Journal of Computational & Graphical Statistics articles from June 2000

Journal of Computational & Graphical Statistics archives from June 2000

Structured Markov Chain Monte Carlo.
June 1, 2000... This article introduces a general method for Bayesian computing in richly parameterized models, structured Markov chain Monte Carlo (SMCMC), that is based on a blocked hybrid of the Gibbs sampling and Metropolis--Hastings algorithms. SMCMC...

Posterior Simulation With Priors Specified on Functionals.
June 1, 2000... Many Bayesian analyses use Markov chain Monte Carlo (MCMC) techniques. MCMC techniques work fastest (per iteration) when the prior distribution of the parameters is chosen conveniently, such as a conjugate prior. However, this is sometimes at...

Markov Chain Sampling Methods for Dirichlet Process Mixture Models.
June 1, 2000... This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to make Metropolis-Hastings updates of the indicators...

Markov Chain Monte Carlo Convergence Assessment via Two-Way Analysis of Variance.
June 1, 2000... In this article we discuss the problem of assessing the performance of Markov chain Monte Carlo (MCMC) algorithms on the basis of simulation output. In essence, we extend the original ideas of Gelman and Rubin and, more recently, Brooks and...

Generation of "Similar" Images From a Given Discrete Image.
June 1, 2000... A discrete image of several colors is viewed as a discrete random field obtained by clipping or quantizing a Gaussian random field at several levels. Given a discrete image, parameters of the unobserved original Gaussian random field are...

An Interval Analysis Approach to the EM Algorithm.
June 1, 2000... The EM algorithm is widely used in incomplete-data problems (and some complete-data problems) for parameter estimation. One limitation of the EM algorithm is that, upon termination, it is not always near a global optimum. As reported by Wu...

On the LASSO and its Dual.
June 1, 2000... Proposed by Tibshirani, the least absolute shrinkage and selection operator (LASSO) estimates a vector of regression coefficients by minimizing the residual sum of squares subject to a constraint on the [l.sup.1]-norm of the coefficient vector....

Data Adaptive Ridging in Local Polynomial Regression.
June 1, 2000... When estimating a regression function or its derivatives, local polynomials are an attractive choice due to their flexibility and asymptotic performance. Seifert and Gasser proposed ridging of local polynomials to overcome problems with...

Block Coordinate Relaxation Methods for Nonparametric Wavelet Denoising.
June 1, 2000... An important class of nonparametric signal processing methods entails forming a set of predictors from an overcomplete set of basis functions associated with a fast transform (e.g., wavelet packets). In these methods, the number of basis...

Simulation From Wishart Distributions With Eigenvalue Constraints.
June 1, 2000... This article provides an efficient algorithm for generating a random matrix according to a Wishart distribution, but with eigenvalues constrained to be less than a given vector of positive values. The procedure of Odell and Feiveson provides a...

Parametric Estimation of a Boolean Segment Process With Stochastic Restoration Estimation.
June 1, 2000... We propose a stochastic restoration estimation (SRE) algorithm to estimate the parameters of the length distribution of a boolean segment process. A boolean segment process is a stochastic process obtained by considering the union of...