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31.
Xiao Wang 《Revue canadienne de statistique》2009,37(1):102-118
The author considers estimation under a Gamma process model for degradation data. The setting for degradation data is one in which n independent units, each with a Gamma process with a common shape function and scale parameter, are observed at several possibly different times. Covariates can be incorporated into the model by taking the scale parameter as a function of the covariates. The author proposes using the maximum pseudo‐likelihood method to estimate the unknown parameters. The method requires usage of the Pool Adjacent Violators Algorithm. Asymptotic properties, including consistency, convergence rate and asymptotic distribution, are established. Simulation studies are conducted to validate the method and its application is illustrated by using bridge beams data and carbon‐film resistors data. The Canadian Journal of Statistics 37: 102‐118; 2009 © 2009 Statistical Society of Canada 相似文献
32.
The Multiple-Try Metropolis is a recent extension of the Metropolis algorithm in which the next state of the chain is selected
among a set of proposals. We propose a modification of the Multiple-Try Metropolis algorithm which allows for the use of correlated
proposals, particularly antithetic and stratified proposals. The method is particularly useful for random walk Metropolis
in high dimensional spaces and can be used easily when the proposal distribution is Gaussian. We explore the use of quasi
Monte Carlo (QMC) methods to generate highly stratified samples. A series of examples is presented to evaluate the potential
of the method. 相似文献
33.
A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces 总被引:2,自引:0,他引:2
Cajo J. F. Ter Braak 《Statistics and Computing》2006,16(3):239-249
Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical
context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian
analysis (after specifying prior and likelihood) using Markov Chain Monte Carlo (MCMC) simulation. This paper integrates the
essential ideas of DE and MCMC, resulting in Differential Evolution Markov Chain (DE-MC). DE-MC is a population MCMC algorithm,
in which multiple chains are run in parallel. DE-MC solves an important problem in MCMC, namely that of choosing an appropriate
scale and orientation for the jumping distribution. In DE-MC the jumps are simply a fixed multiple of the differences of two
random parameter vectors that are currently in the population. The selection process of DE-MC works via the usual Metropolis
ratio which defines the probability with which a proposal is accepted. In tests with known uncertainty distributions, the
efficiency of DE-MC with respect to random walk Metropolis with optimal multivariate Normal jumps ranged from 68% for small
population sizes to 100% for large population sizes and even to 500% for the 97.5% point of a variable from a 50-dimensional
Student distribution. Two Bayesian examples illustrate the potential of DE-MC in practice. DE-MC is shown to facilitate multidimensional
updates in a multi-chain “Metropolis-within-Gibbs” sampling approach. The advantage of DE-MC over conventional MCMC are simplicity,
speed of calculation and convergence, even for nearly collinear parameters and multimodal densities. 相似文献
34.
Sanjoy K. Sinha 《Revue canadienne de statistique》2008,36(2):277-299
The EM algorithm is often used for finding the maximum likelihood estimates in generalized linear models with incomplete data. In this article, the author presents a robust method in the framework of the maximum likelihood estimation for fitting generalized linear models when nonignorable covariates are missing. His robust approach is useful for downweighting any influential observations when estimating the model parameters. To avoid computational problems involving irreducibly high‐dimensional integrals, he adopts a Metropolis‐Hastings algorithm based on a Markov chain sampling method. He carries out simulations to investigate the behaviour of the robust estimates in the presence of outliers and missing covariates; furthermore, he compares these estimates to the classical maximum likelihood estimates. Finally, he illustrates his approach using data on the occurrence of delirium in patients operated on for abdominal aortic aneurysm. 相似文献
35.
36.
邹友金 《上海理工大学学报(社会科学版)》1989,(4)
本文在分析一维优化方法的比例因子法的基础上,提出了一种直接调整步长的最优步长因子法并给出框图。该法较比例因子法理论上完善,迭代控制可靠。 相似文献
37.
矿井掘进工作面安全评价的灰色聚类方法 总被引:2,自引:0,他引:2
本文用灰色聚类方法[1]讨论了煤矿掘进工作面安全评价问题,提出了一种新的安全评价方法─灰色聚类综合法。同时,本文对灰色聚类的算法作了改进,改进后的算法便于记忆和应用。 相似文献
38.
Jing Wang 《统计学通讯:模拟与计算》2013,42(3):539-556
In this article, we use two efficient approaches to deal with the difficulty in computing the intractable integrals when implementing Gibbs sampling in the nonlinear mixed effects model (NLMM) based on Dirichlet processes (DP). In the first approach, we compute the Laplace's approximation to the integral for its high accuracy, low cost, and ease of implementation. The second approach uses the no-gaps algorithm of MacEachern and Müller (1998) to perform Gibbs sampling without evaluating the difficult integral. We apply both approaches to real problems and simulations. Results show that both approaches perform well in density estimation and prediction and are superior to the parametric analysis in that they can detect important model features, such as skewness, long tails, and multimodality, whereas the parametric analysis cannot. 相似文献
39.
40.
This paper deals with the analysis of multivariate survival data from a Bayesian perspective using Markov-chain Monte Carlo methods. The Metropolis along with the Gibbs algorithm is used to calculate some of the marginal posterior distributions. A multivariate survival model is proposed, since survival times within the same group are correlated as a consequence of a frailty random block effect. The conditional proportional-hazards model of Clayton and Cuzick is used with a martingale structured prior process (Arjas and Gasbarra) for the discretized baseline hazard. Besides the calculation of the marginal posterior distributions of the parameters of interest, this paper presents some Bayesian EDA diagnostic techniques to detect model adequacy. The methodology is exemplified with kidney infection data where the times to infections within the same patients are expected to be correlated. 相似文献