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 Author:
 Banerjee, Sudipto; Gelfand, Alan E.; Finley, Andrew O.; Sang, Huiyan
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 825848
 ISSN:
 13697412
 Subject:
 Markov chain; Monte Carlo method; data collection; models; spatial data
 Abstract:
 ... With scientific data available at geocoded locations, investigators are increasingly turning to spatial process models for carrying out statistical inference. Over the last decade, hierarchical models implemented through Markov chain Monte Carlo methods have become especially popular for spatial modelling, given their flexibility and power to fit models that would be infeasible with classical meth ...
 DOI:
 10.1111/j.14679868.2008.00663.x
 PubMed:
 19750209
 PubMed Central:
 PMC2741335

http://dx.doi.org/10.1111/j.14679868.2008.00663.x
 Author:
 Chen, Song Xi; Leung, Denis H.Y.; Qin, Jing
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 803823
 ISSN:
 13697412
 Subject:
 data collection; variance; United States
 Abstract:
 ... The paper considers estimating a parameter β that defines an estimating function U(y, x, β) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for β depends critically on correctly specifying t ...
 DOI:
 10.1111/j.14679868.2008.00662.x

http://dx.doi.org/10.1111/j.14679868.2008.00662.x
 Author:
 Powojowski, Miro R.
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 739753
 ISSN:
 13697412
 Subject:
 covariance; models; probability distribution
 Abstract:
 ... A class of additive covariance models of an isotropic random process is proposed, motivated by the spectral representation of the covariance function. Model parameters are estimated by using a special case of the minimum norm quadratic estimation estimator, whose asymptotic moments have convenient expressions in terms of spectral densities. Fitting a model in this class is equivalent to fitting an ...
 DOI:
 10.1111/j.14679868.2008.00653.x

http://dx.doi.org/10.1111/j.14679868.2008.00653.x
 Author:
 Fan, Jianqing; Wang, Mingjin; Yao, Qiwei
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 679702
 ISSN:
 13697412
 Subject:
 data collection; models; multivariate analysis
 Abstract:
 ... We propose to model multivariate volatility processes on the basis of the newly defined conditionally uncorrelated components (CUCs). This model represents a parsimonious representation for matrixvalued processes. It is flexible in the sense that each CUC may be fitted separately with any appropriate univariate volatility model. Computationally it splits one high dimensional optimization problem ...
 DOI:
 10.1111/j.14679868.2008.00654.x

http://dx.doi.org/10.1111/j.14679868.2008.00654.x
 Author:
 Hall, Peter; Müller, HansGeorg; Yao, Fang
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 703723
 ISSN:
 13697412
 Subject:
 cluster analysis; discriminant analysis; equations; models; prediction; principal component analysis
 Abstract:
 ... In longitudinal data analysis one frequently encounters nonGaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be binomial, Poisson or of another discrete type or could be continuous. The timings of the repeated measurements are often sparse and irregular. We introduce a latent Gaussian process model for such data, establishing a conn ...
 DOI:
 10.1111/j.14679868.2008.00656.x

http://dx.doi.org/10.1111/j.14679868.2008.00656.x
 Author:
 Hall, Peter; Maiti, Tapabrata
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 725738
 ISSN:
 13697412
 Subject:
 Poisson distribution; binomial distribution; models; normal distribution; prediction
 Abstract:
 ... Data in the form of pairs (X,Y), where the response Y is a count, arise in many applications, including problems involving stratified or twostage sampling. Such data are often analysed by using randomeffects models, where the distribution of Y, conditional on X and on an unobserved random parameter Θ, is taken to be either binomial or Poisson, and the distribution of Θ is connected through a lin ...
 DOI:
 10.1111/j.14679868.2008.00658.x

http://dx.doi.org/10.1111/j.14679868.2008.00658.x
 Author:
 Fearnhead, Paul; Papaspiliopoulos, Omiros; Roberts, Gareth O.
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 755777
 ISSN:
 13697412
 Subject:
 Markov chain; Monte Carlo method; Poisson distribution; filters; particles
 Abstract:
 ... We introduce a novel particle filter scheme for a class of partially observed multivariate diffusions. We consider a variety of observation schemes, including diffusion observed with error, observation of a subset of the components of the multivariate diffusion and arrival times of a Poisson process whose intensity is a known function of the diffusion (Cox process). Unlike currently available meth ...
 DOI:
 10.1111/j.14679868.2008.00661.x

http://dx.doi.org/10.1111/j.14679868.2008.00661.x
 Author:
 CuestaAlbertos, J.A.; Matrán, C.; MayoIscar, A.
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 779802
 ISSN:
 13697412
 Subject:
 algorithms; models; statistics
 Abstract:
 ... We introduce a robust estimation procedure that is based on the choice of a representative trimmed subsample through an initial robust clustering procedure, and subsequent improvements based on maximum likelihood. To obtain the initial trimming we resort to the trimmed kmeans, a simple procedure designed for finding the core of the clusters under appropriate configurations. By handling the trimme ...
 DOI:
 10.1111/j.14679868.2008.00657.x

http://dx.doi.org/10.1111/j.14679868.2008.00657.x
 Author:
 McCullagh, Peter
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 643677
 ISSN:
 13697412
 Subject:
 logit analysis; models; probability distribution
 Abstract:
 ... In a regression model, the joint distribution for each finite sample of units is determined by a function px(y) depending only on the list of covariate values x=(x(u₁),[ellipsis (horizontal)],x(un)) on the sampled units. No random sampling of units is involved. In biological work, random sampling is frequently unavoidable, in which case the joint distribution p(y,x) depends on the sampling scheme. ...
 DOI:
 10.1111/j.14679868.2007.00660.x

http://dx.doi.org/10.1111/j.14679868.2007.00660.x