-
作者：Cantoni, E; Ronchetti, E
作者单位：University of Geneva
摘要：By starting from a natural class of robust estimators for generalized linear models based on the notion of qua-si-likelihood, we define robust deviances that can be used for stepwise model selection as in the classical framework. Wc derive the asymptotic distribution of tests based on robust deviances, and we investigate the stability of their asymptotic level under contamination. The binomial and Poisson models are treated in detail. Two applications to real data and a sensitivity analysis sh...
-
作者：Miller, ME; Ten Have, TR; Reboussin, BA; Lohman, KK; Rejeski, WJ
作者单位：Wake Forest University; Wake Forest Baptist Medical Center; University of Pennsylvania; Wake Forest University; Wake Forest Baptist Medical Center; Wake Forest University
摘要：Techniques for analyzing categorical outcomes obtained from longitudinal survey samples, with outcomes subject to multiple-cause nonresponse, are developed within the framework, of weighted generalized estimating equations. Development of these techniques was motivated by disability data obtained from the Longitudinal Study of Aging (LSOA), a longitudinal survey sample containing missing follow-up for many elderly participants. We posit a model that combines different multivariate link functio...
-
作者：Higdon, D; Yamamoto, S
作者单位：Duke University
摘要：We apply Bayesian image analysis techniques to a problem in a newly developed scanned probe technology that uses commercial magnetoresistive (MR) record and playback heads as probes to sense magnetic fields. This technology can be used for magnetic imaging and for evaluating playback and record processes in magnetic recording. In MR microscopy, an MR head is raster scanned while in physical contact with a magnetic sample (e.g., hard disk media, tape, or fine magnetic particles). By plotting th...
-
作者：Lai, TL; Wong, SPS
作者单位：Stanford University; Hong Kong University of Science & Technology
摘要：we consider a variant of the conventional neural network model, called the stochastic neural network, that can be used to approximate complex nonlinear stochastic systems. We show how the expectation-maximization algorithm can be used to develop efficient estimation schemes that have much lower computational complexity than those for conventional neural networks. This enables us to carry out model selection procedures, such as the Bayesian information criterion, to choose the number of hidden ...
