Journal of the Royal Statistical Society: Series B

最新文章

• Perfect slice samplers AI翻译
  作者：Mira, A; Moller, J; Roberts, GO
  作者单位：Lancaster University; University of Insubria; Aalborg University
  摘要：Perfect sampling allows the exact simulation of random variables from the stationary measure of a Markov chain. By exploiting monotonicity properties of the slice sampler we show that a perfect version of the algorithm can be easily implemented, at least when the target distribution is bounded. Various extensions, including perfect product slice samplers, and examples of applications are discussed.
• A penalized likelihood approach to image warping - Discussion AI翻译
  作者：Molchanov, I; Jennison, C; Ersboll, BK; Hancock, E; Wilson, R; Horgan, G; Kent, JT; Ashburner, J; Dryden, IL; Petrou, M; Angulo, JM; Berman, M; Coleman, R; Duta, N; Jain, AK; Ghosh, JK; Murthy, CA; Gray, A; Gustafsson, J; Rudemo, M; Hogg, D; Koch, I; Linney, A; Ramsay, JO; Ramsay, TO; Rao, MM; Sebastiani, G; de Souza, K; Sun, CM; Buckley, M; Titterington, DM; Trubuil, A; Worsley, KJ; Yu, KM
  作者单位：University of Glasgow; University of Bath; Technical University of Denmark; University of York - UK; James Hutton Institute; University of Leeds; University of London; University College London; University of Nottingham; University of Surrey; University of Granada; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Imperial College London; Michigan State University; Indian Statistical Institute; Indian Statistical Institute Kolkata; University of Strathclyde; Chalmers University of Technology; University of Newcastle; University of London; University College London; McGill University; University of California System; University of California Riverside; INRAE; Universite Paris Saclay; University of Plymouth
• A penalized likelihood approach to image warping AI翻译
  作者：Glasbey, CA; Mardia, KV
  作者单位：James Hutton Institute; University of Leeds
  摘要：A warping is a function that deforms images by mapping between image domains. The choice of function is formulated statistically as maximum penalized likelihood, where the likelihood measures the similarity between images after warping and the penalty is a measure of distortion of a warping. The paper addresses two issues simultaneously, of how to choose the warping function and how to assess the alignment. A new, Fourier-von Mises image model is identified, with phase differences between Four...
• Moment estimation for statistics from marked point processes AI翻译
  作者：Politis, DN; Sherman, M
  作者单位：Texas A&M University System; Texas A&M University College Station; University of California System; University of California San Diego
  摘要：In spatial statistics the data typically consist of measurements of some quantity at irregularly scattered locations; in other words, the data form a realization of a marked point process. In this paper, we formulate subsampling estimators of the moments of general statistics computed from marked point process data, and we establish their L-2-consistency. The variance estimator in particular can be used for the construction of confidence intervals for estimated parameters. A practical data-bas...
• On repeated measures analysis with misspecified covariance structure AI翻译
  作者：Crowder, M
  作者单位：Imperial College London
  摘要：In recent years various sophisticated methods have been developed for the analysis of repeated measures. or longitudinal data. The more traditional approach, based on a normal likelihood function, has been shown to be unsatisfactory, in the sense of yielding asymptotically biased estimates when the covariance structure is misspecified. More recent methodology, based on generalized linear models and quasi-likelihood estimation, has gained widespread acceptance as 'generalized estimating equatio...
• Robust estimation for finite populations based on a working model AI翻译
  作者：Kuk, AYC; Welsh, AH
  作者单位：National University of Singapore; Australian National University
  摘要：A common scenario in finite population inference is that it is possible to find a working superpopulation model which explains the main features of the population but which may not capture all the fine details. In addition, there are often outliers in the population which do not follow the assumed superpopulation model. In situations like these, it is still advantageous to make use of the working model to estimate finite population quantities, provided that we do it in a robust manner. The app...
• Parametric models for response-biased sampling AI翻译
  作者：Chen, KN
  作者单位：Hong Kong University of Science & Technology
  摘要：Suppose that subjects in a population follow the model f (y*\x*; theta) where y* denotes a response, x* denotes a vector of covariates and theta is the parameter to be estimated. We consider response-biased sampling, in which a subject is observed with a probability which is a function of its response. Such response-biased sampling frequently occurs in econometrics, epidemiology and survey sampling. The semiparametric maximum likelihood estimate of theta is derived, along with its asymptotic n...
• Non-Gaussian Ornstein-Uhlenbeck-based models and some of their uses in financial economics - Discussion AI翻译
  作者：Hodges, SD; Roberts, G; Papaspiliopoulos, O; Sentana, E; Bingham, NH; Cox, DR; Nicolato, E; Venardos, E; Critchley, F; Davis, MHA; Tompkins, R; Benth, FE; Karlsen, KH; Reikvam, K; Brockwell, PJ; Davis, RA; Christensen, BJ; Dellaportas, P; McCoy, EJ; Stephens, DA; Diebold, FX; Frühwirth-Schnatter, S; Genon-Catalot, V; Larédo, C; Grange, CWJ; Griffin, JE; Steel, MFJ; Hobson, D; Jensen, JL; Jones, MC; Lawrance, AJ; Ledford, AW; Leonenko, NN; Levendorskii, S; Mandelbrot, BB; Meddahi, N; Pitt, MK; Priestley, MB; Renault, E; Rosinski, J; Sato, K; Taylor, SJ; Tong, H; Yang, H; Veretennikov, AY; Walker, SG; Werker, BJM; Wood, A
  作者单位：University of Warwick; Lancaster University; Brunel University; University of Oxford; Aarhus University; Open University - UK; Imperial College London; Technische Universitat Wien; University of Oslo; University of Bergen; Colorado State University System; Colorado State University Fort Collins; Athens University of Economics & Business; University of Pennsylvania; Universite Gustave-Eiffel; INRAE; Universite Paris Saclay; Sorbonne Universite; Universite Paris Cite; University of California System; University of California San Diego; University of Kent; University of Bath; University of Birmingham; University of Surrey; Cardiff University; Southern Federal University; Rostov State University of Economics; Yale University; Universite de Montreal; University of Manchester; University of Tennessee System; University of Tennessee Knoxville; Nagoya University; University of Hong Kong; University of London; London School Economics & Political Science; University of Leeds; Tilburg University; University of Nottingham
• Time series analysis of non-Gaussian observations based on state space models from both classical and Bayesian perspectives - Discussion on the paper by Durbin and Koopman AI翻译
  作者：Durbin, J; Koopman, SJ; Smith, JQ; Shephard, N; Chatfield, C; Young, P; Harvey, A; Bhansali, RJ; Sahu, SK; Doornik, JA; Nelder, JA; Pitt, MK; Aitkin, M; Bartlett, MS; Chan, K; Tong, H; Diebold, FX; van Dijk, HK; Fahrmeir, L; Frühwirth-Schnatter, S; Gamerman, D; Jacquier, E; Polson, NG; Jorgensen, B; Lundbye-Christensen, S; Kitagawa, G; Higuchi, T; Kumar, K; Lee, Y; Maravall, A; Quenneville, B; Thomson, P; Zellner, A
  作者单位：University of London; London School Economics & Political Science; Tilburg University
• Closure of the class of binary generalized linear models in some non-standard settings AI翻译
  作者：Neuhaus, JM
  作者单位：University of California System; University of California San Francisco
  摘要：This paper considers fitting generalized linear models to binary data in non-standard settings such as case-control samples, studies with misclassified responses and misspecified models. We develop simple methods for fitting models to case-control data and show that a closure property holds for generalized linear models in the non-standard settings, i.e., if the responses follow a generalized linear model in the population of interest, then so will the observed response in the non-standard set...