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作者：Lee, Giwhyun; Byon, Eunshin; Ntaimo, Lewis; Ding, Yu
作者单位：Texas A&M University System; Texas A&M University College Station; University of Michigan System; University of Michigan
摘要：This study presents a Bayesian parametric model for the purpose of estimating the extreme load on a wind turbine. The extreme load is the highest stress level imposed on a turbine structure that the turbine would experience during its service lifetime. A wind turbine should be designed to resist such a high load to avoid catastrophic structural failures. To assess the extreme load, turbine structural responses are evaluated by conducting field measurement campaigns or performing aeroelastic si...
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作者：Jiang, Xun; Dey, Dipak K.; Prunier, Rachel; Wilson, Adam M.; Holsinger, Kent E.
作者单位：University of Connecticut; Yale University; Connecticut State University System; Western Connecticut State University; University of Connecticut
摘要：Understanding the mechanisms that allow biological species to co-occur is of great interest to ecologists. Here we investigate the factors that influence co-occurrence of members of the genus Protea in the Cape Floristic Region of southwestern Africa, a global hot spot of biodiversity. Due to the binomial nature of our response, a critical issue is to choose appropriate link functions for the regression model. In this paper we propose a new family of flexible link functions for modeling binomi...
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作者：Airoldi, Edoardo M.; Wang, Xiaopei; Lin, Xiaodong
作者单位：Harvard University; University System of Ohio; University of Cincinnati; Rutgers University System; Rutgers University New Brunswick
摘要：We consider the problem of quantifying temporal coordination between multiple high-dimensional responses. We introduce a family of multi-way stochastic blockmodels suited for this problem, which avoids preprocessing steps such as binning and thresholding commonly adopted for this type of data, in biology. We develop two inference procedures based on collapsed Gibbs sampling and variational methods. We provide a thorough evaluation of the proposed methods on simulated data, in terms of membersh...
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作者：Handorf, Elizabeth A.; Bekelman, Justin E.; Heitjan, Daniel F.; Mitra, Nandita
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Fox Chase Cancer Center; University of Pennsylvania; University of Pennsylvania
摘要：Estimates of the effects of treatment on cost from observational studies are subject to bias if there are unmeasured confounders. It is therefore advisable in practice to assess the potential magnitude of such biases. We derive a general adjustment formula for loglinear models of mean cost and explore special cases under plausible assumptions about the distribution of the unmeasured confounder. We assess the performance of the adjustment by simulation, in particular, examining robustness to a ...
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作者：Pannekoek, Jeroen; Shlomo, Natalie; De Waal, Ton
作者单位：University of Manchester
摘要：A common problem faced by statistical institutes is that data may be missing from collected data sets. The typical way to overcome this problem is to impute the missing data. The problem of imputing missing data is complicated by the fact that statistical data often have to satisfy certain edit rules and that values of variables across units sometimes have to sum up to known totals. For numerical data, edit rules are most often formulated as linear restrictions on the variables. For example, f...
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作者：Ye, Zhi-Sheng; Hong, Yili; Xie, Yimeng
作者单位：Hong Kong Polytechnic University; Virginia Polytechnic Institute & State University
摘要：The main objective of accelerated life tests (ALTs) is to predict fraction failings of products in the field. However, there are often discrepancies between the predicted fraction failing from the lab testing data and that from the field failure data, due to the yet unobserved heterogeneities in usage and operating conditions. Most previous research on ALT planning and data analysis ignores the discrepancies, resulting in inferior test plans and biased predictions. In this paper we model the h...
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作者：Gruhl, Jonathan; Erosheva, Elena A.; Crane, Paul K.
作者单位：University of Washington; University of Washington Seattle; Harborview Medical Center; University of Washington; University of Washington Seattle
摘要：Multivariate data that combine binary, categorical, count and continuous outcomes are common in the social and health sciences. We propose a semiparametric Bayesian latent variable model for multivariate data of arbitrary type that does not require specification of conditional distributions. Drawing on the extended rank likelihood method by Hoff [Ann. Appl. Stat. 1 (2007) 265-283], we develop a semiparametric approach for latent variable modeling with mixed outcomes and propose associated Mark...
