The Experts below are selected from a list of 19362 Experts worldwide ranked by ideXlab platform
Sanku Dey - One of the best experts on this subject based on the ideXlab platform.
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two parameter Rayleigh Distribution different methods of estimation
American Journal of Mathematical and Management Sciences, 2014Co-Authors: Sanku Dey, Tanujit Dey, Debasis KunduAbstract:SYNOPTIC ABSTRACTIn this study we have considered different methods of estimation of the unknown parameters of a two-parameter Rayleigh Distribution from both the frequentists' and the Bayesian view points. First, we briefly describe different frequentists' approaches: maximum likelihood estimators, method of moments estimators, L-moment estimators, percentile-based estimators, and least squares estimators, and we compare them using extensive numerical simulations. We have also considered Bayesian inferences of the unknown parameters. It is observed that the Bayes estimates and the associated credible intervals cannot be obtained in explicit forms, and we have suggested using an importance sampling technique to compute the Bayes estimates and the associated credible intervals. We analyze one dataset for illustrative purposes.
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statistical inference for the Rayleigh Distribution under progressively type ii censoring with binomial removal
Applied Mathematical Modelling, 2014Co-Authors: Sanku Dey, Tanujit DeyAbstract:Abstract This paper takes into account the estimation for the unknown parameter of the Rayleigh Distribution under Type II progressive censoring with binomial removals, where the number of units removed at each failure time follows a binomial Distribution. Maximum likelihood and Bayes procedure are used to derive both point and interval estimates of the parameters involved in the model. The expected termination point to complete the censoring test is computed and analyzed under binomial censoring scheme. Numerical examples are given to illustrate the approach by means of Monte Carlo simulation. A real life data set is used for illustrative purposes in conclusion.
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bayesian estimation and prediction intervals for a Rayleigh Distribution under a conjugate prior
Journal of Statistical Computation and Simulation, 2012Co-Authors: Sanku Dey, Tanujit DeyAbstract:This paper is an effort to obtain Bayes estimators of Rayleigh parameter and its associated risk based on a conjugate prior (square root inverted gamma prior) with respect to both symmetric loss function (squared error loss), and asymmetric loss function (precautionary loss function). We also derive the highest posterior density (HPD) interval for the Rayleigh parameter as well as the HPD prediction intervals for a future observation from this Distribution. An illustrative example to test how the Rayleigh Distribution fits a real data set is presented. Finally, Monte Carlo simulations are performed to compare the performances of the Bayes estimates under different conditions.
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bayesian estimation of the parameter and reliability function of an inverse Rayleigh Distribution
2012Co-Authors: Sanku DeyAbstract:In this paper we obtain Bayes’ estimators for the unknown parameter of an Inverse Rayleigh Distribution (IRD). Bayes estimators are obtained under symmetric (squared error (SE) loss) and asymmetric linear exponential loss functions using a non-informative prior. The performance of the estimators is assessed on the basis of their relative risk under the two loss functions. We also obtain the Bayes estimators of the reliability function using both symmetric as well as asymmetric loss functions and compare its performance based on a Monte Carlo simulation study. Finally, a numerical study is provided to illustrate the results.
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Comparison of Bayes Estimators of the Parameter and Reliability Function for Rayleigh Distribution under Different Loss Functions
2009Co-Authors: Sanku DeyAbstract:In this paper we derive Bayes’ estimators for the parameter and reliability function of the Rayleigh Distribution. These estimators are obtained on the basis of squared error loss function and LINEX loss function. Comparisons in terms of risks of those under linex loss and squared error loss functions with Bayes estimators relative to squared error loss function have been made. Finally, numerical study is given to illustrate the results.
Tanujit Dey - One of the best experts on this subject based on the ideXlab platform.
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two parameter Rayleigh Distribution different methods of estimation
American Journal of Mathematical and Management Sciences, 2014Co-Authors: Sanku Dey, Tanujit Dey, Debasis KunduAbstract:SYNOPTIC ABSTRACTIn this study we have considered different methods of estimation of the unknown parameters of a two-parameter Rayleigh Distribution from both the frequentists' and the Bayesian view points. First, we briefly describe different frequentists' approaches: maximum likelihood estimators, method of moments estimators, L-moment estimators, percentile-based estimators, and least squares estimators, and we compare them using extensive numerical simulations. We have also considered Bayesian inferences of the unknown parameters. It is observed that the Bayes estimates and the associated credible intervals cannot be obtained in explicit forms, and we have suggested using an importance sampling technique to compute the Bayes estimates and the associated credible intervals. We analyze one dataset for illustrative purposes.
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statistical inference for the Rayleigh Distribution under progressively type ii censoring with binomial removal
Applied Mathematical Modelling, 2014Co-Authors: Sanku Dey, Tanujit DeyAbstract:Abstract This paper takes into account the estimation for the unknown parameter of the Rayleigh Distribution under Type II progressive censoring with binomial removals, where the number of units removed at each failure time follows a binomial Distribution. Maximum likelihood and Bayes procedure are used to derive both point and interval estimates of the parameters involved in the model. The expected termination point to complete the censoring test is computed and analyzed under binomial censoring scheme. Numerical examples are given to illustrate the approach by means of Monte Carlo simulation. A real life data set is used for illustrative purposes in conclusion.
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bayesian estimation and prediction intervals for a Rayleigh Distribution under a conjugate prior
Journal of Statistical Computation and Simulation, 2012Co-Authors: Sanku Dey, Tanujit DeyAbstract:This paper is an effort to obtain Bayes estimators of Rayleigh parameter and its associated risk based on a conjugate prior (square root inverted gamma prior) with respect to both symmetric loss function (squared error loss), and asymmetric loss function (precautionary loss function). We also derive the highest posterior density (HPD) interval for the Rayleigh parameter as well as the HPD prediction intervals for a future observation from this Distribution. An illustrative example to test how the Rayleigh Distribution fits a real data set is presented. Finally, Monte Carlo simulations are performed to compare the performances of the Bayes estimates under different conditions.
Sukbok Kang - One of the best experts on this subject based on the ideXlab platform.
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estimation for the double Rayleigh Distribution based on multiply type ii censored samples
Communications for Statistical Applications and Methods, 2008Co-Authors: Juntae Han, Sukbok KangAbstract:In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and the location parameter in a double Rayleigh Distribution based on multiply Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples.
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amles for Rayleigh Distribution based on progressive type ii censored data
Communications for Statistical Applications and Methods, 2007Co-Authors: Eunhyung Seo, Sukbok KangAbstract:In this paper, we shall propose the AMLEs of the scale parameter and the location parameter in the two-parameter Rayleigh Distribution based on progressive Type-II censored samples when one parameter is known. We also propose the AMLEs of the two parameters in the Rayleigh Distribution based on progressive Type-II censored samples when two parameters are unknown. We simulate the mean squared errors of the proposed estimators through Monte Carlo simulation for various censoring schemes.
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estimation for the Rayleigh Distribution with known parameter under multiply type ii censoring
Journal of the Korean Data and Information Science Society, 2006Co-Authors: Juntae Han, Sukbok KangAbstract:For multiply Type-II censored samples from two-parameter Rayleigh Distribution, we derive some approximate maximum likelihood estimators of parameter in the Rayleigh Distribution when the other parameter is known. We also compare the proposed estimators in the sense of the mean squared error for various censored samples.
Debasis Kundu - One of the best experts on this subject based on the ideXlab platform.
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two parameter Rayleigh Distribution different methods of estimation
American Journal of Mathematical and Management Sciences, 2014Co-Authors: Sanku Dey, Tanujit Dey, Debasis KunduAbstract:SYNOPTIC ABSTRACTIn this study we have considered different methods of estimation of the unknown parameters of a two-parameter Rayleigh Distribution from both the frequentists' and the Bayesian view points. First, we briefly describe different frequentists' approaches: maximum likelihood estimators, method of moments estimators, L-moment estimators, percentile-based estimators, and least squares estimators, and we compare them using extensive numerical simulations. We have also considered Bayesian inferences of the unknown parameters. It is observed that the Bayes estimates and the associated credible intervals cannot be obtained in explicit forms, and we have suggested using an importance sampling technique to compute the Bayes estimates and the associated credible intervals. We analyze one dataset for illustrative purposes.
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generalized linear failure rate Distribution
Communications in Statistics-theory and Methods, 2009Co-Authors: Amma M Sarha, Debasis KunduAbstract:The exponential and Rayleigh are the two most commonly used Distributions for analyzing lifetime data. These Distributions have several desirable properties and nice physical interpretations. Unfortunately, the exponential Distribution only has constant failure rate and the Rayleigh Distribution has increasing failure rate. The linear failure rate Distribution generalizes both these Distributions which may have non increasing hazard function also. This article introduces a new Distribution, which generalizes linear failure rate Distribution. This Distribution generalizes the well-known (1) exponential Distribution, (2) linear failure rate Distribution, (3) generalized exponential Distribution, and (4) generalized Rayleigh Distribution. The properties of this Distribution are discussed in this article. The maximum likelihood estimates of the unknown parameters are obtained. A real data set is analyzed and it is observed that the present Distribution can provide a better fit than some other very well-known ...
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generalized Rayleigh Distribution
Computational Statistics & Data Analysis, 2005Co-Authors: Debasis Kundu, Mohammad Z RaqabAbstract:Recently, Surles and Padgett (Lifetime Data Anal., 187-200, 7, 2001) introduced two-parameter Burr Type X Distribution, which can also be described as generalized Rayleigh Distribution. It is observed that this particular skewed Distribution can be used quite effectively in analyzing lifetime data. Different estimation procedures have been used to estimate the unknown parameter(s) and their performances are compared using Monte Carlo simulations.
Juntae Han - One of the best experts on this subject based on the ideXlab platform.
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estimation for the double Rayleigh Distribution based on multiply type ii censored samples
Communications for Statistical Applications and Methods, 2008Co-Authors: Juntae Han, Sukbok KangAbstract:In this paper, we derive the approximate maximum likelihood estimators of the scale parameter and the location parameter in a double Rayleigh Distribution based on multiply Type-II censored samples. We compare the proposed estimators in the sense of the mean squared error for various censored samples.
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estimation for the Rayleigh Distribution with known parameter under multiply type ii censoring
Journal of the Korean Data and Information Science Society, 2006Co-Authors: Juntae Han, Sukbok KangAbstract:For multiply Type-II censored samples from two-parameter Rayleigh Distribution, we derive some approximate maximum likelihood estimators of parameter in the Rayleigh Distribution when the other parameter is known. We also compare the proposed estimators in the sense of the mean squared error for various censored samples.
