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Xihong Lin
Researcher at Harvard University
Publications - 389
Citations - 32083
Xihong Lin is an academic researcher from Harvard University. The author has contributed to research in topics: Population & Genome-wide association study. The author has an hindex of 76, co-authored 361 publications receiving 26162 citations. Previous affiliations of Xihong Lin include Texas A&M University & University of Washington.
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Semiparametric normal transformation models for spatially correlated survival data
Yi Li,Xihong Lin +1 more
TL;DR: In this article, a new class of semiparametric normal transformation models for right-censored spatially correlated survival data is proposed, which assumes that survival outcomes marginally follow a Cox proportional hazard model with unspecified baseline hazard, and their joint distribution is obtained by transforming survival outcomes to normal random variables.
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Impacts of air pollution, temperature, and relative humidity on leukocyte distribution: An epigenetic perspective.
Xu Gao,Elena Colicino,Jincheng Shen,Marianthi-Anna Kioumourtzoglou,Allan C. Just,Jamaji C. Nwanaji-Enwerem,Brent A. Coull,Xihong Lin,Pantel S. Vokonas,Yinan Zheng,Lifang Hou,Joel Schwartz,Andrea A. Baccarelli +12 more
TL;DR: This study suggests that short-term air pollution exposure, temperature, and relative humidity are associated with leukocyte distribution, and provides a successful attempt to use epigenetic patterns to assess the influences of environmental exposures on human immune profiles.
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Bayesian inference in semiparametric mixed models for longitudinal data.
TL;DR: It is argued that the commonly assumed DP prior implies a nonzero mean of the random effect distribution, even when a base measure with mean zero is specified, and can therefore lead to biased estimators and poor inference for the regression coefficients and the spline estimator of the nonparametric function.
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A Tobit Variance-Component Method for Linkage Analysis of Censored Trait Data
TL;DR: This work compares and contrasts the performance of the traditional and tobit VC methods for linkage analysis of censored trait data, and presents a modified VC method that directly models the censoring event, which is called the "tobit VC method."
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GEE-based SNP set association test for continuous and discrete traits in family-based association studies.
TL;DR: This paper proposes a generalized estimating equations (GEEs) based kernel association test, a variance component based testing method, to test for the association between a phenotype and multiple variants in an SNP set jointly using family samples, for both continuous and discrete traits.