2000

最新文章

• 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...
• 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...