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作者：Corcoran, SA
作者单位：University of Oxford
摘要：We introduce two classes of empirical discrepancy statistics that extend empirical likelihood, and establish simple conditions under which they admit Bartlett adjustment.
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作者：Beran, J; Bhansali, RJ; Ocker, D
作者单位：University of Konstanz; University of Liverpool; University of Konstanz
摘要：The question of model choice for the class of stationary and nonstationary, fractional and nonfractional autoregressive processes is considered. This class is defined by the property that the dth difference, for -1/2 < d < infinity, is a stationary autoregressive process of order-p(o) < infinity. A version of the Akaike information criterion, AIC, for determining an appropriate autoregressive order when d and the autoregressive parameters are estimated simultaneously by a maximum likelihood pr...
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作者：Liu, CH; Rubin, DB; Wu, YN
作者单位：AT&T; Alcatel-Lucent; Lucent Technologies; Nokia Corporation; Nokia Bell Labs; Harvard University; University of Michigan System; University of Michigan
摘要：The EM algorithm and its extensions are popular tools for modal estimation but are often criticised for their slow convergence. We propose a new method that can often make EM much faster. The intuitive idea is to use a 'covariance adjustment' to correct the analysis of the M step, capitalising on extra information captured in the imputed complete data. The way we accomplish this is by parameter expansion; we expand the complete-data model while preserving the observed-data model and use the ex...
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作者：Cooley, CA; MacEachern, SN
作者单位：University System of Ohio; Ohio State University
摘要：Multivariate kernel density estimation is often used as the basis for a nonparametric classification technique. However, the multivariate kernel classifier suffers from the curse of dimensionality, requiring inordinately large sample sizes to achieve a reasonable degree of accuracy in high dimensional settings. A variance stabilising approach to kernel classification can be motivated through an alternative interpretation of linear and quadratic discriminant analysis in which rotations of the c...
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作者：Nadeau, C; Lawless, JF
作者单位：Universite de Montreal; University of Waterloo
摘要：Liang & Zeger (1986) introduced methodology for the analysis of longitudinal data that provides an alternative to likelihood-based inference. They considered modelling the marginal means of the response follow-up measures, and proposed the use of unbiased estimating functions to handle inference. Here we wish to do the same for point or jump processes. We consider parametric models for the marginal means, and possibly the covariance structures, of processes that allow covariates. Inference is ...
