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作者：Wood, Simon N.
作者单位：University of Bath
摘要：Recent work by Reiss and Ogden provides a theoretical basis for sometimes preferring restricted maximum likelihood (REML) to generalized cross-validation (GCV) for smoothing parameter selection in semiparametric regression. However, existing REML or marginal likelihood (ML) based methods for semiparametric generalized linear models (GLMs) use iterative REML or ML estimation of the smoothing parameters of working linear approximations to the GLM. Such indirect schemes need not converge and fail...
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作者：Mattei, Alessandra; Mealli, Fabrizia
作者单位：University of Florence
摘要：Many studies involving causal questions are often concerned with understanding the causal pathways by which a treatment affects an outcome. Thus, the concept of 'direct' versus 'indirect' effects comes into play. We tackle the problem of disentangling direct and indirect effects by investigating new augmented experimental designs, where the treatment is randomized, and the mediating variable is not forced, but only randomly encouraged. There are two key features of our framework: we adopt a pr...
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作者：Witten, Daniela M.; Tibshirani, Robert
作者单位：University of Washington; University of Washington Seattle; Stanford University
摘要：We consider the supervised classification setting, in which the data consist of p features measured on n observations, each of which belongs to one of K classes. Linear discriminant analysis (LDA) is a classical method for this problem. However, in the high dimensional setting where p >> n, LDA is not appropriate for two reasons. First, the standard estimate for the within-class covariance matrix is singular, and so the usual discriminant rule cannot be applied. Second, when p is large, it is ...
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作者：Ma, Yanyuan; Hart, Jeffrey D.; Janicki, Ryan; Carroll, Raymond J.
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：We consider functional measurement error models, i.e. models where covariates are measured with error and yet no distributional assumptions are made about the mismeasured variable. We propose and study a score-type local test and an orthogonal series-based, omnibus goodness-of-fit test in this context, where no likelihood function is available or calculated-i.e. all the tests are proposed in the semiparametric model framework. We demonstrate that our tests have optimality properties and comput...
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作者：Fryzlewicz, P.; Oh, H. -S.
作者单位：University of London; London School Economics & Political Science; Seoul National University (SNU)
摘要：Traditional visualization of time series data often consists of plotting the time series values against time and 'connecting the dots'. We propose an alternative, multiscale visualization technique, motivated by the scale-space approach in computer vision. In brief, our method also 'connects the dots' but uses a range of pens of varying thicknesses for this. The resulting multiscale map, which is termed the thick pen transform, corresponds to viewing the time series from a range of distances. ...
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作者：Tibshirani, Robert
作者单位：Stanford University
摘要：In the paper I give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.
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作者：Yau, C.; Papaspiliopoulos, O.; Roberts, G. O.; Holmes, C.
作者单位：Pompeu Fabra University; University of Oxford; University of Warwick
摘要：We propose a flexible non-parametric specification of the emission distribution in hidden Markov models and we introduce a novel methodology for carrying out the computations. Whereas current approaches use a finite mixture model, we argue in favour of an infinite mixture model given by a mixture of Dirichlet processes. The computational framework is based on auxiliary variable representations of the Dirichlet process and consists of a forward-backward Gibbs sampling algorithm of similar compl...
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作者：Guillotte, Simon; Perron, Francois; Segers, Johan
作者单位：Universite Catholique Louvain; University of Prince Edward Island; Universite de Montreal
摘要：The tail of a bivariate distribution function in the domain of attraction of a bivariate extreme value distribution may be approximated by that of its extreme value attractor. The extreme value attractor has margins that belong to a three-parameter family and a dependence structure which is characterized by a probability measure on the unit interval with mean equal to 1/2, which is called the spectral measure. Inference is done in a Bayesian framework using a censored likelihood approach. A pr...
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作者：McCabe, Brendan P. M.; Martin, Gael M.; Harris, David
作者单位：Monash University; University of Liverpool; University of Melbourne
摘要：Efficient probabilistic forecasts of integer-valued random variables are derived. The optimality is achieved by estimating the forecast distribution non-parametrically over a given broad model class and proving asymptotic (non-parametric) efficiency in that setting. The method is developed within the context of the integer auto-regressive class of models, which is a suitable class for any count data that can be interpreted as a queue, stock, birth-and-death process or branching process. The th...
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作者：Bradic, Jelena; Fan, Jianqing; Wang, Weiwei
作者单位：Princeton University; University of Texas System; University of Texas Health Science Center Houston
摘要：In high dimensional model selection problems, penalized least square approaches have been extensively used. The paper addresses the question of both robustness and efficiency of penalized model selection methods and proposes a data-driven weighted linear combination of convex loss functions, together with weighted L-1-penalty. It is completely data adaptive and does not require prior knowledge of the error distribution. The weighted L-1-penalty is used both to ensure the convexity of the penal...
