Mathematical Modelling, 2015Co-Authors: J. I. Seo, S B KangAbstract:In this paper moment estimators and maximum likelihood estimators of unknown parameters in the exponentiated half-Logistic Distribution are derived, and an entropy estimator is obtained for the Distribution. An exact expression of Fisher information is derived to obtain approximate confidence intervals for unknown parameters in the Distribution, and for illustration purposes, the validity of the proposed estimation method is assessed using real data.
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entropy estimation of generalized half Logistic Distribution ghld based on type ii censored samples
Entropy, 2014Co-Authors: J. I. Seo, S B KangAbstract:This paper derives the entropy of a generalized half-Logistic Distribution based on Type-II censored samples, obtains some entropy estimators by using Bayes estimators of an unknown parameter in the generalized half-Logistic Distribution based on Type-II censored samples and compares these estimators in terms of the mean squared error and the bias through Monte Carlo simulations.
Panayiotis Theodossiou - One of the best experts on this subject based on the ideXlab platform.
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truncated skewed type iii generalized Logistic Distribution risk measurement applications
Communications in Statistics-theory and Methods, 2020Co-Authors: Panayiotis TheodossiouAbstract:This article derives the moment functions of the truncated skewed type III generalized Logistic (SGL). These are then applied in finance for the development of value at risk (VaR), expected shortfa...
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skewed type iii generalized Logistic Distribution
Communications in Statistics-theory and Methods, 2019Co-Authors: Panayiotis TheodossiouAbstract:AbstractThis paper develops a skewed extension of the type III generalized Logistic Distribution and presents the analytical equations for the computation of its moments, cumulative probabilities a...
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skewed type iii generalized Logistic Distribution
Social Science Research Network, 2018Co-Authors: Panayiotis TheodossiouAbstract:This paper develops a skewed extension of the type III generalized Logistic Distribution and presents the analytical equations for the computation of its moments, cumulative probabilities and quantile values. It is demonstrated through an example that the Distribution provides an excellent fit to data characterized by skewness and excess kurtosis.
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truncated skewed type iii generalized Logistic Distribution risk measurement applications
Social Science Research Network, 2018Co-Authors: Panayiotis TheodossiouAbstract:This paper derives the moment functions of the truncated skewed type III generalized Logistic (SGL). These are then applied in finance for the development of value-at-risk, expected shortfall and downside risk measures for investment returns and values. The SGL Distribution provides and good fit to the empirical Distribution of a representative set of long series of financial data. Moreover, the SGL generates accurate value at risk measures.
J. I. Seo - One of the best experts on this subject based on the ideXlab platform.
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OPEN ACCESS www.sciforum.net/conference/ecea-1 Conference Proceedings Paper – Entropy Bayesian Estimation of the Entropy of the Half-Logistic Distribution Based on Type-II Censored Samples
2016Co-Authors: J. I. Seo, S B KangAbstract:Abstract: This paper estimates the entropy of the half-Logistic Distribution with the scale parameter based on Type-II censored samples. The maximum likelihood estimator and the approximate confidence interval are derived for entropy. For Bayesian inferences, a hierarchical Bayesian estimation method is developed using the hierarchical structure of the gamma prior Distribution which induces a noninformative prior. The random-walk Metropolis algorithm is employed to generate Markov chain Monte Carlo samples from the posterior Distribution of entropy. The proposed estimation methods are compared through Monte Carlo simulations for various Type-II censoring schemes. Finally, real data are analyzed for illustration purposes
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pivotal inference for the scaled half Logistic Distribution based on progressively type ii censored samples
Statistics & Probability Letters, 2015Co-Authors: J. I. Seo, S B KangAbstract:Abstract This paper proposes a new method based on a pivotal quantity for estimating the scale parameter of the half Logistic Distribution based on progressively Type-II censored samples, which provides a simpler estimation equation than the likelihood equation.
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Notes on the exponentiated half Logistic Distribution
Applied Mathematical Modelling, 2015Co-Authors: J. I. Seo, S B KangAbstract:In this paper moment estimators and maximum likelihood estimators of unknown parameters in the exponentiated half-Logistic Distribution are derived, and an entropy estimator is obtained for the Distribution. An exact expression of Fisher information is derived to obtain approximate confidence intervals for unknown parameters in the Distribution, and for illustration purposes, the validity of the proposed estimation method is assessed using real data.
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entropy estimation of generalized half Logistic Distribution ghld based on type ii censored samples
Entropy, 2014Co-Authors: J. I. Seo, S B KangAbstract:This paper derives the entropy of a generalized half-Logistic Distribution based on Type-II censored samples, obtains some entropy estimators by using Bayes estimators of an unknown parameter in the generalized half-Logistic Distribution based on Type-II censored samples and compares these estimators in terms of the mean squared error and the bias through Monte Carlo simulations.
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estimation for generalized half Logistic Distribution based on records
Journal of the Korean Data and Information Science Society, 2012Co-Authors: J. I. Seo, Hwajung Lee, Sukbok KanAbstract:In this paper, we derive maximum likelihood estimators (MLEs) and approximate MLEs (AMLEs) of the unknown parameters in a generalized half Logistic Distribution when the data are upper record values. As an illustration, we examine the validity of our estimation using real data and simulated data. Finally, we compare the proposed estimators in the sense of the mean squared error (MSE) through a Monte Carlo simulation for various record values of size.
H M Saleh - One of the best experts on this subject based on the ideXlab platform.
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recurrence relations for single and product moments of progressively type ii censored order statistics from generalized Logistic Distribution with applications to inference
Communications in Statistics - Simulation and Computation, 2017Co-Authors: N Balakrishnan, H M SalehAbstract:ABSTRACTIn this article, we establish several recurrence relations for the single and product moments of progressively Type-II right censored order statistics from a generalized Logistic Distribution. The use of these relations in a systematic manner allow us to compute all the means, variances, and covariances of progressively Type-II right censored order statistics from the generalized Logistic Distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes (R1, …, Rm). These moments are then utilized to derive best linear unbiased estimators of the scale and location-scale parameters of the generalized Logistic Distribution. A comparison of these estimators with the maximum likelihood estimates is then made through Monte Carlo simulations. Finally, the best linear unbiased predictors of censored failure times is discussed briefly.
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recurrence relations for single and product moments of progressively type ii censored order statistics from a generalized half Logistic Distribution with application to inference
Journal of Statistical Computation and Simulation, 2013Co-Authors: N Balakrishnan, H M SalehAbstract:In this paper, we establish several recurrence relations for the single and product moments of progressively Type-II right-censored order statistics from a generalized half-Logistic Distribution. The use of these relations in a systematic recursive manner enables the computation of all the means, variances, and covariances of progressively Type-II right-censored order statistics from the generalized half-Logistic Distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes (R 1, …, R m ). The results established here generalize the corresponding results for the usual order statistics due to Balakrishnan and Sandhu [Recurrence relations for single and product moments of order statistics from a generalized half-Logistic Distribution with applications to inference, J. Stat. Comput. Simul. 52 (1995), pp. 385–398.]. The moments so determined are then utilized to derive the best linear unbiased estimators of the scale and location–scale parameters of the generalized half-...
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relations for moments of progressively type ii censored order statistics from half Logistic Distribution with applications to inference
Computational Statistics & Data Analysis, 2011Co-Authors: N Balakrishnan, H M SalehAbstract:In this paper, we establish several recurrence relations for the single and product moments of progressively Type-II right censored order statistics from a half-Logistic Distribution. The use of these relations in a systematic recursive manner would enable one to compute all the means, variances and covariances of progressively Type-II right censored order statistics from the half-Logistic Distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes (R"1,...,R"m). The results established here generalize the corresponding results for the usual order statistics due to Balakrishnan (1985). These moments are then utilized to derive best linear unbiased estimators of the scale and location-scale parameters of the half-Logistic Distribution. A comparison of these estimators with the maximum likelihood estimates is then made. The best linear unbiased predictors of censored failure times is then discussed briefly. Finally, two numerical examples are presented to illustrate all the inferential methods developed here.
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recurrence relations for moments of progressively censored order statistics from Logistic Distribution with applications to inference
Journal of Statistical Planning and Inference, 2011Co-Authors: N Balakrishnan, Essam K Alhussaini, H M SalehAbstract:Abstract In this paper, we establish several recurrence relations for the single and product moments of progressively Type-II right censored order statistics from a Logistic Distribution. The use of these relations in a systematic manner allows us to compute all the means, variances and covariances of progressively Type-II right censored order statistics from the Logistic Distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes ( R 1 , … , R m ) . The results established here generalize the corresponding results for the usual order statistics due to Shah, 1966 , Shah, 1970 . These moments are then utilized to derive best linear unbiased estimators of the location and scale parameters of the Logistic Distribution. A comparison of these estimators with the maximum likelihood estimations is then made. The best linear unbiased predictors of censored failure times are briefly discussed. Finally, an illustrative example is presented.
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moments and estimation from progressively censored data of half Logistic Distribution
International Journal of Reliability and Applications, 2006Co-Authors: K S Sultan, M R Mahmoud, H M SalehAbstract:【In this paper, we derive recurrence relations for the single and product moments of progressively Type-II right censored order statistics from half Logistic Distribution. Next, we derive the maximum likelihood estimators (MLEs) of the location and scale parameters of the half Logistic Distribution. In addition, we use the setup proposed by Balakrishnan and Aggarwala (2000) to compute the approximate best linear unbiased estimates (ABLUEs) of the location and scale parameters. Finally, we point out a simulation study to compare between the efficiency of the techniques considered for the estimation.】
N Balakrishnan - One of the best experts on this subject based on the ideXlab platform.
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recurrence relations for single and product moments of progressively type ii censored order statistics from generalized Logistic Distribution with applications to inference
Communications in Statistics - Simulation and Computation, 2017Co-Authors: N Balakrishnan, H M SalehAbstract:ABSTRACTIn this article, we establish several recurrence relations for the single and product moments of progressively Type-II right censored order statistics from a generalized Logistic Distribution. The use of these relations in a systematic manner allow us to compute all the means, variances, and covariances of progressively Type-II right censored order statistics from the generalized Logistic Distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes (R1, …, Rm). These moments are then utilized to derive best linear unbiased estimators of the scale and location-scale parameters of the generalized Logistic Distribution. A comparison of these estimators with the maximum likelihood estimates is then made through Monte Carlo simulations. Finally, the best linear unbiased predictors of censored failure times is discussed briefly.
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the generalized pascal triangle and the matrix variate jensen Logistic Distribution
Communications in Statistics-theory and Methods, 2015Co-Authors: Francisco J Carolopera, Graciela Gonzalezfarias, N BalakrishnanAbstract:This article defines the so called Generalized Matrix Variate Jensen-Logistic Distribution. The relevant applications of this class of Distributions in Configuration Shape Theory consist of a more efficient computation, supported by the corresponding inference. This demands the solution of two important problems: (1) the development of analytical and efficient formulae for their k-th derivatives and (2) the use of the derivatives to transform the configuration density into a polynomial density under some special matrix Kummer relation, indexed in this case by the Jensen-Logistic kernel. In this article, we solve these problems by deriving a simple formula for the k-th derivative of the density function, avoiding the usual partition theory framework and using a generalization of Pascal triangles. Then we apply the results by obtaining the associated Jensen-Logistic Kummer relations and the configuration polynomial density in the setting of Statistical Shape Theory.
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recurrence relations for single and product moments of progressively type ii censored order statistics from a generalized half Logistic Distribution with application to inference
Journal of Statistical Computation and Simulation, 2013Co-Authors: N Balakrishnan, H M SalehAbstract:In this paper, we establish several recurrence relations for the single and product moments of progressively Type-II right-censored order statistics from a generalized half-Logistic Distribution. The use of these relations in a systematic recursive manner enables the computation of all the means, variances, and covariances of progressively Type-II right-censored order statistics from the generalized half-Logistic Distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes (R 1, …, R m ). The results established here generalize the corresponding results for the usual order statistics due to Balakrishnan and Sandhu [Recurrence relations for single and product moments of order statistics from a generalized half-Logistic Distribution with applications to inference, J. Stat. Comput. Simul. 52 (1995), pp. 385–398.]. The moments so determined are then utilized to derive the best linear unbiased estimators of the scale and location–scale parameters of the generalized half-...
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relations for moments of progressively type ii censored order statistics from half Logistic Distribution with applications to inference
Computational Statistics & Data Analysis, 2011Co-Authors: N Balakrishnan, H M SalehAbstract:In this paper, we establish several recurrence relations for the single and product moments of progressively Type-II right censored order statistics from a half-Logistic Distribution. The use of these relations in a systematic recursive manner would enable one to compute all the means, variances and covariances of progressively Type-II right censored order statistics from the half-Logistic Distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes (R"1,...,R"m). The results established here generalize the corresponding results for the usual order statistics due to Balakrishnan (1985). These moments are then utilized to derive best linear unbiased estimators of the scale and location-scale parameters of the half-Logistic Distribution. A comparison of these estimators with the maximum likelihood estimates is then made. The best linear unbiased predictors of censored failure times is then discussed briefly. Finally, two numerical examples are presented to illustrate all the inferential methods developed here.
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recurrence relations for moments of progressively censored order statistics from Logistic Distribution with applications to inference
Journal of Statistical Planning and Inference, 2011Co-Authors: N Balakrishnan, Essam K Alhussaini, H M SalehAbstract:Abstract In this paper, we establish several recurrence relations for the single and product moments of progressively Type-II right censored order statistics from a Logistic Distribution. The use of these relations in a systematic manner allows us to compute all the means, variances and covariances of progressively Type-II right censored order statistics from the Logistic Distribution for all sample sizes n, effective sample sizes m, and all progressive censoring schemes ( R 1 , … , R m ) . The results established here generalize the corresponding results for the usual order statistics due to Shah, 1966 , Shah, 1970 . These moments are then utilized to derive best linear unbiased estimators of the location and scale parameters of the Logistic Distribution. A comparison of these estimators with the maximum likelihood estimations is then made. The best linear unbiased predictors of censored failure times are briefly discussed. Finally, an illustrative example is presented.
