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作者：Cui, HJ; He, XM; Ng, KW
作者单位：Beijing Normal University; University of Illinois System; University of Illinois Urbana-Champaign; University of Hong Kong
摘要：Algebraically, principal components can be defined as the eigenvalues and eigenvectors of a covariance or correlation matrix, but they are statistically meaningful as successive projections of the multivariate data in the direction of maximal variability. An attractive alternative in robust principal component analysis is to replace the classical variability measure, i.e. variance, by a robust dispersion measure. This projection-pursuit approach was first proposed in Li & Chen (1985) as a meth...
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作者：Davis, RA; Dunsmuir, WTM; Streett, SB
作者单位：Colorado State University System; Colorado State University Fort Collins; University of New South Wales Sydney; National Center Atmospheric Research (NCAR) - USA
摘要：This paper is concerned with a general class of observation-driven models for time series of counts whose conditional distributions given past observations and explanatory variables follow a Poisson distribution. These models provide a flexible framework for modelling a wide range of dependence structures. Conditions for stationarity and ergodicity of these processes are established from which the large-sample properties of the maximum likelihood estimators can be derived. Simulations are prov...
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作者：Wong, F; Carter, CK; Kohn, R
作者单位：University of New South Wales Sydney; Commonwealth Scientific & Industrial Research Organisation (CSIRO); University of New South Wales Sydney
摘要：A Bayesian method is proposed for estimating an inverse covariance matrix from Gaussian data. The method is based on a prior that allows the off-diagonal elements of the inverse covariance matrix to be zero, and in many applications results in a parsimonious parameterisation of the covariance matrix. No assumption is made about the structure of the corresponding graphical model, so the method applies to both nondecomposable and decomposable graphs. All the parameters are estimated by model ave...
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作者：Tang, G; Little, RJA; Raghunathan, TE
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Michigan System; University of Michigan
摘要：We consider multivariate regression analysis with missing data in the outcome variables, when the nonresponse mechanism depends on the underlying values of the responses and hence is nonignorable. Related problems include response-biased sampling where data are sampled with probability depending only on the univariate response. Our methods do not require specification of the form of the nonresponse mechanism. We show that, under certain regularity conditions, all the regression parameters can ...
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作者：Sasieni, PD; Winnett, A
作者单位：University of London; Queen Mary University London; Cancer Research UK
摘要：The proportional hazards model makes two major assumptions: the hazard ratio is constant over time, and the relationship between the hazard and continuous covariates is log-linear. Methods exist for checking and relaxing each of these assumptions, but in both cases the methods rely on the other assumption being true. Problems can occur if neither of the assumptions is appropriate, or even if only one of the assumptions is appropriate but it is not known which. We propose a new kind of residual...
