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作者：Hall, P; Maiti, T
作者单位：Australian National University; Iowa State University
摘要：The particularly wide range of applications of small area prediction, e.g. in policy making decisions, has meant that this topic has received substantial attention in recent years. The problems of estimating mean-squared predictive error, of correcting that estimator for bias and of constructing prediction intervals have been addressed by various workers, although existing methodology is still restricted to a narrow range of models. To overcome this difficulty we develop new, bootstrap-based m...
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作者：Sima, Diana M.; Van Huffel, Sabine
作者单位：KU Leuven
摘要：We formulate and solve a semiparametric fitting problem with regularization constraints. The model that we focus on is composed of a parametric non-linear part and a nonparametric part that can be reconstructed via splines. Regularization is employed to impose a certain degree of smoothness on the nonparametric part. Semiparametric regression is presented as a generalization of non-linear regression, and all important differences that arise from the statistical and computational points of view...
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作者：DiCiccio, TJ; Monti, AC; Young, GA
作者单位：Imperial College London; Cornell University; University of Sannio
摘要：We present a variance stabilizing transformation for inference about a scalar parameter that is estimated by a function of a multivariate M-estimator. The transformation proposed is automatic, computationally simple and can be applied quite generally. Though it is based on an intuitive notion and entirely empirical, the transformation is shown to have an appropriate justification in providing variance stabilization when viewed from both parametric and nonparametric perspectives. Further, the t...
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作者：Van Bellegem, Sebastien; Dahlhaus, Rainer
作者单位：Universite Catholique Louvain; Ruprecht Karls University Heidelberg
摘要：Over recent decades increasingly more attention has been paid to the problem of how to fit a parametric model of time series with time-varying parameters. A typical example is given by autoregressive models with time-varying parameters. We propose a procedure to fit such time-varying models to general non-stationary processes. The estimator is a maximum Whittle likelihood estimator on sieves. The results do not assume that the observed process belongs to a specific class of time-varying parame...
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作者：Yao, F; Lee, TCM
作者单位：Colorado State University System; Colorado State University Fort Collins
摘要：We propose an iterative estimation procedure for performing functional principal component analysis. The procedure aims at functional or longitudinal data where the repeated measurements from the same subject are correlated. An increasingly popular smoothing approach, penalized spline regression, is used to represent the mean function. This allows straightforward incorporation of covariates and simple implementation of approximate inference procedures for coefficients. For the handling of the ...
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作者：Morris, JS; Carroll, RJ
作者单位：University of Texas System; UTMD Anderson Cancer Center; Texas A&M University System; Texas A&M University College Station
摘要：Increasingly, scientific studies yield functional data, in which the ideal units of observation are curves and the observed data consist of sets of curves that are sampled on a fine grid. We present new methodology that generalizes the linear mixed model to the functional mixed model framework, with model fitting done by using a Bayesian wavelet-based approach. This method is flexible, allowing functions of arbitrary form and the full range of fixed effects structures and between-curve covaria...
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作者：Ray, S; Mallick, B
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：We propose a nonparametric Bayes wavelet model for clustering of functional data. The wavelet-based methodology is aimed at the resolution of generic global and local features during clustering and is suitable for clustering high dimensional data. Based on the Dirichlet process, the nonparametric Bayes model extends the scope of traditional Bayes wavelet methods to functional clustering and allows the elicitation of prior belief about the regularity of the functions and the number of clusters ...
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作者：Beskos, Alexandros; Papaspiliopoulos, Omiros; Roberts, Gareth O.; Fearnhead, Paul
作者单位：Lancaster University
摘要：The objective of the paper is to present a novel methodology for likelihood-based inference for discretely observed diffusions. We propose Monte Carlo methods, which build on recent advances on the exact simulation of diffusions, for performing maximum likelihood and Bayesian estimation.
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作者：Hall, P; Hosseini-Nasab, M
作者单位：Australian National University
摘要：Functional data analysis is intrinsically infinite dimensional; functional principal component analysis reduces dimension to a finite level, and points to the most significant components of the data. However, although this technique is often discussed, its properties are not as well understood as they might be. We show how the properties of functional principal component analysis can be elucidated through stochastic expansions and related results. Our approach quantifies the errors that arise ...
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作者：Antoniadis, Anestis; Paparoditis, Efstathios; Sapatinas, Theofanis
作者单位：Communaute Universite Grenoble Alpes; Universite Grenoble Alpes (UGA); University of Cyprus
摘要：We consider the prediction problem of a time series on a whole time interval in terms of its past. The approach that we adopt is based on functional kernel nonparametric regression estimation techniques where observations are discrete recordings of segments of an underlying stochastic process considered as curves. These curves are assumed to lie within the space of continuous functions, and the discretized time series data set consists of a relatively small, compared with the number of segment...
