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作者：Behseta, S; Kass, RE; Wallstrom, GL
作者单位：California State University System; California State University Bakersfield; Carnegie Mellon University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Carnegie Mellon University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：In many applications of functional data analysis, summarising functional variation based on fits, without taking account of the estimation process, runs the risk of attributing the estimation variation to the functional variation, thereby overstating the latter. For example, the first eigenvalue of a sample covariance matrix computed from estimated functions may be biased upwards. We display a set of estimated neuronal Poisson-process intensity functions where this bias is substantial, and we ...
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作者：Kume, A; Wood, ATA
作者单位：University of Kent; University of Nottingham
摘要：The Fisher-Bingham distribution is obtained when a multivariate normal random vector is conditioned to have unit length. Its normalising constant can be expressed as an elementary function multiplied by the density, evaluated at 1, of a linear combination of independent noncentral chi(2)(1) random variables. Hence we may approximate the normalising constant by applying a saddlepoint approximation to this density. Three such approximations, implementation of each of which is straightforward, ar...
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作者：Yin, XR; Cook, RD
作者单位：University System of Georgia; University of Georgia; University of Minnesota System; University of Minnesota Twin Cities
摘要：We propose a general dimension-reduction method that combines the ideas of likelihood, correlation, inverse regression and information theory. We do not require that the dependence be confined to particular conditional moments, nor do we place restrictions on the predictors or on the regression that are necessary for methods like ordinary least squares and sliced-inverse regression. Although we focus on single-index regressions, the underlying idea is applicable more generally. Illustrative ex...
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作者：Chan, NH; Peng, L
作者单位：Chinese University of Hong Kong; University System of Georgia; Georgia Institute of Technology
摘要：The weighted least absolute deviations estimator is studied for an AR(1) process with ARCH(1) errors c, Unlike for the quasi maximum likelihood estimator, the estimator's limiting distribution is shown to be normal even when E(epsilon(4)(t)) = infinity. Furthermore, the estimator can be applied to examine the symmetry of the density of epsilon(t) and to estimate the quantity E(log vertical bar alpha + lambda(1/2)epsilon(t)vertical bar), which are of crucial importance for conducting asymptotic...
