Journal of the Royal Statistical Society: Series B

最新文章

• Adaptive weights smoothing with applications to image restoration AI翻译
  作者：Polzehl, J; Spokoiny, VG
  作者单位：Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics
  摘要：We propose a new method of nonparametric estimation which is based on locally constant smoothing with an adaptive choice of weights for every pair of data points. Some theoretical properties of the procedure are investigated. Then we demonstrate the performance of the method on some simulated univariate and bivariate examples and compare it with other nonparametric methods. Finally we discuss applications of this procedure to magnetic resonance and satellite imaging.
• Extremal analysis of processes sampled at different frequencies AI翻译
  作者：Robinson, ME; Tawn, JA
  作者单位：Lancaster University; University of Surrey
  摘要：The observed extremes of a discrete time process depend on the process itself and the sampling frequency. We develop theoretical results which show how to account for the effect of sampling frequency on extreme values, thus enabling us to analyse systematically extremal data from series with different sampling rates. We present statistical methodology based on these results which we illustrate though simulations and by applications to sea-waves and rainfall data.
• Standard errors for EM estimation AI翻译
  作者：Jamshidian, M; Jennrich, RI
  作者单位：State University System of Florida; University of Central Florida; University of California System; University of California Los Angeles
  摘要：The EM algorithm is a popular method for computing maximum likelihood estimates. One of its drawbacks is that it does not produce standard errors as a by-product. We consider obtaining standard errors by numerical differentiation. Two approaches are considered. The first differentiates the Fisher score vector to yield the Hessian of the log-likelihood. The second differentiates the EM operator and uses an identity that relates its derivative to the Hessian of the log-likelihood. The well-known...
• Local likelihood smoothing of sample extremes AI翻译
  作者：Davison, AC; Ramesh, NI
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Greenwich
  摘要：Trends in sample extremes are of interest in many contexts, an example being environmental statistics. Parametric models are often used to model trends in such data, but they may not be suitable for exploratory data analysis. This paper outlines a semiparametric approach to smoothing sample extremes, based on local polynomial fitting of the generalized extreme value distribution and related models. The uncertainty of fits is assessed by using resampling methods. The methods are applied to data...
• Mixture Kalman filters AI翻译
  作者：Chen, R; Liu, JS
  作者单位：Texas A&M University System; Texas A&M University College Station; Stanford University
  摘要：In treating dynamic systems, sequential Monte Carte methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line 'filtering' task. We propose a special sequential Monte Carlo method, the mixture Kalman filter, which uses a random mixture of the Gaussian distributions to approximate a target distribution. It is designed for on-line estimation and prediction of conditional and part...
• Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method AI翻译
  作者：Robert, CP; Rydén, T; Titterington, DM
  作者单位：Lund University; Institut Polytechnique de Paris; ENSAE Paris; University of Glasgow
  摘要：Hidden Markov models form an extension of mixture models which provides a flexible class of models exhibiting dependence and a possibly large degree of variability. We show how reversible jump Markov chain Monte Carte techniques can be used to estimate the parameters as well as the number of components of a hidden Markov model in a Bayesian framework. We employ a mixture of zero-mean normal distributions as our main example and apply this model to three sets of data from finance, meteorology a...
• Perfect simulation of conditionally specified models AI翻译
  作者：Moller, J
  作者单位：Aalborg University
  摘要：We discuss how the ideas of producing perfect simulations based on coupling from the past for finite state space models naturally extend to multivariate distributions with infinite or uncountable state spaces such as autogamma, auto-Poisson and autonegative binomial models, using Gibbs sampling in combination with sandwiching methods originally introduced for perfect simulation of point processes.
• The achievable region approach to the optimal control of stochastic systems - Discussion AI翻译
  作者：Gibbens, RJ; Whittle, P; Ansell, P; Bather, J; Collins, EJ; Gittins, J; Zervos, M; Bäuerle, N; Birge, JR; Frostig, E; Weiss, G; McDiarmid, C; Möhring, RH; Uetz, M; Schulz, AS; Owen, RW; Gregorio-Domínguez, MM; Preater, J; Rieder, U; Stidham, S Jr; Thomas, L; Yao, DD
  作者单位：University of Cambridge; University of Newcastle; University of Sussex; University of Bristol; Ulm University; University of Michigan System; University of Michigan; University of Haifa; University of Oxford; Technical University of Berlin; Massachusetts Institute of Technology (MIT); University of Essex; Instituto Tecnologico Autonomo de Mexico; Keele University; University of North Carolina; University of North Carolina Chapel Hill; University of Edinburgh; Columbia University; University of Newcastle
• Bayesian nonparametric inference for random distributions and related functions AI翻译
  作者：Walker, SG; Damien, P; Laud, PW; Smith, AFM
  作者单位：Imperial College London; University of Michigan System; University of Michigan; Medical College of Wisconsin; University of London; Queen Mary University London
  摘要：In recent years, Bayesian nonparametric inference, both theoretical and computational, has witnessed considerable advances. However, these advances have not received a full critical and comparative analysis of their scope, impact and limitations in statistical modelling; many aspects of the theory and methods remain a mystery to practitioners and many open questions remain. In this paper, we discuss and illustrate the rich modelling and analytic possibilities that are available to the statisti...
• Upper probabilities based only on the likelihood function AI翻译
  作者：Walley, P; Moral, S
  作者单位：University of Granada
  摘要：In the problem of parametric statistical inference with a finite parameter space, we propose some simple rules far defining posterior upper and lower probabilities directly from the observed likelihood function, without using any prior information. The rules satisfy the likelihood principle and a basic consistency principle ('avoiding sure loss'), they produce vacuous inferences when the likelihood function is constant, and they have other symmetry, monotonicity and continuity properties. One ...