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作者：Wager, CG; Coull, BA; Lange, N
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School; Harvard University; Harvard University Medical Affiliates; McLean Hospital
摘要：Pharmacological experiments in brain microscopy study patterns of cellular activation in response to psychotropic drugs for connected neuroanatomic regions. A typical experimental design produces replicated point patterns having highly complex spatial variability. Modelling this variability hierarchically can enhance the inference for comparing treatments. We propose a semiparametric formulation that combines the robustness of a nonparametric kernel method with the efficiency of likelihood-bas...
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作者：Cornford, D; Csató, L; Evans, DJ; Opper, M
作者单位：Aston University
摘要：The retrieval of wind vectors from satellite scatterometer observations is a non-linear inverse problem. A common approach to solving inverse problems is to adopt a Bayesian framework and to infer the posterior distribution of the parameters of interest given the observations by using a likelihood model relating the observations to the parameters, and a prior distribution over the parameters. We show how Gaussian process priors can be used efficiently with a variety of likelihood models, using...
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作者：Huang, WZ
摘要：It is well known that in a sequential study the probability that the likelihood ratio for a simple alternative hypothesis H-1 versus a simple null hypothesis H-o will ever be greater than a positive constant c will not exceed /c under H-o. However, for a composite alternative hypothesis, this bound of 1/c will no longer hold when a generalized likelihood ratio statistic is used. We consider a stepwise likelihood ratio statistic which, for each new observation, is updated by cumulatively multip...
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作者：Huang, JHZ; Yang, LJ
作者单位：University of Pennsylvania; Michigan State University
摘要：We propose a lag selection method for non-linear additive autoregressive models that is based on spline estimation and the Bayes information criterion. The additive structure of the autoregression function is used to overcome the 'curse of dimensionality', whereas the spline estimators effectively take into account such a structure in estimation. A stepwise procedure is suggested to implement the method proposed. A comprehensive Monte Carlo study demonstrates good performance of the method pro...
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作者：Fokianos, K
作者单位：University of Cyprus
摘要：The density ratio model specifies that the likelihood ratio of m-1 probability density functions with respect to the mth is of known parametric form without reference to any parametric model. We study the semiparametric inference problem that is related to the density ratio model by appealing to the methodology of empirical likelihood. The combined data from all the samples leads to more efficient kernel density estimators for the unknown distributions. We adopt variants of well-established te...
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作者：Kim, YJ; Gu, C
作者单位：Purdue University System; Purdue University; Yale University
摘要：Smoothing splines via the penalized least squares method provide versatile and effective nonparametric models for regression with Gaussian responses. The computation of smoothing splines is generally of the order O(n(3)), n being the sample size, which severely limits its practical applicability. We study more scalable computation of smoothing spline regression via certain low dimensional approximations that are asymptotically as efficient. A simple algorithm is presented and the Bayes model t...
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作者：Koenker, R; Mizera, I
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Alberta
摘要：Hansen, Kooperberg and Sardy introduced a family of continuous, piecewise linear functions defined over adaptively selected triangulations of the plane as a general approach to statistical modelling of bivariate densities and regression and hazard functions. These triograms enjoy a natural affine equivariance that offers distinct advantages over competing tensor product methods that are more commonly used in statistical applications. Triograms employ basis functions consisting of linear 'tent ...
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作者：Miloslavsky, M; Keles, S; van der Laan, MJ; Butler, S
作者单位：University of California System; University of California Berkeley; Roche Holding; Roche Holding USA; Genentech
摘要：Recurrent events models have had considerable attention recently. The majority of approaches show the consistency of parameter estimates under the assumption that censoring is independent of the recurrent events process of interest conditional on the covariates that are included in the model. We provide an overview of available recurrent events analysis methods and present an inverse probability of censoring weighted estimator for the regression parameters in the Andersen-Gill model that is co...
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作者：Barber, S; Nason, GP
作者单位：University of Bristol
摘要：Wavelet shrinkage is an effective nonparametric regression technique, especially when the underlying curve has irregular features such as spikes or discontinuities. The basic idea is simple: take the discrete wavelet transform of data consisting of a signal corrupted by noise; shrink or remove the wavelet coefficients to remove the noise; then invert the discrete wavelet transform to form an estimate of the true underlying curve. Various researchers have proposed increasingly sophisticated met...
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作者：Lin, HQ; Scharfstein, DO; Rosenheck, RA
作者单位：Yale University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; US Department of Veterans Affairs; Veterans Health Administration (VHA); VA Connecticut Healthcare System
摘要：A frequent problem in longitudinal studies is that subjects may miss scheduled visits or be assessed at self-selected points in time. As a result, observed outcome data may be highly unbalanced and the availability of the data may be directly related to the outcome measure and/or some auxiliary factors that are associated with the outcome. If the follow-up visit and outcome processes are correlated, then marginal regression analyses will produce biased estimates. Building on the work of Robins...
