In this article,the empirical Bayes(EB)estimators are constructed for the estimable functions of the parameters in partitioned normal linear model.The superiorities of the EB estimators over ordinary least-squares...In this article,the empirical Bayes(EB)estimators are constructed for the estimable functions of the parameters in partitioned normal linear model.The superiorities of the EB estimators over ordinary least-squares(LS)estimator are investigated under mean square error matrix(MSEM)criterion.展开更多
In this paper, the Bayes estimator of the error variance is derived in a linear regression model, and the parametric empirical Bayes estimator (PEBE) is constructed. The superiority of the PEBE over the least square...In this paper, the Bayes estimator of the error variance is derived in a linear regression model, and the parametric empirical Bayes estimator (PEBE) is constructed. The superiority of the PEBE over the least squares estimator (LSE) is investigated under the mean square error (MSE) criterion. Finally, some simulation results for the PEBE are obtained.展开更多
In this paper we consider the empirical Bayes (EB) estimation problem for estimable function of regression coefficient in a multiple linear regression model Y=Xβ+e. where e with given β has a multivariate standard n...In this paper we consider the empirical Bayes (EB) estimation problem for estimable function of regression coefficient in a multiple linear regression model Y=Xβ+e. where e with given β has a multivariate standard normal distribution. We get the EB estimators by using kernel estimation of multivariate density function and its first order partial derivatives. It is shown that the convergence rates of the EB estimators are under the condition where an integer k > 1 . is an arbitrary small number and m is the dimension of the vector Y.展开更多
In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Poste...In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Posteriori(MAP)approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients.For the approx imation coefficients,a new fusion rule based on the Principal Component Analysis(PCA)is applied.We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method.The obt ained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics.Robustness of the proposed method is further tested against different types of noise.The plots of fusion met rics establish the accuracy of the proposed fusion method.展开更多
For the multi-parameter discrete exponential family,we construct an empirical Bayes(EB)estimator of the vector-valued parameterθ.under some conditions,this estimator is proved to be asymptotically optimal.
We present Bayes estimators, highest posterior density (HPD) intervals, and maximum likelihood estimators (MLEs), for the Maxwell failure distribution based on Type II censored data, i.e. using the first r lifetimes f...We present Bayes estimators, highest posterior density (HPD) intervals, and maximum likelihood estimators (MLEs), for the Maxwell failure distribution based on Type II censored data, i.e. using the first r lifetimes from a group of n components under test. Reliability/Hazard function estimates, Bayes predictive distributions and highest posterior density prediction intervals for a future observation are also considered. Two data examples and a Monte Carlo simulation study are used to illustrate the results and to compare the performances of the different methods.展开更多
The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies...The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This is based on the solution of an inverse generalized evaluate problem. The stochastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the engineer's confidence in the measurement frequencies is quantified and incorporated into the identification procedure. A numerical example demonstrates the efficiency of the method.展开更多
In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likeliho...In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.展开更多
The parameter estimation is considered for the Gompertz distribution under frequensitst and Bayes approaches when records are available.Maximum likelihood estimators,exact and approximate confidence intervals are deve...The parameter estimation is considered for the Gompertz distribution under frequensitst and Bayes approaches when records are available.Maximum likelihood estimators,exact and approximate confidence intervals are developed for the model parameters,and Bayes estimators of reliability performances are obtained under different losses based on a mixture of continuous and discrete priors.To investigate the performance of the proposed estimators,a record simulation algorithm is provided and a numerical study is presented by using Monte-Carlo simulation.展开更多
In this paper, the estimation of parameters based on a progressively typeI interval censored sample from a Pareto distribution is studied. Different methods of estimation are discussed, which include mid-point approxi...In this paper, the estimation of parameters based on a progressively typeI interval censored sample from a Pareto distribution is studied. Different methods of estimation are discussed, which include mid-point approximation estimator, the maximum likelihood estimator and moment estimator. The estimation procedures are discussed in details and compared via Monte Carlo simulations in terms of their biases.展开更多
In this paper we investigate the estimator for the rth power of the scale parameter in a class of exponential family under symmetric entropy loss L(θ, δ) = v(θ/δ + δ/θ - 2). An exact form of the minimum ris...In this paper we investigate the estimator for the rth power of the scale parameter in a class of exponential family under symmetric entropy loss L(θ, δ) = v(θ/δ + δ/θ - 2). An exact form of the minimum risk equivariant estimator under symmetric entropy loss is given, and the minimaxity of the minimum risk equivariant estimator is proved. The results with regard to admissibility and inadmissibility of a class of linear estimators of the form cT(X) + d are given, where T(X) Gamma(v, θ).展开更多
This paper describes a fundamental consideration on our works on the design of general Bayes' filters for the state estimation of non-stationary, non-linear, and non-Gaussian environmental sound and vibration syst...This paper describes a fundamental consideration on our works on the design of general Bayes' filters for the state estimation of non-stationary, non-linear, and non-Gaussian environmental sound and vibration systems. We have discussed an essential point of several Bayes' filters proposed by using the orthogonal or non-orthogonal expansion form of Bayes' theorem. They can estimate any kinds of statistics of arbitrary function type of state variables including the lower and the higher order statistics connected with the Lx evaluation index in the environmental sound and vibration systems. Here, we have mainly focussed on giving the fundamental viewpoints of their design policies. Some new estimation methods and new results not yet published are included.展开更多
In this work,we consider the problem of estimating the parameters and predicting the unobserved or removed ordered data for the progressive type II censored flexible Weibull sample.Frequentist and Bayesian analyses ar...In this work,we consider the problem of estimating the parameters and predicting the unobserved or removed ordered data for the progressive type II censored flexible Weibull sample.Frequentist and Bayesian analyses are adopted for conducting the estimation and prediction problems.The likelihood method as well as the Bayesian sampling techniques is applied for the inference problems.The point predictors and credible intervals of unobserved data based on an informative set of data are computed.Markov ChainMonte Carlo samples are performed to compare the so-obtained methods,and one real data set is analyzed for illustrative purposes.展开更多
A new one-parameter Chris-Jerry distribution,created by mixing exponential and gamma distributions,is discussed in this article in the presence of incomplete lifetime data.We examine a novel generalized progressively ...A new one-parameter Chris-Jerry distribution,created by mixing exponential and gamma distributions,is discussed in this article in the presence of incomplete lifetime data.We examine a novel generalized progressively hybrid censoring technique that ensures the experiment ends at a predefined period when the model of the test participants has a Chris-Jerry(CJ)distribution.When the indicated censored data is present,Bayes and likelihood estimations are used to explore the CJ parameter and reliability indices,including the hazard rate and reliability functions.We acquire the estimated asymptotic and credible confidence intervals of each unknown quantity.Additionally,via the squared-error loss,the Bayes’estimators are obtained using gamma prior.The Bayes estimators cannot be expressed theoretically since the likelihood density is created in a complex manner;nonetheless,Markov-chain Monte Carlo techniques can be used to evaluate them.The effectiveness of the investigated estimations is assessed,and some recommendations are given using Monte Carlo results.Ultimately,an analysis of two engineering applications,such as mechanical equipment and ball bearing data sets,shows the applicability of the proposed approaches that may be used in real-world settings.展开更多
The widespread use of Location-Based Services (LBSs), which allows untrusted service providers to collect large quantities of information regarding users' locations, has raised serious privacy concerns. In response...The widespread use of Location-Based Services (LBSs), which allows untrusted service providers to collect large quantities of information regarding users' locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms (LPPMs) have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary's estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.展开更多
In this article, the Bayes linear unbiased estimator (BALUE) of parameters is derived for the multivariate linear models. The superiorities of the BALUE over the least square estimator (LSE) is studied in terms of...In this article, the Bayes linear unbiased estimator (BALUE) of parameters is derived for the multivariate linear models. The superiorities of the BALUE over the least square estimator (LSE) is studied in terms of the mean square error matrix (MSEM) criterion and Bayesian Pitman closeness (PC) criterion.展开更多
In this paper, the multivariate linear model Y = XB+e, e ~ Nm×k(0, ImΣ) is considered from the Bayes perspective. Under the normal-inverse Wishart prior for (BΣ), the Bayes estimators are derived. The sup...In this paper, the multivariate linear model Y = XB+e, e ~ Nm×k(0, ImΣ) is considered from the Bayes perspective. Under the normal-inverse Wishart prior for (BΣ), the Bayes estimators are derived. The superiority of the Bayes estimators of B and Σ over the least squares estimators under the criteria of Bayes mean squared error (BMSE) and Bayes mean squared error matrix (BMSEM) is shown. In addition, the Pitman Closeness (PC) criterion is also included to investigate the superiority of the Bayes estimator of B.展开更多
In this paper Γ-minimax estimation of a multivariate normal mean under arbitrary squared error loss is considered where the covariance matrix of the normal distribution is a known symmetric and positive definite matr...In this paper Γ-minimax estimation of a multivariate normal mean under arbitrary squared error loss is considered where the covariance matrix of the normal distribution is a known symmetric and positive definite matrix with unknown multiple. The set F is fixed by imposing restrictions on the vector of first moments and on the matrix of second moments as well as on the first moment of the unknown factor determining of the covariance matrix. Necessary and sufficient conditions are derived which ensure that an estimator is Γ-minimax and that a prior is least favorable in Γ.展开更多
Based on lower record values,we first derive the exact explicit expressions as well as recurrence relations for the single and product moments of record values and then use these results to compute the means,variances...Based on lower record values,we first derive the exact explicit expressions as well as recurrence relations for the single and product moments of record values and then use these results to compute the means,variances and coefficient of skewness and kurtosis of exponentiated moment exponential distribution(EMED),a new extension of moment exponential distribution,recently introduced by Hasnain(Exponentiated moment exponential distribution.Ph.D.Thesis,2013).Next we obtain the maximum likelihood estimators of the unknown parameters and the approximate confidence intervals of the EMED.Finally,we consider Bayes estimation under the symmetric and asymmetric loss functions using gamma priors for both shape and scale parameters.We have also derived the Bayes interval of this distribution and discussed both frequentist and the Bayesian prediction intervals of the future record values based on the observed record values.Monte Carlo simulations are performed to compare the performances of the proposed methods,and a data set has been analyzed for illustrative purposes.展开更多
Let Y have an n-variate normal distribution with covariance matrim σ2I and mean vector Xβ,where X is a known n×p matrix. The problem of estimating θ=σ2+β′X′CXβ is studied. The admissibility and inadmissib...Let Y have an n-variate normal distribution with covariance matrim σ2I and mean vector Xβ,where X is a known n×p matrix. The problem of estimating θ=σ2+β′X′CXβ is studied. The admissibility and inadmissibility of the estimators of the form bS2+β′X′CXβ, where β= (X′X) ˉX′Y and S2=(Y-Xβ)′(Y-Xβ), are established. Another class of admissible quadratic estimators of θ is derived.展开更多
基金the Knowledge Innovation Program of the Chinese Academy of Sciences(KJCX3-SYW-S02)the Youth Foundation of USTC
文摘In this article,the empirical Bayes(EB)estimators are constructed for the estimable functions of the parameters in partitioned normal linear model.The superiorities of the EB estimators over ordinary least-squares(LS)estimator are investigated under mean square error matrix(MSEM)criterion.
文摘In this paper, the Bayes estimator of the error variance is derived in a linear regression model, and the parametric empirical Bayes estimator (PEBE) is constructed. The superiority of the PEBE over the least squares estimator (LSE) is investigated under the mean square error (MSE) criterion. Finally, some simulation results for the PEBE are obtained.
文摘In this paper we consider the empirical Bayes (EB) estimation problem for estimable function of regression coefficient in a multiple linear regression model Y=Xβ+e. where e with given β has a multivariate standard normal distribution. We get the EB estimators by using kernel estimation of multivariate density function and its first order partial derivatives. It is shown that the convergence rates of the EB estimators are under the condition where an integer k > 1 . is an arbitrary small number and m is the dimension of the vector Y.
文摘In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Posteriori(MAP)approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients.For the approx imation coefficients,a new fusion rule based on the Principal Component Analysis(PCA)is applied.We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method.The obt ained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics.Robustness of the proposed method is further tested against different types of noise.The plots of fusion met rics establish the accuracy of the proposed fusion method.
文摘For the multi-parameter discrete exponential family,we construct an empirical Bayes(EB)estimator of the vector-valued parameterθ.under some conditions,this estimator is proved to be asymptotically optimal.
文摘We present Bayes estimators, highest posterior density (HPD) intervals, and maximum likelihood estimators (MLEs), for the Maxwell failure distribution based on Type II censored data, i.e. using the first r lifetimes from a group of n components under test. Reliability/Hazard function estimates, Bayes predictive distributions and highest posterior density prediction intervals for a future observation are also considered. Two data examples and a Monte Carlo simulation study are used to illustrate the results and to compare the performances of the different methods.
文摘The Bayesian method of statistical analysis has been applied to the parameter identification problem. A method is presented to identify parameters of dynamic models with the Bayes estimators of measurement frequencies. This is based on the solution of an inverse generalized evaluate problem. The stochastic nature of test data is considered and a normal distribution is used for the measurement frequencies. An additional feature is that the engineer's confidence in the measurement frequencies is quantified and incorporated into the identification procedure. A numerical example demonstrates the efficiency of the method.
文摘In this paper, the estimators of the scale parameter of the exponential distribution obtained by applying four methods, using complete data, are critically examined and compared. These methods are the Maximum Likelihood Estimator (MLE), the Square-Error Loss Function (BSE), the Entropy Loss Function (BEN) and the Composite LINEX Loss Function (BCL). The performance of these four methods was compared based on three criteria: the Mean Square Error (MSE), the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Using Monte Carlo simulation based on relevant samples, the comparisons in this study suggest that the Bayesian method is better than the maximum likelihood estimator with respect to the estimation of the parameter that offers the smallest values of MSE, AIC, and BIC. Confidence intervals were then assessed to test the performance of the methods by comparing the 95% CI and average lengths (AL) for all estimation methods, showing that the Bayesian methods still offer the best performance in terms of generating the smallest ALs.
基金supported by the National Natural Science Foundation of China(1150143371473187)+1 种基金the Fundamental Research Funds for the Central Universities(JB1507177215591806)
文摘The parameter estimation is considered for the Gompertz distribution under frequensitst and Bayes approaches when records are available.Maximum likelihood estimators,exact and approximate confidence intervals are developed for the model parameters,and Bayes estimators of reliability performances are obtained under different losses based on a mixture of continuous and discrete priors.To investigate the performance of the proposed estimators,a record simulation algorithm is provided and a numerical study is presented by using Monte-Carlo simulation.
基金The NSF (11271155,11001105,11071126,10926156 and 11071269) of Chinathe Specialized Research Fund (20110061110003 and 20090061120037) for the Doctoral Program of Higher Education+1 种基金the General Humanities and Social Science Research Projects (11YJAZH125) Sponsored by Ministry of Educationthe Science and Technology Development Program (201201082) of Jilin Province
文摘In this paper, the estimation of parameters based on a progressively typeI interval censored sample from a Pareto distribution is studied. Different methods of estimation are discussed, which include mid-point approximation estimator, the maximum likelihood estimator and moment estimator. The estimation procedures are discussed in details and compared via Monte Carlo simulations in terms of their biases.
基金The SRFDPHE(20070183023)the NSF(10571073,J0630104)of China
文摘In this paper we investigate the estimator for the rth power of the scale parameter in a class of exponential family under symmetric entropy loss L(θ, δ) = v(θ/δ + δ/θ - 2). An exact form of the minimum risk equivariant estimator under symmetric entropy loss is given, and the minimaxity of the minimum risk equivariant estimator is proved. The results with regard to admissibility and inadmissibility of a class of linear estimators of the form cT(X) + d are given, where T(X) Gamma(v, θ).
文摘This paper describes a fundamental consideration on our works on the design of general Bayes' filters for the state estimation of non-stationary, non-linear, and non-Gaussian environmental sound and vibration systems. We have discussed an essential point of several Bayes' filters proposed by using the orthogonal or non-orthogonal expansion form of Bayes' theorem. They can estimate any kinds of statistics of arbitrary function type of state variables including the lower and the higher order statistics connected with the Lx evaluation index in the environmental sound and vibration systems. Here, we have mainly focussed on giving the fundamental viewpoints of their design policies. Some new estimation methods and new results not yet published are included.
文摘In this work,we consider the problem of estimating the parameters and predicting the unobserved or removed ordered data for the progressive type II censored flexible Weibull sample.Frequentist and Bayesian analyses are adopted for conducting the estimation and prediction problems.The likelihood method as well as the Bayesian sampling techniques is applied for the inference problems.The point predictors and credible intervals of unobserved data based on an informative set of data are computed.Markov ChainMonte Carlo samples are performed to compare the so-obtained methods,and one real data set is analyzed for illustrative purposes.
基金This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R50)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘A new one-parameter Chris-Jerry distribution,created by mixing exponential and gamma distributions,is discussed in this article in the presence of incomplete lifetime data.We examine a novel generalized progressively hybrid censoring technique that ensures the experiment ends at a predefined period when the model of the test participants has a Chris-Jerry(CJ)distribution.When the indicated censored data is present,Bayes and likelihood estimations are used to explore the CJ parameter and reliability indices,including the hazard rate and reliability functions.We acquire the estimated asymptotic and credible confidence intervals of each unknown quantity.Additionally,via the squared-error loss,the Bayes’estimators are obtained using gamma prior.The Bayes estimators cannot be expressed theoretically since the likelihood density is created in a complex manner;nonetheless,Markov-chain Monte Carlo techniques can be used to evaluate them.The effectiveness of the investigated estimations is assessed,and some recommendations are given using Monte Carlo results.Ultimately,an analysis of two engineering applications,such as mechanical equipment and ball bearing data sets,shows the applicability of the proposed approaches that may be used in real-world settings.
基金supported in part by the National Science and Technology Major Project (No. 2012ZX03002001004)the National Natural Science Foundation of China (Nos. 61172090, 61163009, and 61163010)+1 种基金the PhD Programs Foundation of Ministry of Education of China (No. 20120201110013)the Scientific and Technological Project in Shaanxi Province (Nos. 2012K06-30 and 2014JQ8322)
文摘The widespread use of Location-Based Services (LBSs), which allows untrusted service providers to collect large quantities of information regarding users' locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms (LPPMs) have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary's estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.
基金Supported by the National Natural Science Foundation of China (No.10801123,10801124,10771204)the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KJCX3-SYW-S02)
文摘In this article, the Bayes linear unbiased estimator (BALUE) of parameters is derived for the multivariate linear models. The superiorities of the BALUE over the least square estimator (LSE) is studied in terms of the mean square error matrix (MSEM) criterion and Bayesian Pitman closeness (PC) criterion.
基金Supported by National Natural Science Foundation of China(Grant Nos.11201005,11071015)the Foundation of National Bureau of Statistics(Grant No.2013LZ17)the Natural Science Foundation of Anhui Province(Grant No.1308085QA13)
文摘In this paper, the multivariate linear model Y = XB+e, e ~ Nm×k(0, ImΣ) is considered from the Bayes perspective. Under the normal-inverse Wishart prior for (BΣ), the Bayes estimators are derived. The superiority of the Bayes estimators of B and Σ over the least squares estimators under the criteria of Bayes mean squared error (BMSE) and Bayes mean squared error matrix (BMSEM) is shown. In addition, the Pitman Closeness (PC) criterion is also included to investigate the superiority of the Bayes estimator of B.
文摘In this paper Γ-minimax estimation of a multivariate normal mean under arbitrary squared error loss is considered where the covariance matrix of the normal distribution is a known symmetric and positive definite matrix with unknown multiple. The set F is fixed by imposing restrictions on the vector of first moments and on the matrix of second moments as well as on the first moment of the unknown factor determining of the covariance matrix. Necessary and sufficient conditions are derived which ensure that an estimator is Γ-minimax and that a prior is least favorable in Γ.
文摘Based on lower record values,we first derive the exact explicit expressions as well as recurrence relations for the single and product moments of record values and then use these results to compute the means,variances and coefficient of skewness and kurtosis of exponentiated moment exponential distribution(EMED),a new extension of moment exponential distribution,recently introduced by Hasnain(Exponentiated moment exponential distribution.Ph.D.Thesis,2013).Next we obtain the maximum likelihood estimators of the unknown parameters and the approximate confidence intervals of the EMED.Finally,we consider Bayes estimation under the symmetric and asymmetric loss functions using gamma priors for both shape and scale parameters.We have also derived the Bayes interval of this distribution and discussed both frequentist and the Bayesian prediction intervals of the future record values based on the observed record values.Monte Carlo simulations are performed to compare the performances of the proposed methods,and a data set has been analyzed for illustrative purposes.
文摘Let Y have an n-variate normal distribution with covariance matrim σ2I and mean vector Xβ,where X is a known n×p matrix. The problem of estimating θ=σ2+β′X′CXβ is studied. The admissibility and inadmissibility of the estimators of the form bS2+β′X′CXβ, where β= (X′X) ˉX′Y and S2=(Y-Xβ)′(Y-Xβ), are established. Another class of admissible quadratic estimators of θ is derived.