共查询到20条相似文献,搜索用时 15 毫秒
1.
In this paper Bayesian statistical analysis of masked data is
considered based on the Pareto distribution. The likelihood function is simplified by
introducing auxiliary variables, which describe the causes of failure. Three Bayesian
approaches (Bayes using subjective priors, hierarchical Bayes and empirical Bayes) are
utilized to estimate the parameters, and we compare these methods by analyzing a real
data. Finally we discuss the method of avoiding the choice of the hyperparameters in
the prior distributions. 相似文献
2.
Chih-Wen Hsu Marick S. Sinay John S. J. Hsu 《Annals of the Institute of Statistical Mathematics》2012,64(2):319-342
Bayesian analysis for a covariance structure has been in use for decades. The commonly adopted Bayesian setup involves the
conjugate inverse Wishart prior specification for the covariance matrix. Here we depart from this approach and adopt a novel
prior specification by considering a multivariate normal prior for the elements of the matrix logarithm of the covariance
structure. This specification allows for a richer class of prior distributions for the covariance structure with respect to
strength of beliefs in prior location hyperparameters and the added ability to model potential correlation amongst the covariance
structure. We provide three computational methods for calculating the posterior moment of the covariance matrix. The moments
of interest are calculated based upon computational results via Importance sampling, Laplacian approximation and Markov Chain
Monte Carlo/Metropolis–Hastings techniques. As a particular application of the proposed technique we investigate educational
test score data from the project talent data set. 相似文献
3.
In this paper we study varying‐coefficient models for count data. A Bayesian approach is taken to model the variability of the regression parameters. Based on a Kalman filter procedure the varying coefficients are estimated as the mode of the posterior distribution. All hyperparameters, including an overdispersion parameter in the negative binomial varying‐coefficient model (NBVC), are estimated as ML‐estimators using an EM‐type algorithm. A bootstrapping test of the fixed‐coefficient hypothesis against a varying‐coefficient alternative is proposed, which is evaluated running a simulation study. The study shows that the choice of a suitable count data model is of special importance in the framework of varying‐coefficient models. The methodology is illustrated analysing the determinants of the number of individual doctor visits. Copyright © 2001 John Wiley & Sons, Ltd. 相似文献
4.
Arjun K. Gupta Truc T. Nguyen Yinning Wang Jacek Weso
owski 《Journal of multivariate analysis》2001,77(2):509
Problems of specification of discrete bivariate statistical models by a modified power series conditional distribution and a regression function are studied. An identifiability result for a wide class of such mixtures with infinite support is obtained. Also the finite support case within a more specific model is considered. Applications for Poisson, (truncated) geometric, and binomial mixtures are given. From the viewpoint of Bayesian analysis unique determination of the prior by a Bayes estimate of the mean for modified power series mixtures is investigated. 相似文献
5.
6.
We analyze the reliability of NASA composite pressure vessels by using a new Bayesian semiparametric model. The data set consists of lifetimes of pressure vessels, wrapped with a Kevlar fiber, grouped by spool, subject to different stress levels; 10% of the data are right censored. The model that we consider is a regression on the log‐scale for the lifetimes, with fixed (stress) and random (spool) effects. The prior of the spool parameters is nonparametric, namely they are a sample from a normalized generalized gamma process, which encompasses the well‐known Dirichlet process. The nonparametric prior is assumed to robustify inferences to misspecification of the parametric prior. Here, this choice of likelihood and prior yields a new Bayesian model in reliability analysis. Via a Bayesian hierarchical approach, it is easy to analyze the reliability of the Kevlar fiber by predicting quantiles of the failure time when a new spool is selected at random from the population of spools. Moreover, for comparative purposes, we review the most interesting frequentist and Bayesian models analyzing this data set. Our credibility intervals of the quantiles of interest for a new random spool are narrower than those derived by previous Bayesian parametric literature, although the predictive goodness‐of‐fit performances are similar. Finally, as an original feature of our model, by means of the discreteness of the random‐effects distribution, we are able to cluster the spools into three different groups. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
7.
In this paper, mathematical properties of Lindley distribution via Bayesian approach are derived under different loss functions. These properties include: Bayes Estimators, posterior risks and failure rate function for simulation scheme. Elicitation of hyperparameters is also discussed. A real life application to waiting time data at the bank is also described for the developed procedures (also using WinBUGS). Results are compared on the basis of posterior risk. 相似文献
8.
Tzong-Ru Tsai Yuhlong Lio Nan Jiang Yu-Jau Lin Ya-Yen Fan 《The Journal of the Operational Research Society》2015,66(9):1511-1518
Economical sampling plans to ensure the qualities of Burr type XII distributed lifetimes were established using a truncated life test. The Bayesian inference method was used to address the lot-to-lot variation of products. The sampling plan was characterized by the sample size and the acceptance number to minimize the expected total cost. A simple empirical Bayesian estimation method was provided to estimate the hyperparameters of prior distribution, and simulation studies were conducted to validate the proposed empirical Bayesian estimation method. Lastly, the application of this proposed method was illustrated using two examples. 相似文献
9.
In Wilderness Search and Rescue (WiSAR), the incident commander (IC) creates a probability distribution map of the likely
location of the missing person. This map is important because it guides the IC in allocating search resources and coordinating
efforts, but it often depends almost exclusively on the missing person profile, prior experience, and subjective judgment.
We propose a Bayesian model that uses publicly available terrain features data to help model lost-person behaviors. This approach
enables domain experts to encode uncertainty in their prior estimations and also makes it possible to incorporate human behavior
data collected in the form of posterior distributions, which are used to build a first-order Markov transition matrix for
generating a temporal, posterior predictive probability distribution map. The map can work as a base to be augmented by search
and rescue workers to incorporate additional information. Using a Bayesian χ
2 test for goodness-of-fit, we show that the model fits a synthetic dataset well. This model also serves as a foundation for
a larger framework that allows for easy expansion to incorporate additional factors such as season and weather conditions
that affect the lost-person’s behaviors. 相似文献
10.
In the last paper, the geometry of the Sz.-Nagy-Foia
model for contraction operators on Hilbert spaces was used to advantage in several problems of multivariate analysis. The lifting of intertwining operators, one of the basic results from the Sz.-Nagy-Foia
theory, is now recognized as the most adequate operatorial form of the deep classical results of the extrapolation theory. The labeling of the exact intertwining dilations given by [1]Acta Sci. Math. (Szeged) 40 9–32] and the recursive methods used there open a broad perspective for using the Sz.-Nagy-Foia
model in multivariate filtering theory. In this paper, using the notion of correlated action (see [5 and 6] Rev. Roumaine Math. Pures Appl. 23, No. 9 1393–1423]) as a time domain, a linear filtering problem is formulated and its solution in terms of the coefficients of the analytic function which factorizes the spectral distribution of the known data and the coefficients of an analytic function which describes the cross correlations is given. In some special cases it is shown that the filter coefficients can be determined using recursive methods from the intertwining dilation theory, of the autocorrelation function of the known data and an intertwining operator, interpreted as the initial estimator given by the prior statistics. 相似文献
11.
Shuvashree
Mondal Ritwik Bhattacharya Biswabrata Pradhan Debasis Kundu 《商业与工业应用随机模型》2020,36(4):628-640
Joint progressive censoring schemes are quite useful to conduct comparative life‐testing experiment of different competing products. Recently, Mondal and Kundu (“A New Two Sample Type‐II Progressive Censoring Scheme,” Commun Stat‐Theory Methods; 2018) introduced a joint progressive censoring scheme on two samples known as the balanced joint progressive censoring (BJPC) scheme. Optimal planning of such progressive censoring scheme is an important issue to the experimenter. This article considers optimal life‐testing plan under the BJPC scheme using the Bayesian precision and D‐optimality criteria, assuming that the lifetimes follow Weibull distribution. In order to obtain the optimal BJPC life‐testing plans, one needs to carry out an exhaustive search within the set of all admissible plans under the BJPC scheme. However, for large sample size, determination of the optimal life‐testing plan is difficult by exhaustive search technique. A metaheuristic algorithm based on the variable neighborhood search method is employed for computation of the optimal life‐testing plan. Optimal plans are provided under different scenarios. The optimal plans depend upon the values of the hyperparameters of the prior distribution. The effect of different prior information on optimal scheme is studied. 相似文献
12.
A Bayesian approach is used to analyze the seismic events with magnitudes at least 4.7 on Taiwan. Following the idea proposed
by Ogata (1988,Journal of the American Statistical Association,83, 9–27), an epidemic model for the process of occurrence times given the observed magnitude values is considered, incorporated
with gamma prior distributions for the parameters in the model, while the hyper-parameters of the prior are essentially determined
by the seismic data in an earlier period. Bayesian inference is made on the conditional intensity function via Markov chain
Monte Carlo method. The results yield acceptable accuracies in predicting large earthquake events within short time periods. 相似文献
13.
Series models have several functions: comprehending the functional dependence of variable of interest on covariates, forecasting the dependent variable for future values of covariates and estimating variance disintegration, co-integration and steady-state relations. Although the regression function in a time series model has been extensively modeled both parametrically and nonparametrically, modeling of the error autocorrelation is mainly restricted to the parametric setup. A proper modeling of autocorrelation not only helps to reduce the bias in regression function estimate, but also enriches forecasting via a better forecast of the error term. In this article, we present a nonparametric modeling of autocorrelation function under a Bayesian framework. Moving into the frequency domain from the time domain, we introduce a Gaussian process prior to the log of the spectral density, which is then updated by using a Whittle approximation for the likelihood function (Whittle likelihood). The posterior computation is simplified due to the fact that Whittle likelihood is approximated by the likelihood of a normal mixture distribution with log-spectral density as a location shift parameter, where the mixture is of only five components with known means, variances, and mixture probabilities. The problem then becomes conjugate conditional on the mixture components, and a Gibbs sampler is used to initiate the unknown mixture components as latent variables. We present a simulation study for performance comparison, and apply our method to the two real data examples. 相似文献
14.
15.
《Journal of computational and graphical statistics》2013,22(1):116-138
Describing the structure in a two-way contingency table in terms of an RC(m) association model, we are concerned with the computation of posterior distributions of the model parameters using prior distributions which take into account the nonlinear restrictions of the model. We are further involved with the determination of the order of association m, based on Bayesian arguments. Using projection methods, a prior distribution over the parameters of the simpler RC(m) model is induced from a prior of the parameters of the saturated model. The fit of the assumed RC(m) model is evaluated using the posterior distribution of its distance from the full model. Our methods are illustrated with a popular dataset. 相似文献
16.
We consider Bayesian shrinkage predictions for the Normal regression problem under the frequentist Kullback-Leibler risk function.Firstly, we consider the multivariate Normal model with an unknown mean and a known covariance. While the unknown mean is fixed, the covariance of future samples can be different from that of training samples. We show that the Bayesian predictive distribution based on the uniform prior is dominated by that based on a class of priors if the prior distributions for the covariance and future covariance matrices are rotation invariant.Then, we consider a class of priors for the mean parameters depending on the future covariance matrix. With such a prior, we can construct a Bayesian predictive distribution dominating that based on the uniform prior.Lastly, applying this result to the prediction of response variables in the Normal linear regression model, we show that there exists a Bayesian predictive distribution dominating that based on the uniform prior. Minimaxity of these Bayesian predictions follows from these results. 相似文献
17.
在经济领域中,时间序列具有序列相关和长记忆等特征,用考虑了时间序列短记忆性和长记忆的ARFIMA来模型分析研究经济时间序列有利于提高拟合及预测的精度。近几十年来对ARFIMA模型参数估计和分数差分算子阶数d的研究越来越多,该模型的应用也越来越广泛。基于贝叶斯方法在参数估计中的优越性,本文结合众多应用此方法的文献所得到的后验分布特点,提出了合理的先验分布,考虑到计算难度,采用MCMC方法对模型的参数进行估计,最后应用我国过去几十年的GDP数据进行实证分析,得到了ARFIMA模型参数的后验分布图、均值、方差及95%的置信区间。 相似文献
18.
19.
Felix Abramovich Claudia Angelini Daniela De Canditiis 《Annals of the Institute of Statistical Mathematics》2007,59(3):425-434
We consider pointwise mean squared errors of several known Bayesian wavelet estimators, namely, posterior mean, posterior
median and Bayes Factor, where the prior imposed on wavelet coefficients is a mixture of an atom of probability zero and a
Gaussian density. We show that for the properly chosen hyperparameters of the prior, all the three estimators are (up to a
log-factor) asymptotically minimax within any prescribed Besov ball
. We discuss the Bayesian paradox and compare the results for the pointwise squared risk with those for the global mean squared
error. 相似文献
20.
In this article we provide a Bayesian analysis for dependent elliptical measurement error models considering nondifferential and differential errors. In both cases we compute posterior distributions for structural parameters by using squared radial prior distributions for the precision parameters. The main result is that the posterior distribution of location parameters, for specific priors, is invariant with respect to changes in the generator function, in agreement with previous results obtained in the literature under different assumptions. Finally, although the results obtained are valid for any elliptical distribution for the error term, we illustrate those results by using the student-t distribution and a real data set. 相似文献