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作者：Plumlee, Matthew
作者单位：University of Michigan System; University of Michigan
摘要：Bayesian calibration is used to study computer models in the presence of both a calibration parameter and model bias. The parameter in the predominant methodology is left undefined. This results in an issue, where the posterior of the parameter is suboptimally broad. There has been no generally accepted alternatives to date. This article proposes using Bayesian calibration, where the prior distribution on the bias is orthogonal to the gradient of the computer model. Problems associated with Ba...
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作者：Chan, Kin Wai; Yau, Chun Yip
作者单位：Chinese University of Hong Kong
摘要：The time-average covariance matrix (TACM) Sigma := Sigma(k is an element of Z) Gamma(k), where Gamma(k) is the auto-covariance function, is an important quantity for the inference of the mean of an R-d-valued stationary process (d >= 1). This article proposes two recursive estimators for Sigma with optimal asymptotic mean square error (AMSE) under different strengths of serial dependence. The optimal estimator involves a batch size selection, which requires knowledge of a smoothness parameter ...
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作者：Gelman, Andrew; Carlin, John
作者单位：Columbia University; Columbia University; University of Melbourne; Murdoch Children's Research Institute; University of Melbourne
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作者：Yang, Yun; Tokdar, Surya T.
作者单位：Duke University
摘要：In spite of the recent surge of interest in quantile regression, joint estimation of linear quantile planes remains a great challenge in statistics and econometrics. We propose a novel parameterization that characterizes any collection of noncrossing quantile planes over arbitrarily shaped convex predictor domains in any dimension by means of unconstrained scalar, vector and function valued parameters. Statistical models based on this parameterization inherit a fast computation of the likeliho...
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作者：Zhu, Hongtu; Shen, Dan; Peng, Xuewei; Liu, Leo Yufeng
作者单位：University of Texas System; UTMD Anderson Cancer Center; University of North Carolina; University of North Carolina Chapel Hill; State University System of Florida; University of South Florida; State University System of Florida; University of South Florida; Texas A&M University System; Texas A&M University College Station; University of North Carolina; University of North Carolina Chapel Hill
摘要：We propose a multiscale weighted principal component regression (MWPCR) framework for the use of high-dimensional features with strong spatial features (e.g., smoothness and correlation) to predict an outcome variable, such as disease status. This development is motivated by identifying imaging biomarkers that could potentially aid detection, diagnosis, assessment of prognosis, prediction of response to treatment, and monitoring of disease status, among many others. The MWPCR can be regarded a...
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作者：Vincent, Kyle; Thompson, Steve
作者单位：Bank of Canada; Simon Fraser University
摘要：We present a new design and method for estimating the size of a hidden population best reached through a link-tracing design. The design is based on selecting initial samples at random and then adaptively tracing links to add new members. The inferential procedure involves the Rao-Blackwell theorem applied to a sufficient statistic markedly different from the usual one that arises in sampling from a finite population. The strategy involves a combination of link-tracing and mark-recapture estim...
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作者：Li, Lexin; Zhang, Xin
作者单位：University of California System; University of California Berkeley; State University System of Florida; Florida State University
摘要：Aiming at abundant scientific and engineering data with not only high dimensionality but also complex structure, we study the regression problem with a multidimensional array (tensor) response and a vector predictor. Applications include, among others, comparing tensor images across groups after adjusting for additional covariates, which is of central interest in neuroimaging analysis. We propose parsimonious tensor response regression adopting a generalized sparsity principle. It models all v...
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作者：Ando, Tomohiro; Bai, Jushan
作者单位：University of Melbourne; Columbia University; Nankai University
摘要：This article introduces a new procedure for clustering a large number of financial time series based on high-dimensional panel data with grouped factor structures. The proposed method attempts to capture the level of similarity of each of the time series based on sensitivity to observable factors as well as to the unobservable factor structure. The proposed method allows for correlations between observable and unobservable factors and also allows for cross-sectional and serial dependence and h...
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作者：Zhang, Jingfei; Cao, Jiguo
作者单位：University of Miami; Simon Fraser University
摘要：Finding functional modules in gene regulation networks is an important task in systems biology. Many methods have been proposed for finding communities in static networks; however, the application of such methods is limited due to the dynamic nature of gene regulation networks. In this article, we first propose a statistical framework for detecting common modules in the Drosophila melanogaster time-varying gene regulation network. We then develop both a significance test and a robustness test ...
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作者：Choi, David
作者单位：Carnegie Mellon University
摘要：Randomized experiments on social networks pose statistical challenges, due to the possibility of interference between units. We propose new methods for finding confidence intervals on the attributable treatment effect in such settings. The methods do not require partial interference, but instead require an identifying assumption that is similar to requiring nonnegative treatment effects. Network or spatial information can be used to customize the test statistic; in principle, this can increase...
