-
作者：Jansen, Maarten; Nason, Guy P.; Silverman, B. W.
作者单位：University of Bristol; KU Leuven; University of Oxford
摘要：For regularly spaced one-dimensional data, wavelet shrinkage has proven to be a compelling method for non-parametric function estimation. We create three new multiscale methods that provide wavelet-like transforms both for data arising on graphs and for irregularly spaced spatial data in more than one dimension. The concept of scale still exists within these transforms, but as a continuous quantity rather than dyadic levels. Further, we adapt recent empirical Bayesian shrinkage techniques to e...
-
作者：Guan, Yongtao
作者单位：Yale University
摘要：The paper introduces a new approach to estimate the variance of statistics that are computed from an inhomogeneous spatial point process. The approach proposed is based on the assumption that the observed point process can be thinned to be a second-order stationary point process, where the thinning probability depends only on the first-order intensity function of the (unthinned) original process. The resulting variance estimator is proved to be asymptotically consistent for the target paramete...
-
作者：Jose Lombardia, Maria; Sperlich, Stefan
作者单位：Universidade de Santiago de Compostela; University of Gottingen
摘要：The paper presents a study of the generalized partially linear model including random effects in its linear part. We propose an estimator that combines likelihood approaches for mixed effects models, with kernel methods. Following the methodology of Hardle and co-workers, we introduce a test for the hypothesis of a parametric mixed effects model against the alternative of a semiparametric mixed effects model. The critical values are estimated by using a bootstrap procedure. The asymptotic theo...
-
作者：Jin, Jiashun
作者单位：Carnegie Mellon University; Purdue University System; Purdue University
摘要：Since James and Stein's seminal work, the problem of estimating n normal means has received plenty of enthusiasm in the statistics community. Recently, driven by the fast expansion of the field of large-scale multiple testing, there has been a resurgence of research interest in the n normal means problem. The new interest, however, is more or less concentrated on testing n normal means: to determine simultaneously which means are 0 and which are not. In this setting, the proportion of the non-...
-
作者：Molenberghs, Geert; Beunckens, Caroline; Sotto, Cristina; Kenward, Michael G.
作者单位：Hasselt University; University of London; London School of Hygiene & Tropical Medicine
摘要：Over the last decade a variety of models to analyse incomplete multivariate and longitudinal data have been proposed, many of which allowing for the missingness to be not at random, in the sense that the unobserved measurements influence the process governing missingness, in addition to influences coming from observed measurements and/or covariates. The fundamental problems that are implied by such models, to which we refer as sensitivity to unverifiable modelling assumptions, has, in turn, sp...
-
作者：Wood, Simon N.; Bravington, Mark V.; Hedley, Sharon L.
作者单位：University of Bath; Commonwealth Scientific & Industrial Research Organisation (CSIRO); University of St Andrews
摘要：Conventional smoothing methods sometimes perform badly when used to smooth data over complex domains, by smoothing inappropriately across boundary features, such as peninsulas. Solutions to this smoothing problem tend to be computationally complex, and not to provide model smooth functions which are appropriate for incorporating as components of other models, such as generalized additive models or mixed additive models. We propose a class of smoothers that are appropriate for smoothing over di...
-
作者：Cressie, Noel; Johannesson, Gardar
作者单位：University System of Ohio; Ohio State University; United States Department of Energy (DOE); Lawrence Livermore National Laboratory
摘要：Spatial statistics for very large spatial data sets is challenging. The size of the data set, n, causes problems in computing optimal spatial predictors such as kriging, since its computational cost is of order n(3). In addition, a large data set is often defined on a large spatial domain, so the spatial process of interest typically exhibits non-stationary behaviour over that domain. A flexible family of non-stationary covariance functions is defined by using a set of basis functions that is ...
-
作者：Heaton, T. J.; Silverman, B. W.
作者单位：University of Oxford; University of Oxford
摘要：The paper proposes a new approach to imputation using the expected sparse representation of a surface in a wavelet or lifting scheme basis. Our method incorporates a Bayesian mixture prior for these wavelet coefficients into a Gibbs sampler to generate a complete posterior distribution for the variable of interest. Intuitively, the estimator operates by borrowing strength from those observed neighbouring values to impute at the unobserved sites. We demonstrate the strong performance of our est...
-
作者：Hsieh, Jin-Jian; Wang, Weijing; Ding, A. Adam
作者单位：National Yang Ming Chiao Tung University; Northeastern University; National University of Singapore
摘要：Semicompeting risks data are commonly seen in biomedical applications in which a terminal event censors a non-terminal event. Possible dependent censoring complicates statistical analysis. We consider regression analysis based on a non-terminal event, say disease progression, which is subject to censoring by death. The methodology proposed is developed for discrete covariates under two types of assumption. First, separate copula models are assumed for each covariate group and then a flexible r...
-
作者：Fearnhead, Paul; Papaspiliopoulos, Omiros; Roberts, Gareth O.
作者单位：Lancaster University; University of Warwick
摘要：We introduce a novel particle filter scheme for a class of partially observed multivariate diffusions. We consider a variety of observation schemes, including diffusion observed with error, observation of a subset of the components of the multivariate diffusion and arrival times of a Poisson process whose intensity is a known function of the diffusion (Cox process). Unlike currently available methods, our particle filters do not require approximations of the transition and/or the observation d...
