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作者：Efron, B
作者单位：Stanford University; Stanford University
摘要：Survival analysis problems often involve dual timescales, most commonly calendar date and lifetime, the latter being the elapsed time since an initiating event such as a heart transplant. In our main example attention is focused on the hazard rate of 'death' as a function of calendar date. Three different estimates are discussed, one each from proportional hazards analyses on the lifetime and the calendar date scales, and one from a symmetric approach called here the 'two-way proportional haza...
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作者：Barber, S; Nason, GP; Silverman, BW
作者单位：University of Bristol
摘要：We use cumulants to derive Bayesian credible intervals for wavelet regression estimates. The first four cumulants of the posterior distribution of the estimates are expressed in terms of the observed data and integer powers of the mother wavelet functions. These powers are closely approximated by linear combinations of wavelet scaling functions at an appropriate finer scale. Hence, a suitable modification of the discrete wavelet transform allows the posterior cumulants to be found efficiently ...
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作者：Wood, S; Kohn, R; Shively, T; Jiang, WX
作者单位：University of New South Wales Sydney; University of Texas System; University of Texas Austin; Northwestern University
摘要：A Bayesian approach is presented for model selection in nonparametric regression with Gaussian errors and in binary nonparametric regression. A smoothness prior is assumed for each component of the model and the posterior probabilities of the candidate models are approximated using the Bayesian information criterion. We study the model selection method by simulation and show that it has excellent frequentist properties and gives improved estimates of the regression surface. All the computation...
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作者：Larget, B; Simon, DL; Kadane, JB
作者单位：Duquesne University; Carnegie Mellon University
摘要：The determination of evolutionary relationships is a fundamental problem in evolutionary biology. Genome arrangement data are potentially more informative than deoxyribonucleic acid sequence data for inferring evolutionary relationships between distantly related taxa. We describe a Bayesian framework for phylogenetic inference from mitochondrial genome arrangement data using Markov chain Monte Carlo methods. We apply the method to assess evolutionary relationships between eight animal phyla.
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作者：Delaigle, A; Gijbels, I
作者单位：Universite Catholique Louvain
摘要：We propose a kernel estimator of integrated squared density derivatives, from a sample that has been contaminated by random noise. We derive asymptotic expressions for the bias and the variance of the estimator and show that the squared bias term dominates the variance term. This coincides with results that are available for non-contaminated observations. We then discuss the selection of the bandwidth parameter when estimating integrated squared density derivatives based on contaminated data. ...
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作者：Brown, PJ; Vannucci, M; Fearn, T
作者单位：University of Kent; Texas A&M University System; Texas A&M University College Station; University of London; University College London
摘要：When a number of distinct models contend for use in prediction, the choice of a single model can offer rather unstable predictions. In regression, stochastic search variable selection with Bayesian model averaging offers a cure for this robustness issue but at the expense of requiring very many predictors. Here we look at Bayes model averaging incorporating variable selection for prediction. This offers similar mean-square errors of prediction but with a vastly reduced predictor space. This ca...
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作者：Fernández, C; Green, PJ
作者单位：Lancaster University; University of Bristol
摘要：The paper develops mixture models for spatially indexed data. We confine attention to the case of finite, typically irregular, patterns of points or regions with prescribed spatial relationships, and to problems where it is only the weights in the mixture that vary from one location to another. Our specific focus is on Poisson-distributed data, and applications in disease mapping. We work in a Bayesian framework, with the Poisson parameters drawn from gamma priors, and an unknown number of com...
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作者：Xia, YC; Tong, H; Li, WK; Zhu, LX
作者单位：University of Cambridge; University of Jinan; University of Hong Kong; University of London; London School Economics & Political Science; Chinese Academy of Sciences
摘要：Searching for an effective dimension reduction space is an important problem in regression, especially for high dimensional data. We propose an adaptive approach based on semiparametric models, which we call the (conditional) minimum average variance estimation (MAVE) method, within quite a general setting. The MAVE method has the following advantages. Most existing methods must undersmooth the nonparametric link function estimator to achieve a faster rate of consistency for the estimator of t...
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作者：Hall, P; Humphreys, K; Titterington, DM
作者单位：University of Glasgow; Australian National University; Karolinska Institutet
摘要：Variational methods have been proposed for obtaining deterministic lower bounds for log-likelihoods within missing data problems, but with little formal justification or investigation of the worth of the lower bound surfaces as tools for inference. We provide, within a general Markovian context, sufficient conditions under which estimators from the variational approximations are asymptotically equivalent to maximum likelihood estimators, and we show empirically, for the simple example of a fir...
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作者：Spiegelhalter, DJ; Best, NG; Carlin, BR; van der Linde, A
作者单位：University of Cambridge; MRC Biostatistics Unit; Imperial College London; University of Minnesota System; University of Minnesota Twin Cities; University of Bremen
摘要：We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure P-D for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general P-D approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which ...
