This article introduces a three-parameter Lehman-type t distribution having 2 degrees of freedom,that is capable of fitting positive and negative skewed data sets.It is shown that the density and hazard functions of t...This article introduces a three-parameter Lehman-type t distribution having 2 degrees of freedom,that is capable of fitting positive and negative skewed data sets.It is shown that the density and hazard functions of the proposed distribution are uni-model.Ordinary moments,entropy measure,ordering,identifiability and order statistics are investigated.Since the quantile function is explicitly defined,quantile-based statistics are also discussed for the proposed distribution.These properties include measures of skewness and kurtosis,L-moments,quantile density and hazard functions,mean residual life function and Parzen's score function.Mechanisms of maximum likelihood,bias correction and matching of percentiles are employed for estimating the unknown parameters of the distribution.Simulation experiments are conducted to compare the performance of these three estimation methods.A real-life data set consisting of strength of glass fibres is fitted to show the adequacy of the proposed distribution over some extensions of the normal and t distributions.Parametric regression model is developed along with its parameter estimation using the maximum likelihood approach.Simulation study for the regression model is also presented that endorsed the asymptotic properties of the estimators.展开更多
Maximum likelihood and Bayes estimators of the parameters, survival function (SF) and hazard rate function (HRF) are obtained for the three-parameter exponentiated Burr type XII distribution when sample is available f...Maximum likelihood and Bayes estimators of the parameters, survival function (SF) and hazard rate function (HRF) are obtained for the three-parameter exponentiated Burr type XII distribution when sample is available from type II censored scheme. Bayes estimators have been developed using the standard Bayes and MCMC methods under square error and LINEX loss functions, using informative type of priors for the parameters. Simulation comparison of various estimation methods is made when n = 20, 40, 60 and censored data. The Bayes estimates are found to be, generally, better than the maximum likelihood estimates against the proposed prior, in the sense of having smaller mean square errors. This is found to be true whether the data are complete or censored. Estimates improve by increasing sample size. Analysis is also carried out for real life data.展开更多
基金the financial support from Science and Engineering Research Board,Department of Science&Technology,Government of India,under the scheme Early Career Research Award(file no.ECR/2017/002416)Dr.Sharma also acknowledges Banaras Hindu University,Varanasi,India,for providing financial support as seed grant under the Institute of Eminence Scheme(Scheme no.Dev.6031).
文摘This article introduces a three-parameter Lehman-type t distribution having 2 degrees of freedom,that is capable of fitting positive and negative skewed data sets.It is shown that the density and hazard functions of the proposed distribution are uni-model.Ordinary moments,entropy measure,ordering,identifiability and order statistics are investigated.Since the quantile function is explicitly defined,quantile-based statistics are also discussed for the proposed distribution.These properties include measures of skewness and kurtosis,L-moments,quantile density and hazard functions,mean residual life function and Parzen's score function.Mechanisms of maximum likelihood,bias correction and matching of percentiles are employed for estimating the unknown parameters of the distribution.Simulation experiments are conducted to compare the performance of these three estimation methods.A real-life data set consisting of strength of glass fibres is fitted to show the adequacy of the proposed distribution over some extensions of the normal and t distributions.Parametric regression model is developed along with its parameter estimation using the maximum likelihood approach.Simulation study for the regression model is also presented that endorsed the asymptotic properties of the estimators.
文摘Maximum likelihood and Bayes estimators of the parameters, survival function (SF) and hazard rate function (HRF) are obtained for the three-parameter exponentiated Burr type XII distribution when sample is available from type II censored scheme. Bayes estimators have been developed using the standard Bayes and MCMC methods under square error and LINEX loss functions, using informative type of priors for the parameters. Simulation comparison of various estimation methods is made when n = 20, 40, 60 and censored data. The Bayes estimates are found to be, generally, better than the maximum likelihood estimates against the proposed prior, in the sense of having smaller mean square errors. This is found to be true whether the data are complete or censored. Estimates improve by increasing sample size. Analysis is also carried out for real life data.
