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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：Wickramasuriya, Shanika L.; Athanasopoulos, George; Hyndman, Rob J.
作者单位：University of Auckland; Monash University
摘要：Large collections of time series often have aggregation constraints due to product or geographical groupings. The forecasts for the most disaggregated series are usually required to add-up exactly to the forecasts of the aggregated series, a constraint we refer to as coherence. Forecast reconciliation is the process of adjusting forecasts to make them coherent. The reconciliation algorithm proposed by Hyndman et al. (2011) is based on a generalized least squares estimator that requires an esti...
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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：Vandekar, Simon N.; Reiss, Philip T.; Shinohara, Russell T.
作者单位：University of Pennsylvania; University of Haifa
摘要：In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of the high-dimensional variable are created to sequentially test and localize the association with the outcome. In some cases, the associations between the outcome and summary measures are significant, but subsequent tests used to localize differences are underpowered and do not identify regions associated with ...
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作者：Wang, Yifei; Tancredi, Daniel J.; Miglioretti, Diana L.
作者单位：University of California System; University of California San Francisco; University of California System; University of California Davis; University of California System; University of California Davis
摘要：It is a common interest in medicine to determine whether a hospital meets a benchmark created from an aggregate reference population, after accounting for differences in distributions of multiple covariates. Due to the difficulties of collecting individual-level data, however, it is often the case that only marginal distributions of the covariates are available, making covariate-adjusted comparison challenging. We propose and evaluate a novel approach for conducting indirect standardization wh...
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作者：Li, Li; Jara, Alejandro; Jose Garcia-Zattera, Maria; Hanson, Timothy E.
作者单位：University of New Mexico; Pontificia Universidad Catolica de Chile; Medtronic
摘要：Motivated by data gathered in an oral health study, we propose a Bayesian nonparametric approach for population-averaged modeling of correlated time-to-event data, when the responses can only be determined to lie in an interval obtained from a sequence of examination times and the determination of the occurrence of the event is subject to misclassification. The joint model for the true, unobserved time-to-event data is defined semiparametrically; proportional hazards, proportional odds, and ac...
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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：Ding, Peng; Feller, Avi; Miratrix, Luke
作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley; Harvard University
摘要：Understanding and characterizing treatment effect variation in randomized experiments has become essential for going beyond the black box of the average treatment effect. Nonetheless, traditional statistical approaches often ignore or assume away such variation. In the context of randomized experiments, this article proposes a framework for decomposing overall treatment effect variation into a systematic component explained by observed covariates and a remaining idiosyncratic component. Our fr...
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作者：Fan, Jianqing; Ke, Yuan; Sun, Qiang; Zhou, Wen-Xin
作者单位：Fudan University; Princeton University; University System of Georgia; University of Georgia; University of Toronto; University of California System; University of California San Diego
摘要：Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for estimating the false discovery proportion (FDP) often ignore the effect of heavy-tailedness and the dependence structure among test statistics, and thus may lead to inefficient or even inconsistent estimation. Also, the commonly imposed joint normality assumption is arguably too stringent for many applications. To addres...
