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作者：Koopman, Siem Jan; Oohs, Marius; Carnero, M. Angeles
作者单位：Vrije Universiteit Amsterdam; Universitat d'Alacant
摘要：Novel periodic extensions of dynamic long-memory regression models with autoregressive conditional heteroscedastic errors are considered for the analysis of daily electricity spot prices. The parameters of the model with mean and variance specifications are estimated simultaneously by the method of approximate maximum likelihood. The methods are implemented for time series of 1,200-4,400 daily price observations in four European power markets. Apart from persistence, heteroscedasticity, and ex...
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作者：Huang, Hanwen; Zou, Fei; Wright, Fred A.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：One objective of allelic-loss studies is to identify chromosomal locations that may harbor tumor-suppressor genes. An instability-selection model has been developed for allelic-loss data in which the loss events are available for each tumor and each marker (allelotypes). In performing pooled analyses of published allelic-loss experiments, however, only summaries of the frequency of allelic loss (EAL) may be available. The instability-selection model can be applied to these summary data, but na...
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作者：Wu, Yichao; Liu, Yufeng
作者单位：Princeton University; University of North Carolina; University of North Carolina Chapel Hill
摘要：The support vector machine (SVM) has been widely applied for classification problems in both machine learning and statistics. Despite its popularity, however, SVM has some drawbacks in certain situations. In particular, the SVM classifier can be very sensitive to outliers in the training sample. Moreover, the number of support vectors (SVs) can be very large in many applications. To circumvent these drawbacks, we propose the robust truncated hinge loss SVM (RSVM), which uses a truncated hinge ...
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作者：Ruppert, David
作者单位：Cornell University
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作者：Bouman, Peter; Meng, Xiao-Li; Dignam, James; Dukic, Vanja
作者单位：Northwestern University; Harvard University; University of Chicago
摘要：In multicenter studies, one often needs to make inference about a population survival curve based on multiple, possibly heterogeneous survival data from individual centers. We investigate a flexible Bayesian method for estimating a population survival curve based on a semiparametric multiresolution hazard model that can incorporate covariates and account for center heterogeneity. The method yields a smooth estimate of the survival curve for multiple resolutions or time scales of interest. The ...
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作者：Hans, Chris; Dobra, Adrian; West, Mike
作者单位：University System of Ohio; Ohio State University; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; Duke University
摘要：Model search in regression with very large numbers of candidate predictors raises challenges for both model specification and computation, for which standard approaches such as Markov chain Monte Carlo (MCMC) methods are often infeasible or ineffective. We describe a novel shotgun stochastic search (SSS) approach that explores interesting regions of the resulting high-dimensional model spaces and quickly identifies regions of high posterior probability over models. We describe algorithmic and ...
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作者：Tong, Tiejun; Wang, Yuedong
作者单位：Yale University; University of California System; University of California Santa Barbara
摘要：Microarray technology allows a scientist to study genomewide patterns of gene expression. Thousands of individual genes are measured with a relatively small number of replications, which poses challenges to traditional statistical methods. In particular, the gene-specific estimators of variances are not reliable and gene-by-gene tests have Sow powers. In this article we propose a family of shrinkage estimators for variances raised to a fixed power. We derive optimal shrinkage parameters under ...
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作者：He, Xuming; Hu, Feifang
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Virginia
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作者：Wang, Lan; Qu, Annie
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Oregon State University
摘要：A robust approach for testing the parametric form of a regression function versus an omnibus alternative is introduced. This generalizes existing robust methods for testing subhypotheses in a regression model. The new test is motivated by developments in modern smoothing-based testing procedures and can be viewed as a robustification of a smoothing-based conditional moment test. It is asymptotically normal under both the null hypothesis and local alternatives. The robustified test retains the ...
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作者：Fried, Roland; Einbeck, Jochen; Gather, Ursula
作者单位：Dortmund University of Technology; Durham University
摘要：We propose weighted repeated median filters and smoothers for robust nonparametric regression in general and for robust online signal extraction from time series in particular. The new methods allow us to remove outlying sequences and to preserve discontinuities (shifts) in the underlying regression function (the signal) in the presence of local linear trends. Suitable weighting of the observations according to their distances in the design space reduces the bias arising from nonlinearities an...
