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作者：Liu, Xu; Jiang, Hongmei; Zhou, Yong
作者单位：Northwestern University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Shanghai University of Finance & Economics
摘要：In this article, we develop a varying-coefficient density-ratio model for case-control studies. The case and control samples come from two different distributions. Under the model assumption, the ratio of the two densities is related to the linear combination of covariates with varying coefficients through a known function. A special case is the exponential tilt model where the log ratio of the two densities is a linear function of covariates. We propose a local empirical likelihood (EL) appro...
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作者：Miao, Hongyu; Wu, Hulin; Xue, Hongqi
作者单位：University of Rochester
摘要：Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated for the first time. We develop the likelihood-based parameter estimation and inference methods for GODE models. We propose robust computing algorithms and rigorously investigate the asymptotic properties of the proposed estimator by considering both measurement errors and numerical errors in solving ODEs. The si...
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作者：Zhu, Hongtu; Khondker, Zakaria; Lu, Zhaohua; Ibrahim, Joseph G.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：We propose a Bayesian generalized low-rank regression model (GLRR) for the analysis of both high-dimensional responses and covariates. This development is motivated by performing searches for associations between genetic variants and brain imaging phenotypes. GLRR integrates a low rank matrix to approximate the high-dimensional regression coefficient matrix of GLRR and a dynamic factor model to model the high-dimensional covariance matrix of brain imaging phenotypes. Local hypothesis testing i...
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作者：Liu, Dandan; Zheng, Yingye; Prentice, Ross L.; Hsu, Li
作者单位：Vanderbilt University; Fred Hutchinson Cancer Center
摘要：Accurate and individualized risk prediction is critical for population control of chronic diseases such as cancer and cardiovascular disease. Large cohort studies provide valuable resources for building risk prediction models, as the risk factors are collected at the baseline and subjects are followed over time until disease occurrence or termination of the study. However, for rare diseases the baseline risk may not be estimated reliably based on cohort data only, due to sparse events. In this...
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作者：Wang, Chao; Liu, Heng; Yao, Jian-feng; Davis, Richard A.; Li, Wai Keung
作者单位：University of Hong Kong; Alphabet Inc.; Google Incorporated; Columbia University
摘要：This article studies theory and inference of an observation-driven model for time series of counts. It is assumed that the observations follow a Poisson distribution conditioned on an accompanying intensity process, which is equipped with a two-regime structure according to the magnitude of the lagged observations. Generalized from the Poisson autoregression, it allows more flexible, and even negative correlation, in the observations, which cannot be produced by the single-regime model. Classi...
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作者：Allen, Genevera I.; Grosenick, Logan; Taylor, Jonathan
作者单位：Rice University; Rice University; Baylor College of Medicine; Baylor College Medical Hospital; Baylor College of Medicine; Baylor College Medical Hospital; Stanford University; Stanford University
摘要：Variables in many big-data settings are structured, arising, for example, from measurements on a regular grid as in imaging and time series or from spatial-temporal measurements as in climate studies. Classical multivariate techniques ignore these structural relationships often resulting in poor performance. We propose a generalization of principal components analysis (PCA) that is appropriate for massive datasets with structured variables or known two-way dependencies. By finding the best low...
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作者：Joyce, Patrick M.; Malec, Donald; Little, Roderick J. A.; Gilary, Aaron; Navarro, Alfredo; Asiala, Mark E.
作者单位：Centers for Disease Control & Prevention - USA; CDC National Center for Health Statistics (NCHS); University of Michigan System; University of Michigan
摘要：Section 203 of the Voting Rights Act includes provisions requiring the use of election materials in languages other than English for states or political subdivisions, specifically, when a minimum number of voting age U.S. citizens of specified language minority groups who are unable to speak English very well and have obtained less than a fifth-grade education is met. Data on these characteristics are provided by the 2010 Census and the American Community Survey (ACS), a general purpose sample...
