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 Author:
 Niu, Mu; Cheung, Pokman; Lin, Lizhen; Dai, Zhenwen; Lawrence, Neil; Dunson, David
 Source:
 Journal of the Royal Statistical Society 2019 v.81 no.3 pp. 603627
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; covariance; equations; geometry; heat; models; Show all 6 Subjects
 Abstract:
 ... We propose a class of intrinsic Gaussian processes (GPs) for interpolation, regression and classification on manifolds with a primary focus on complex constrained domains or irregularly shaped spaces arising as subsets or submanifolds of R, R2, R3 and beyond. For example, intrinsic GPs can accommodate spatial domains arising as complex subsets of Euclidean space. Intrinsic GPs respect the potentia ...
 DOI:
 10.1111/rssb.12320

http://dx.doi.org/10.1111/rssb.12320
 Author:
 Yao, Shun; Zhang, Xianyang; Shao, Xiaofeng
 Source:
 Journal of the Royal Statistical Society 2018 v.80 no.3 pp. 455480
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; covariance; equations; models; Show all 4 Subjects
 Abstract:
 ... We introduce an L2‐type test for testing mutual independence and banded dependence structure for high dimensional data. The test is constructed on the basis of the pairwise distance covariance and it accounts for the non‐linear and non‐monotone dependences among the data, which cannot be fully captured by the existing tests based on either Pearson correlation or rank correlation. Our test can be c ...
 DOI:
 10.1111/rssb.12259

http://dx.doi.org/10.1111/rssb.12259
 Author:
 Gonçalves, Flávio B.; Gamerman, Dani
 Source:
 Journal of the Royal Statistical Society 2018 v.80 no.1 pp. 157175
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; Bayesian theory; Markov chain; algorithms; equations; models; Show all 6 Subjects
 Abstract:
 ... We present a novel inference methodology to perform Bayesian inference for spatiotemporal Cox processes where the intensity function depends on a multivariate Gaussian process. Dynamic Gaussian processes are introduced to enable evolution of the intensity function over discrete time. The novelty of the method lies on the fact that no discretization error is involved despite the non‐tractability of ...
 DOI:
 10.1111/rssb.12237

http://dx.doi.org/10.1111/rssb.12237
 Author:
 Zhu, Yunzhang; Li, Lexin
 Source:
 Journal of the Royal Statistical Society 2018 v.80 no.5 pp. 927950
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; algorithms; business enterprises; equations; graphs; magnetic resonance imaging; models; probability; Show all 8 Subjects
 Abstract:
 ... Matrix‐valued data, where the sampling unit is a matrix consisting of rows and columns of measurements, are emerging in numerous scientific and business applications. Matrix Gaussian graphical models are a useful tool to characterize the conditional dependence structure of rows and columns. We employ non‐convex penalization to tackle the estimation of multiple graphs from matrix‐valued data under ...
 DOI:
 10.1111/rssb.12278

http://dx.doi.org/10.1111/rssb.12278
 Author:
 Botev, Z. I.
 Source:
 Journal of the Royal Statistical Society 2017 v.79 no.1 pp. 125148
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; Markov chain; equations; methodology; models; regression analysis; Show all 6 Subjects
 Abstract:
 ... Simulation from the truncated multivariate normal distribution in high dimensions is a recurrent problem in statistical computing and is typically only feasible by using approximate Markov chain Monte Carlo sampling. We propose a minimax tilting method for exact independently and identically distributed data simulation from the truncated multivariate normal distribution. The new methodology provid ...
 DOI:
 10.1111/rssb.12162

http://dx.doi.org/10.1111/rssb.12162
 Author:
 Hauser, Alain; Bühlmann, Peter
 Source:
 Journal of the Royal Statistical Society 2015 v.77 no.1 pp. 291318
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; Markov chain; models; Show all 3 Subjects
 Abstract:
 ... In many applications we have both observational and (randomized) interventional data. We propose a Gaussian likelihood framework for joint modelling of such different data types, based on global parameters consisting of a directed acyclic graph and corresponding edge weights and error variances. Thanks to the global nature of the parameters, maximum likelihood estimation is reasonable with only on ...
 DOI:
 10.1111/rssb.12071

http://dx.doi.org/10.1111/rssb.12071
 Author:
 Khare, Kshitij; Oh, Sang‐Yun; Rajaratnam, Bala
 Source:
 Journal of the Royal Statistical Society 2015 v.77 no.4 pp. 803825
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; covariance; models; regression analysis; Show all 4 Subjects
 Abstract:
 ... Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l₁‐penalties to either parametric likelihoods, or regularized regression/pseudolikelihoods, with the latter having the distinct advantage that they do not explicitly assume Gaussianity. As none of the popular methods proposed for solving pseudolikelihood‐based obje ...
 DOI:
 10.1111/rssb.12088

http://dx.doi.org/10.1111/rssb.12088
 Author:
 Papageorgiou, Georgios; Richardson, Sylvia; Best, Nicky
 Source:
 Journal of the Royal Statistical Society 2015 v.77 no.5 pp. 973999
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; data collection; epidemiology; models; risk factors; Show all 5 Subjects
 Abstract:
 ... We develop Bayesian non‐parametric models for spatially indexed data of mixed type. Our work is motivated by challenges that occur in environmental epidemiology, where the usual presence of several confounding variables that exhibit complex interactions and high correlations makes it difficult to estimate and understand the effects of risk factors on health outcomes of interest. The modelling appr ...
 DOI:
 10.1111/rssb.12097

http://dx.doi.org/10.1111/rssb.12097
 Author:
 Allen, Genevera I.; Tibshirani, Robert
 Source:
 Journal of the Royal Statistical Society 2012 v.74 no.4 pp. 721743
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; genes; microarray technology; models; variance; Show all 5 Subjects
 Abstract:
 ... We consider the problem of largeâscale inference on the row or column variables of data in the form of a matrix. Many of these data matrices are transposable meaning that neither the row variables nor the column variables can be considered independent instances. An example of this scenario is detecting significant genes in microarrays when the samples may be dependent because of latent variables ...
 DOI:
 10.1111/j.14679868.2011.01027.x

http://dx.doi.org/10.1111/j.14679868.2011.01027.x
 Author:
 Delaigle, Aurore; Hall, Peter; Jin, Jiashun
 Source:
 Journal of the Royal Statistical Society 2011 v.73 no.3 pp. 283301
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; equations; methodology; ttest; Show all 4 Subjects
 Abstract:
 ... Student's tstatistic is finding applications today that were never envisaged when it was introduced more than a century ago. Many of these applications rely on properties, e.g. robustness against heavytailed sampling distributions, that were not explicitly considered until relatively recently. We explore these features of the tstatistic in the context of its application to very high dimensional ...
 DOI:
 10.1111/j.14679868.2010.00761.x

http://dx.doi.org/10.1111/j.14679868.2010.00761.x
 Author:
 Riani, Marco; Atkinson, Anthony C.; Cerioli, Andrea
 Source:
 Journal of the Royal Statistical Society 2009 v.71 no.2 pp. 447466
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; equations; multivariate analysis; Show all 3 Subjects
 Abstract:
 ... We use the forward search to provide robust Mahalanobis distances to detect the presence of outliers in a sample of multivariate normal data. Theoretical results on order statistics and on estimation in truncated samples provide the distribution of our test statistic. We also introduce several new robust distances with associated distributional results. Comparisons of our procedure with tests usin ...
 DOI:
 10.1111/j.14679868.2008.00692.x

http://dx.doi.org/10.1111/j.14679868.2008.00692.x
 Author:
 Dette, Holger; Volgushev, Stanislav
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.3 pp. 609627
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; regression analysis; variance; Show all 3 Subjects
 Abstract:
 ... Since the introduction by Koenker and Bassett, quantile regression has become increasingly important in many applications. However, many nonparametric conditional quantile estimates yield crossing quantile curves (calculated for various p [set membership] (0, 1)). We propose a new nonparametric estimate of conditional quantiles that avoids this problem. The method uses an initial estimate of the ...
 DOI:
 10.1111/j.14679868.2008.00651.x

http://dx.doi.org/10.1111/j.14679868.2008.00651.x
 Author:
 Drton, Mathias; Richardson, Thomas S.
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.2 pp. 287309
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; algorithms; linear models; Show all 3 Subjects
 Abstract:
 ... Loglinear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical loglinear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class t ...
 DOI:
 10.1111/j.14679868.2007.00636.x

http://dx.doi.org/10.1111/j.14679868.2007.00636.x
 Author:
 Ferreira, Marco A. R.; Holan, Scott H.; Bertolde, Adelmo I.
 Source:
 Journal of the Royal Statistical Society 2011 v.73 no.5 pp. 663688
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; algorithms; equations; heteroskedasticity; models; mortality; Brazil; Missouri; Show all 8 Subjects
 Abstract:
 ... We introduce a new class of dynamic multiscale models for spatiotemporal processes arising from Gaussian areal data. Specifically, we use nested geographical structures to decompose the original process into multiscale coefficients which evolve through time following state space equations. Our approach naturally accommodates data that are observed on irregular grids as well as heteroscedasticity. ...
 DOI:
 10.1111/j.14679868.2011.00774.x

http://dx.doi.org/10.1111/j.14679868.2011.00774.x
 Author:
 Sang, Huiyan; Huang, Jianhua Z.
 Source:
 Journal of the Royal Statistical Society 2012 v.74 no.1 pp. 111132
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; Bayesian theory; covariance; data collection; models; prediction; spatial data; Show all 7 Subjects
 Abstract:
 ... Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations ...
 DOI:
 10.1111/j.14679868.2011.01007.x

http://dx.doi.org/10.1111/j.14679868.2011.01007.x
 Author:
 Roy, Vivekananda; Hobert, James P.
 Source:
 Journal of the Royal Statistical Society 2007 v.69 no.4 pp. 607623
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; Markov chain; algorithms; variance; Show all 4 Subjects
 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:
 Hall, Peter; Maiti, Tapabrata
 Source:
 Journal of the Royal Statistical Society 2008 v.70 no.4 pp. 725738
 ISSN:
 13697412
 Subject:
 normal distribution, etc ; Poisson distribution; binomial distribution; models; prediction; Show all 5 Subjects
 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