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作者：Crainiceanu, Ciprian M.; Staicu, Ana-Maria; Di, Chong-Zhi
作者单位：Johns Hopkins University; North Carolina State University; Fred Hutchinson Cancer Center
摘要：We introduce Generalized Multilevel Functional Linear Models (GMFLMs), a novel statistical framework for regression models where exposure has a multilevel functional structure. We show that GMFLMs are, in fact, generalized multilevel mixed models. Thus, GMFLMs can be analyzed using the mixed effects inferential machinery and can be generalized within a well-researched statistical framework. We propose and compare two methods for inference: (1) a two-stage frequentist approach: and (2) a joint ...
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作者：Chib, Siddhartha; Ergashev, Bakhodir
作者单位：Washington University (WUSTL); Federal Reserve System - USA; Federal Reserve Bank - Richmond
摘要：In finance and economics much work has been done on the theoretical modeling and statistical estimation of the yield curve, defined as the relationship between -1/tau logp(t)(tau) and tau, where p(t)(tau) is the time t price of a zero-coupon bond with payoff I at maturity date t + tau. Of considerable current interest are models of the yield curve in which a collection of observed and latent factors determine the market price of factor risks, the stochastic discount factor, and the arbitrage-f...
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作者：El Ghouch, Anouar; Genton, Marc G.
作者单位：University of Geneva; Texas A&M University System; Texas A&M University College Station
摘要：We propose a new approach to conditional quantile function estimation that combines both parametric and nonparametric techniques. At each design point, a global, possibly incorrect, pilot parametric model is locally adjusted through a kernel smoothing fit. The resulting quantile regression estimator behaves like a parametric estimator when the latter is correct and converges to the nonparametric solution as the parametric start deviates from the true underlying model. We give a Bahadur-type re...
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作者：Fokianos, Konstantinos; Rahbek, Anders; Tjostheim, Dag
作者单位：University of Cyprus; University of Copenhagen; CREATES; University of Bergen
摘要：In this article we consider geometric ergodicity and likelihood-based inference for linear and nonlinear Poisson autoregression. In the linear case, the conditional mean is linked linearly to its past values, as well as to the observed values of the Poisson process. This also applies to the conditional variance, making possible interpretation as an integer-valued generalized autoregressive conditional heteroscedasticity process. In a nonlinear conditional Poisson model, the conditional mean is...
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作者：Hall, Peter; Titterington, D. M.; Xue, Jing-Hao
作者单位：University of Melbourne; University of Glasgow; University of London; University College London
摘要：Conventional distance-based classifiers use standard Euclidean distance, and so can suffer from excessive volatility if vector components have heavy-tailed distributions. This difficulty can be alleviated by replacing the L-2 distance by its L-1 counterpart. For example, the L-1 version of the popular centroid classifier would allocate a new data value to the population to whose centroid it was closest in L-1 terms. However, this approach can lead to inconsistency, because the centroid is defi...
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作者：Ghosh, Debashis; Choi, Hyungwon
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
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作者：Gustafson, Paul
作者单位：University of British Columbia
摘要：In health research and other fields. the observational data available to researchers often fall short of the data that ideally would be available, due to the inherent limitations of study design and data acquisition. Were they available, these ideal data might be readily analyzed via straightforward statistical models with such desirable properties as parameter identifiability. Conversely, realistic models for the available data that incorporate uncertainty about the link between ideal and ava...
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作者：Wang, Lan; Kai, Bo; Li, Runze
作者单位：University of Minnesota System; University of Minnesota Twin Cities; College of Charleston; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：By allowing the regression coefficients to change with certain covariates, the class of varying coefficient models offers a flexible approach to modeling nonlinearity and interactions between covariates. This article proposes a novel estimation procedure for the varying coefficient models based on local ranks. The new procedure provides a highly efficient and robust alternative to the local linear least squares method. and can be conveniently implemented using existing R software package. Theo...
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作者：Xiao, Zhijie; Koenker, Roger
作者单位：Boston College; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Conditional quantile estimation is an essential ingredient in modem risk management. Although generalized autoregressive conditional heteroscedasticity (GARCH) processes have proven highly successful in modeling financial data, it is generally recognized that it would be useful to consider a broader class of processes capable of representing more flexibly both asymmetry and tail behavior of conditional returns distributions. In this article we study estimation of conditional quantiles for GARC...
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作者：Rutter, Carolyn M.; Miglioretti, Diana L.; Savarino, James E.
作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
摘要：Microsimulation models that describe disease processes synthesize information from multiple sources and can be used to estimate the effects of screening and treatment on cancer incidence and mortality at a population level. These models are characterized by simulation of individual event histories for an idealized population of interest. Microsimulation models are complex and invariably include parameters that are not well informed by existing data. Therefore, a key component of model developm...
