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作者：Dai, Chenguang; Liu, Jun S.
作者单位：Harvard University
摘要：By mixing the target posterior distribution with a surrogate distribution, of which the normalizing constant is tractable, we propose a method for estimating the marginal likelihood using the Wang-Landau algorithm. We show that a faster convergence of the proposed method can be achieved via the momentum acceleration. Two implementation strategies are detailed: (i) facilitating global jumps between the posterior and surrogate distributions via the multiple-try Metropolis (MTM); (ii) constructin...
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作者：Li, Jialiang; Lv, Jing; Wan, Alan T. K.; Liao, Jun
作者单位：National University of Singapore; Southwest University - China; City University of Hong Kong; Renmin University of China
摘要：Model average techniques are very useful for model-based prediction. However, most earlier works in this field focused on parametric models and continuous responses. In this article, we study varying coefficient multinomial logistic models and propose a semiparametric model averaging prediction (SMAP) approach for multi-category outcomes. The proposed procedure does not need any artificial specification of the index variable in the adopted varying coefficient sub-model structure to forecast th...
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作者：Qiu, Yumou; Zhou, Xiao-Hua
作者单位：Iowa State University; Peking University
摘要：Partial correlations are commonly used to analyze the conditional dependence among variables. In this work, we propose a hierarchical model to study both the subject- and population-level partial correlations based on multi-subject time-series data. Multiple testing procedures adaptive to temporally dependent data with false discovery proportion control are proposed to identify the nonzero partial correlations in both the subject and population levels. A computationally feasible algorithm is d...
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作者：Zhang, Zhengwu; Wang, Xiao; Kong, Linglong; Zhu, Hongtu
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Purdue University System; Purdue University; University of Alberta; University of North Carolina; University of North Carolina Chapel Hill
摘要：This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar covariates on function...
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作者：Yu, Jiahui; Shi, Jian; Liu, Anna; Wang, Yuedong
作者单位：Boston University; PayPal Holdings, Inc.; University of Massachusetts System; University of Massachusetts Amherst; University of California System; University of California Santa Barbara
摘要：Density estimation plays a fundamental role in many areas of statistics and machine learning. Parametric, nonparametric, and semiparametric density estimation methods have been proposed in the literature. Semiparametric density models are flexible in incorporating domain knowledge and uncertainty regarding the shape of the density function. Existing literature on semiparametric density models is scattered and lacks a systematic framework. In this article, we consider a unified framework based ...
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作者：Zhang, Xianyang; Chen, Jun
作者单位：Texas A&M University System; Texas A&M University College Station; Mayo Clinic; Mayo Clinic
摘要：Conventional multiple testing procedures often assume hypotheses for different features are exchangeable. However, in many scientific applications, additional covariate information regarding the patterns of signals and nulls are available. In this article, we introduce an FDR control procedure in large-scale inference problem that can incorporate covariate information. We develop a fast algorithm to implement the proposed procedure and prove its asymptotic validity even when the underlying lik...
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作者：Tian, Qinglong; Meng, Fanqi; Nordman, Daniel J.; Meeker, William Q.
作者单位：Iowa State University
摘要：This article describes prediction methods for the number of future events from a population of units associated with an on-going time-to-event process. Examples include the prediction of warranty returns and the prediction of the number of future product failures that could cause serious threats to property or life. Important decisions such as whether a product recall should be mandated are often based on such predictions. Data, generally right-censored (and sometimes left truncated and right-...
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作者：Jewell, Nicholas P.
作者单位：University of London; London School of Hygiene & Tropical Medicine
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作者：Das, Kiranmoy; Ghosh, Pulak; Daniels, Michael J.
作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; Indian Institute of Management (IIM System); Indian Institute of Management Bangalore; State University System of Florida; University of Florida
摘要：As the population of the older individuals continues to grow, it is important to study the relationship among the variables measuring financial health and physical health of the older individuals to better understand the demand for healthcare, and health insurance. We propose a semiparametric approach to jointly model these variables. We use data from the Health and Retirement Study which includes a set of correlated longitudinal variables measuring financial and physical health. In particular...
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作者：Awan, Jordan; Slavkovic, Aleksandra
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Differential privacy (DP) provides a framework for provable privacy protection against arbitrary adversaries, while allowing the release of summary statistics and synthetic data. We address the problem of releasing a noisy real-valued statistic vectorT, a function of sensitive data under DP, via the class ofK-norm mechanisms with the goal of minimizing the noise added to achieve privacy. First, we introduce thesensitivity space of T, which extends the concepts of sensitivity polytope and sensi...
