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作者：Sun, Qiang; Zhang, Heping
作者单位：University of Toronto; Yale University
摘要：Analysis of high-dimensional data has received considerable and increasing attention in statistics. In practice, we may not be interested in every variable that is observed. Instead, often some of the variables are of particular interest, and the remaining variables are nuisance. To this end, we propose the nuisance penalized regression which does not penalize the parameters of interest. When the coherence between interest parameters and nuisance parameters is negligible, we show that resultin...
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作者：Harris, Trevor; Li, Bo; Steiger, Nathan J.; Smerdon, Jason E.; Narisetty, Naveen; Tucker, J. Derek
作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Columbia University; United States Department of Energy (DOE); Sandia National Laboratories
摘要：Climate field reconstructions (CFRs) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To...
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作者：Bates, Stephen; Candes, Emmanuel; Janson, Lucas; Wang, Wenshuo
作者单位：Stanford University; Stanford University; Harvard University
摘要：Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives. A frequently discussed challenge to apply this method is to construct knockoff variables, which are synthetic variables obeying a crucial exchangeability property with the explanatory variables under study. This article introduces techniques for knockoff generation in gr...
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作者：Fasiolo, Matteo; Wood, Simon N.; Zaffran, Margaux; Nedellec, Raphael; Goude, Yannig
作者单位：University of Bristol; Institut Polytechnique de Paris; ENSTA Paris; Electricite de France (EDF)
摘要：We propose a novel framework for fitting additive quantile regression models, which provides well-calibrated inference about the conditional quantiles and fast automatic estimation of the smoothing parameters, for model structures as diverse as those usable with distributional generalized additive models, while maintaining equivalent numerical efficiency and stability. The proposed methods are at once statistically rigorous and computationally efficient, because they are based on the general b...
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作者：Chan, Lax; Silverman, Bernard W.; Vincent, Kyle
作者单位：University of Nottingham
摘要：Multiple systems estimation strategies have recently been applied to quantify hard-to-reach populations, particularly when estimating the number of victims of human trafficking and modern slavery. In such contexts, it is not uncommon to see sparse or even no overlap between some of the lists on which the estimates are based. These create difficulties in model fitting and selection, and we develop inference procedures to address these challenges. The approach is based on Poisson log-linear regr...
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作者：Ray, Kolyan; Szabo, Botond
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作者：Kuenzer, Thomas; Hormann, Siegfried; Kokoszka, Piotr
作者单位：Graz University of Technology; Colorado State University System; Colorado State University Fort Collins
摘要：We develop an expansion, similar in some respects to the Karhunen-Loeve expansion, but which is more suitable for functional data indexed by spatial locations on a grid. Unlike the traditional Karhunen-Loeve expansion, it takes into account the spatial dependence between the functions. By doing so, it provides a more efficient dimension reduction tool, both theoretically and in finite samples, for functional data with moderate spatial dependence. For such data, it also possesses other theoreti...
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作者：Scealy, Janice L.; Wood, Andrew T. A.
作者单位：Australian National University
摘要：Robust estimation of location for data on the unit sphere is an important problem in directional statistics even though the influence functions of the sample mean direction and other location estimators are bounded. A significant limitation of previous literature on this topic is that robust estimators and procedures have been developed under the assumption that the underlying population is rotationally symmetric. This assumption often does not hold with real data and in these cases there is a...
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作者：Dai, Ben; Shen, Xiaotong; Wang, Junhui; Qu, Annie
作者单位：University of Minnesota System; University of Minnesota Twin Cities; City University of Hong Kong; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Personalized prediction presents an important yet challenging task, which predicts user-specific preferences on a large number of items given limited information. It is often modeled as certain recommender systems focusing on ordinal or continuous ratings, as in collaborative filtering and content-based filtering. In this article, we propose a new collaborative ranking system to predict most-preferred items for each user given search queries. Particularly, we propose a psi-ranker based on rank...
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作者：Hoffman, Kentaro; Hannig, Jan; Zhang, Kai
作者单位：University of North Carolina; University of North Carolina Chapel Hill
