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作者：Paparoditis, E; Politis, DN
作者单位：University of Cyprus; University of California System; University of California San Diego
摘要：We introduce and study tapered block bootstrap methodology that yields an improvement over the well-known block bootstrap for time series of Kunsch (1989). The asymptotic validity and the favourable bias properties of the tapered block bootstrap are shown. The important practical issues of optimally choosing the window shape and the block size are addressed in detail, while some finite-sample simulations are presented validating the good performance of the tapered block bootstrap.
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作者：Van Lieshout, MNM; Molchanov, IS; Zuyev, SA
作者单位：Centrum Wiskunde & Informatica (CWI); University of Glasgow; University of Strathclyde
摘要：We formulate clustering as a minimisation problem in the space of measures by modelling the cluster centres as a Poisson process with unknown intensity function. We derive a Ward-type clustering criterion which, under the Poisson assumption, can easily be evaluated explicitly in terms of the intensity function. We show that asymptotically, i.e. for increasing total intensity, the optimal intensity function is proportional to a dimension-dependent power of the density of the observations. For f...
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作者：Li, WK; Ling, SQ; Wong, H
作者单位：University of Hong Kong; Hong Kong University of Science & Technology; Hong Kong Polytechnic University
摘要：This paper investigates a partially nonstationary multivariate autoregressive model, which allows its innovations to be generated by a multivariate ARCH, autoregressive conditional heteroscedastic, process. Three estimators, including the least squares estimator, a full-rank maximum likelihood estimator and a reduced-rank maximum likelihood estimator, are considered and their asymptotic distributions are derived. When the multivariate ARCH process reduces to the innovation with a constant cova...
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作者：Pfeiffer, RM; Gail, MH; Pee, D
作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); Information Management Services, Inc.
摘要：Family studies to identify disease-related genes often collect families with multiple cases. If environmental exposures or other measured covariates are also important, they should be incorporated into these genetic analyses to control for confounding and increase statistical power. We propose a two-level mixed effects model that allows us to estimate environmental effects while accounting for varying genetic correlations among family members and adjusting for ascertainment by conditioning on ...
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作者：Liseo, B; Marinucci, D; Petrella, L
作者单位：Sapienza University Rome
摘要：We develop a Bayesian semiparametric procedure for the analysis of stationary long-range dependent time series, We use frequency domain methods to partition the infinite-dimensional parameter space into regions where genuine prior information on the form of the spectral density is available, and others where vague prior beliefs are adopted; the solution to the partition problem, which is equivalent to bandwidth choice from a frequentist point of view, is obtained via Bayes factors. We derive a...
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作者：DiMatteo, I; Genovese, CR; Kass, RE
作者单位：Carnegie Mellon University
摘要：We describe a Bayesian method, for fitting curves to data drawn from an exponential family, that uses splines for which the number and locations of knots are free parameters. The method uses reversible-jump Markov chain Monte Carlo to change the knot configurations and a locality heuristic to speed up mixing. For nonnormal models, we approximate the integrated likelihood ratios needed to compute acceptance probabilities by using the Bayesian information criterion, BIC, under priors that make t...
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作者：Zhang, B
作者单位：University System of Ohio; University of Toledo
摘要：We propose an information-matrix-based goodness-of-fit statistic to test the validity of the logistic regression model based on case-control data by extending the information matrix test of White (1982) for detecting one-sample parametric model misspecification to the semiparametric profile likelihood setting under a two-sample semiparametric model, which is equivalent to the assumed logistic regression model. The proposed test statistic requires a high-dimensional matrix inversion, but is oth...
