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作者：Yao, F; Müller, HG; Wang, JL
作者单位：Colorado State University System; Colorado State University Fort Collins; University of California System; University of California Davis
摘要：We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where the number of repeated measurements available per subject is small. In contrast, classical functional data analysis requires a large number of regularly spaced measurements per subject. We assume that the repeated measurements are located randomly with a random number of repetitions for each subject and ar...
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作者：Chen, YG; Diaconis, P; Holmes, SR; Liu, JS
作者单位：Duke University; Stanford University; Harvard University; Harvard University
摘要：We describe a sequential importance sampling (SIS) procedure for analyzing two-way zero-one or contingency tables with fixed marginal sums. An essential feature of the new method is that it samples the columns of the table progressively according to certain special distributions. Our method produces Monte Carlo samples that are remarkably close to the uniform distribution, enabling one to approximate closely the null distributions of various test statistics about these tables. Our method compa...
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作者：Zhang, L; Mykland, PA; Aït-Sahalia, Y
作者单位：Carnegie Mellon University; University of Chicago; Princeton University; Princeton University; National Bureau of Economic Research
摘要：It is a common practice in finance to estimate volatility from the sum of frequently sampled squared returns. However, market microstructure poses challenges to this estimation approach, as evidenced by recent empirical studies in finance. The present work attempts to lay out theoretical grounds that reconcile continuous-time modeling and discrete-time samples. We propose an estimation approach that takes advantage of the rich sources in tick-by-tick data while preserving the continuous-time a...
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作者：Forni, M; Hallin, M; Lippi, M; Reichlin, L
作者单位：Universita di Modena e Reggio Emilia; Universite Libre de Bruxelles; Universite Libre de Bruxelles; Sapienza University Rome
摘要：This article proposes a new forecasting method that makes use of information from a large panel of time series. Like earlier methods, our method is based on a dynamic factor model. We argue that our method improves on a standard principal component predictor in that it fully exploits all the dynamic covariance structure of the panel and also weights the variables according to their estimated signal-to-noise ratio. We provide asymptotic results for our optimal forecast estimator and show that i...
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作者：Kumbhakar, SC; Tsionas, EG
作者单位：State University of New York (SUNY) System; Binghamton University, SUNY; Athens University of Economics & Business
摘要：This article estimates technical and allocative inefficiencies and increase in costs therefrom of individual firms using a translog cost system consisting of the cost function and the cost share equations. We call it a nonlinear random-effects system because technical and allocative inefficiencies are random (hence the term random effects) and are separated from the random noise terms appearing in each equation of the system, and because the inefficiency terms appear in the system in a highly ...
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作者：Peng, RD; Schoenberg, FP; Woods, JA
作者单位：University of California System; University of California Los Angeles; California State University System; California State University Long Beach
摘要：Numerical indices are commonly used as tools to aid wildfire management and hazard assessment. Although the use of such indices is widespread, assessment of these indices in their respective regions of application is rare. We evaluate the effectiveness of the burning index (BI) for predicting wildfire occurrences in Los Angeles County, California using space-time point-process models. These models are based on an additive decomposition of the conditional intensity, with separate terms used to ...
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作者：Lin, MT; Zhang, JNL; Cheng, QS; Chen, R
作者单位：Peking University; Peking University; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：Sequential Monte Carlo methods, especially the particle filter (PF) and its various modifications, have been used effectively in dealing with stochastic dynamic systems. The standard PF samples the current state through the underlying state dynamics, then uses the current observation to evaluate the sample's importance weight. However, there is a set of problems in which the current observation provides significant information about the current state but the state dynamics are weak, and thus s...
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作者：Dukic, VM; Peña, EA
作者单位：University of Chicago; University of South Carolina System; University of South Carolina Columbia
摘要：This article considers the problem of estimating the dispersion parameter in a Gaussian model that is intermediate between a model where the mean parameter is fully known (fixed) and a model where the mean parameter is completely unknown. One of the goals is to understand the implications of the two-step process of first selecting a model among a finite number of submodels, then estimating a parameter of interest after the model selection, but using the same sample data. The estimators are cla...
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作者：Tamhane, AC
作者单位：Northwestern University
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作者：Chen, SN; Dahl, GB; Khan, S
作者单位：Hong Kong University of Science & Technology; University of Rochester
摘要：In this article we consider identification and estimation of a censored nonparametric location scale-model. We first show that in the case where the location function is strictly less than the (fixed) censoring point for all values in the support of the explanatory variables, the location function is not identified anywhere. In contrast, when the location function is greater or equal to the censoring point with positive probability, the location function is identified on the entire support, in...
