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作者：Roberts, GO; Stramer, O
作者单位：Lancaster University; University of Iowa
摘要：In this paper, we introduce a new Markov chain Monte Carlo approach to Bayesian analysis of discretely observed diffusion processes. We treat the paths between any two data points as missing data. As such, we show that, because of full dependence between the missing paths and the volatility of the diffusion, the rate of convergence of basic algorithms can be arbitrarily slow if the amount of the augmentation is large. We offer a transformation of the diffusion which breaks down dependency betw...
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作者：Antoniadis, A; Sapatinas, T
作者单位：Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); University of Cyprus
摘要：We propose a wavelet shrinkage methodology for univariate natural exponential families with quadratic variance functions, covering the Gaussian, Poisson, gamma, binomial, negative binomial and generalised hyperbolic secant distributions. Simulation studies for Poisson and binomial data are used to illustrate the usefulness of the proposed methodology, and comparisons are made with other methods available in the literature. We also present applications to datasets arising from high-energy astro...
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作者：Jones, MC; Hjort, NL; Harris, IR; Basu, A
作者单位：Open University - UK; University of Oslo; Southern Methodist University; Indian Statistical Institute; Indian Statistical Institute Kolkata
摘要：This paper compares the minimum divergence estimator of Basu et al. (1998) to a competing minimum divergence estimator which turns out to be equivalent to a method proposed from a different perspective by Windham (1995). Both methods can be applied for any parametric model and contain maximum likelihood as a special case. Efficiencies are compared under model conditions, and robustness properties are studied. Overall the two methods are found to perform quite similarly. Some relatively small a...
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作者：Lieberman, O
作者单位：Technion Israel Institute of Technology
摘要：We apply and extend Firth's (1993) modified score estimator to deal with a class of stationary Gaussian long-memory processes. Our estimator removes the first-order bias of the maximum likelihood estimator. A small simulation study reveals the reduction in the bias is considerable, while it does not inflate the corresponding mean squared error.
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作者：Naik, PA; Tsai, CL
作者单位：University of California System; University of California Davis
摘要：We derive a new model selection criterion for single-index models, AIC(C), by minimising the expected Kullback-Leibler distance between the true and candidate models. The proposed criterion selects not only relevant variables but also the smoothing parameter for an unknown link function. Thus, it is a general selection criterion that provides a unified approach to model selection across both parametric and nonparametric functions. Monte Carlo studies demonstrate that AICC performs satisfactori...
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作者：Wu, WB; Woodroofe, M; Mentz, G
作者单位：University of Michigan System; University of Michigan
摘要：A test based on isotonic regression is developed for monotonic trends in short range dependent sequences and is applied to Argentina rainfall data and global warming data. This test provides another perspective for changepoint problems. The isotonic test is shown to be more powerful than some existing tests for trend.
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作者：Sweeting, TJ
作者单位：University of Surrey
摘要：We review objective Bayes procedures based on both parametric and predictive coverage probability bias and explore the extent to which such procedures contravene the likelihood principle in the case of a scalar parameter. The discussion encompasses choice of objective priors, objective posterior probability statements and objective predictive probability statements. We conclude with some remarks concerning the future development and implementation of objective priors based on small coverage pr...
