Jump to Main Content
PubAg
Main content area
Search
Search Results
 Author:
 Rasmussen, Jakob G.; Møller, Jesper; Aukema, Brian H.; Raffa, Kenneth F.; Zhu, Jun
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 701713
 ISSN:
 13697412
 Subject:
 Bayesian theory; data collection; models
 Abstract:
 ... We consider statistical and computational aspects of simulationbased Bayesian inference for a spatialtemporal model based on a multivariate point process which is only observed at sparsely distributed times. The point processes are indexed by the sites of a spatial lattice, and they exhibit spatial interaction. For specificity we consider a particular dynamical spatial lattice data set which has ...
 DOI:
 10.1111/j.14679868.2007.00608.x

http://dx.doi.org/10.1111/j.14679868.2007.00608.x
 Author:
 Lijoi, Antonio; Mena, Ramsés H.; Prünster, Igor
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 715740
 ISSN:
 13697412
 Subject:
 Bayesian theory; models; probability distribution
 Abstract:
 ... The paper deals with the problem of determining the number of components in a mixture model. We take a Bayesian nonparametric approach and adopt a hierarchical model with a suitable nonparametric prior for the latent structure. A commonly used model for such a problem is the mixture of Dirichlet process model. Here, we replace the Dirichlet process with a more general nonparametric prior obtain ...
 DOI:
 10.1111/j.14679868.2007.00609.x

http://dx.doi.org/10.1111/j.14679868.2007.00609.x
 Author:
 Roy, Vivekananda; Hobert, James P.
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 607623
 ISSN:
 13697412
 Subject:
 Markov chain; algorithms; normal distribution; variance
 Abstract:
 ... Consider a probit regression problem in which Y₁, [ellipsis (horizontal)], Yn are independent Bernoulli random variables such that [graphic removed] where xi is a pdimensional vector of known covariates that are associated with Yi, β is a pdimensional vector of unknown regression coefficients and Φ(·) denotes the standard normal distribution function. We study Markov chain Monte Carlo algorithms ...
 DOI:
 10.1111/j.14679868.2007.00602.x

http://dx.doi.org/10.1111/j.14679868.2007.00602.x
 Author:
 Cuzick, Jack; Sasieni, Peter; Myles, Jonathan; Tyrer, Jonathan
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 565588
 ISSN:
 13697412
 Subject:
 equations; models; risk
 Abstract:
 ... Methods for adjusting for noncompliance and contamination, which respect the randomization, are extended from binary outcomes to timetoevent analyses by using a proportional hazards model. A simple noniterative method is developed when there are no covariates, which is a generalization of the MantelHaenszel estimator. More generally, a 'partial likelihood' is developed which accommodates cova ...
 DOI:
 10.1111/j.14679868.2007.00600.x

http://dx.doi.org/10.1111/j.14679868.2007.00600.x
 Author:
 Chiou, JengMin; Li, PaiLing
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 679699
 ISSN:
 13697412
 Subject:
 algorithms; cluster analysis; covariance; gene expression; models; prediction; taxonomic revisions
 Abstract:
 ... A functional clustering (FC) method, kcentres FC, for longitudinal data is proposed. The kcentres FC approach accounts for both the means and the modes of variation differentials between clusters by predicting cluster membership with a reclassification step. The cluster membership predictions are based on a nonparametric randomeffect model of the truncated KarhunenLoève expansion, coupled wit ...
 DOI:
 10.1111/j.14679868.2007.00605.x

http://dx.doi.org/10.1111/j.14679868.2007.00605.x
 Author:
 Park, Mee Young; Hastie, Trevor
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 659677
 ISSN:
 13697412
 Subject:
 algorithms; data collection; linear models
 Abstract:
 ... We introduce a path following algorithm for L₁regularized generalized linear models. The L₁regularization procedure is useful especially because it, in effect, selects variables according to the amount of penalization on the L₁norm of the coefficients, in a manner that is less greedy than forward selectionbackward deletion. The generalized linear model path algorithm efficiently computes solut ...
 DOI:
 10.1111/j.14679868.2007.00607.x

http://dx.doi.org/10.1111/j.14679868.2007.00607.x
 Author:
 Zeng, D.; Lin, D.Y.
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 507564
 ISSN:
 13697412
 Subject:
 longitudinal studies; models; regression analysis
 Abstract:
 ... Semiparametric regression models play a central role in formulating the effects of covariates on potentially censored failure times and in the joint modelling of incomplete repeated measures and failure times in longitudinal studies. The presence of infinite dimensional parameters poses considerable theoretical and computational challenges in the statistical analysis of such models. We present sev ...
 DOI:
 10.1111/j.13697412.2007.00606.x

http://dx.doi.org/10.1111/j.13697412.2007.00606.x
 Author:
 Ma, Renjun; Jørgensen, Bent
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 625641
 ISSN:
 13697412
 Subject:
 algorithms; baking; epilepsy; models
 Abstract:
 ... We introduce a new class of generalized linear mixed models based on the Tweedie exponential dispersion model distributions, accommodating a wide range of discrete, continuous and mixed data. Using the best linear unbiased predictor of random effects, we obtain an optimal estimating function for the regression parameters in the sense of Godambe, allowing an efficient common fitting algorithm for t ...
 DOI:
 10.1111/j.14679868.2007.00603.x

http://dx.doi.org/10.1111/j.14679868.2007.00603.x
 Author:
 Fearnhead, Paul; Liu, Zhen
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 589605
 ISSN:
 13697412
 Subject:
 DNA; algorithms; humans; models
 Abstract:
 ... We propose an online algorithm for exact filtering of multiple changepoint problems. This algorithm enables simulation from the true joint posterior distribution of the number and position of the changepoints for a class of changepoint models. The computational cost of this exact algorithm is quadratic in the number of observations. We further show how resampling ideas from particle filters can b ...
 DOI:
 10.1111/j.14679868.2007.00601.x

http://dx.doi.org/10.1111/j.14679868.2007.00601.x
 Author:
 Genton, Marc G.; Hall, Peter
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 643657
 ISSN:
 13697412
 Subject:
 equations; models; statistics
 Abstract:
 ... In the study of variable stars, where the light reaching an observer fluctuates over time, it can be difficult to explain the nature of the variation unless it follows a regular pattern. In this respect, socalled periodic variable stars are particularly amenable to analysis. There, radiation varies in a perfectly periodic fashion, and period length is a major focus of interest. We develop methods ...
 DOI:
 10.1111/j.14679868.2007.00604.x

http://dx.doi.org/10.1111/j.14679868.2007.00604.x