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作者：Wang, Yixin; Blei, David M.
作者单位：Columbia University; Columbia University
摘要：A key challenge for modern Bayesian statistics is how to perform scalable inference of posterior distributions. To address this challenge, variational Bayes (VB) methods have emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) methods. VB methods tend to be faster while achieving comparable predictive performance. However, there are few theoretical results around VB. In this article, we establish frequentist consistency and asymptotic normality of VB methods. Spec...
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作者：Einmahl, Jesson J.; Einmahl, John H. J.; de Haan, Laurens
作者单位：Tilburg University; Tilburg University; Tilburg University; Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; Universidade de Lisboa
摘要：There is no scientific consensus on the fundamental question whether the probability distribution of the human life span has a finite endpoint or not and, if so, whether this upper limit changes over time. Our study uses a unique dataset of the ages at death-in days-of all (about 285,000) Dutch residents, born in the Netherlands, who died in the years 1986-2015 at a minimum age of 92 years and is based on extreme value theory, the coherent approach to research problems of this type. Unlike som...
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作者：Carone, Marco; Luedtke, Alexander R.; van der Laan, Mark J.
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of California System; University of California Berkeley
摘要：Despite the risk of misspecification they are tied to, parametric models continue to be used in statistical practice because they are simple and convenient to use. In particular, efficient estimation procedures in parametric models are easy to describe and implement. Unfortunately, the same cannot be said of semiparametric and nonparametric models. While the latter often reflect the level of available scientific knowledge more appropriately, performing efficient inference in these models is ge...
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作者：Johndrow, James E.; Smith, Aaron; Pillai, Natesh; Dunson, David B.
作者单位：Stanford University; University of Ottawa; Harvard University; Duke University
摘要：Many modern applications collect highly imbalanced categorical data, with some categories relatively rare. Bayesian hierarchical models combat data sparsity by borrowing information, while also quantifying uncertainty. However, posterior computation presents a fundamental barrier to routine use; a single class of algorithms does not work well in all settings and practitioners waste time trying different types of Markov chain Monte Carlo (MCMC) approaches. This article was motivated by an appli...
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作者：Strait, Justin; Chkrebtii, Oksana; Kurtek, Sebastian
作者单位：University System of Georgia; University of Georgia; University System of Ohio; Ohio State University
摘要：A population quantity of interest in statistical shape analysis is the location of landmarks, which are points that aid in reconstructing and representing shapes of objects. We provide an automated, model-based approach to inferring landmarks given a sample of shape data. The model is formulated based on a linear reconstruction of the shape, passing through the specified points, and a Bayesian inferential approach is described for estimating unknown landmark locations. The question of how many...
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作者：Kong, Xinbing; Wang, Jiangyan; Xing, Jinbao; Xu, Chao; Ying, Chao
作者单位：Nanjing Audit University; Soochow University - China
摘要：The distributions of the common and idiosyncratic components for an individual variable are important in forecasting and applications. However, they are not identified with low-dimensional observations. Using the recently developed theory for large dimensional approximate factor model for large panel data, the common and idiosyncratic components can be estimated consistently. Based on the estimated common and idiosyncratic components, we construct the empirical processes for estimation of the ...
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作者：Deng, Shirong; Zhao, Xingqiu
作者单位：Wuhan University; Hong Kong Polytechnic University
摘要：In many longitudinal studies, repeated response and predictors are not directly observed, but can be treated as distorted by unknown functions of a common confounding covariate. Moreover, longitudinal data involve an observation process which may be informative with a longitudinal response process in practice. To deal with such complex data, we propose a class of flexible semiparametric covariate-adjusted joint models. The new models not only allow for the longitudinal response to be correlate...
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作者：Tavakoli, Shahin; Pigoli, Davide; Aston, John A. D.; Coleman, John S.
作者单位：University of Warwick; University of London; King's College London; University of Cambridge; University of Oxford
摘要：Dialect variation is of considerable interest in linguistics and other social sciences. However, traditionally it has been studied using proxies (transcriptions) rather than acoustic recordings directly. We introduce novel statistical techniques to analyze geolocalized speech recordings and to explore the spatial variation of pronunciations continuously over the region of interest, as opposed to traditional isoglosses, which provide a discrete partition of the region. Data of this type require...
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作者：Hu, Lixia; Huang, Tao; You, Jinhong
作者单位：Shanghai Lixin University of Accounting & Finance; Shanghai University of Finance & Economics
摘要：The additive model and the varying-coefficient model are both powerful regression tools, with wide practical applications. However, our empirical study on a financial data has shown that both of these models have drawbacks when applied to locally stationary time series. For the analysis of functional data, Zhang and Wang have proposed a flexible regression method, called the varying-coefficient additive model (VCAM), and presented a two-step spline estimation method. Motivated by their approac...
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作者：Liu, Zhonghua; Lin, Xihong
作者单位：University of Hong Kong; Harvard University; Harvard T.H. Chan School of Public Health
摘要：Joint analysis of multiple phenotypes can increase statistical power in genetic association studies. Principal component analysis, as a popular dimension reduction method, especially when the number of phenotypes is high dimensional, has been proposed to analyze multiple correlated phenotypes. It has been empirically observed that the first PC, which summarizes the largest amount of variance, can be less powerful than higher-order PCs and other commonly used methods in detecting genetic associ...
