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作者：Wong, C. S.; Chan, W. S.; Kam, P. L.
作者单位：Chinese University of Hong Kong; University of Hong Kong
摘要：We introduce the class of Student t-mixture autoregressive models, which is promising for financial time series modelling. The model is able to capture serial correlations, time-varying means and volatilities, and the shape of the conditional distributions can be time varied from short-tailed to long-tailed, or from unimodal to multimodal. The use of t-distributed errors in each component of the model allows conditional leptokurtic distributions that account for the commonly observed excess un...
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作者：Wu, S.; Shen, X.; Geyer, C. J.
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Several sparseness penalties have been suggested for delivery of good predictive performance in automatic variable selection within the framework of regularization. All assume that the true model is sparse. We propose a penalty, a convex combination of the L-1- and L-infinity-norms, that adapts to a variety of situations including sparseness and nonsparseness, grouping and nongrouping. The proposed penalty performs grouping and adaptive regularization. In addition, we introduce a novel homotop...
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作者：Claeskens, Gerda; Krivobokova, Tatyana; Opsomer, Jean D.
作者单位：KU Leuven; KU Leuven; University of Gottingen; Colorado State University System; Colorado State University Fort Collins
摘要：We study the class of penalized spline estimators, which enjoy similarities to both regression splines, without penalty and with fewer knots than data points, and smoothing splines, with knots equal to the data points and a penalty controlling the roughness of the fit. Depending on the number of knots, sample size and penalty, we show that the theoretical properties of penalized regression spline estimators are either similar to those of regression splines or to those of smoothing splines, wit...
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作者：Davis, Richard A.; Wu, Rongning
作者单位：Columbia University; City University of New York (CUNY) System; Baruch College (CUNY)
摘要：We study generalized linear models for time series of counts, where serial dependence is introduced through a dependent latent process in the link function. Conditional on the covariates and the latent process, the observation is modelled by a negative binomial distribution. To estimate the regression coefficients, we maximize the pseudolikelihood that is based on a generalized linear model with the latent process suppressed. We show the consistency and asymptotic normality of the generalized ...
