Zobrazeno 1 - 10 of 149 pro vyhledávání: '"Grace Wahba"'
4
Statistical tests and identifiability conditions for pooling and analyzing multisite datasets
Autor: Hao Henry, Zhou, Vikas, Singh, Sterling C, Johnson, Grace, Wahba, Zaven, Khachaturian
Publikováno v: Proceedings of the National Academy of Sciences of the United States of America
Significance How can one efficiently combine experimental and observational predictive data from different laboratories into a single predictive model when the laboratories have differently calibrated measuring instruments and the study populations h
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cbe9bc600938253974f1eeabab1caf74
https://pubmed.ncbi.nlm.nih.gov/29386387
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5
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease
Autor: Hao Henry, Zhou, Sathya N, Ravi, Vamsi K, Ithapu, Sterling C, Johnson, Grace, Wahba, Vikas, Singh
Publikováno v: Advances in neural information processing systems. 29
Consider samples from two different data sources {xsi}~Psource and {xti}~Ptarget. We only observe their transformed versions h(xsi) and g(xti), for some known function class h(·) and g(·). Our goal is to perform a statistical test checking if Psour
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::e2d1cb6a9a23aadd2302e27d677dae5b
https://pubmed.ncbi.nlm.nih.gov/29308004
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6
Using distance covariance for improved variable selection with application to learning genetic risk models
Autor: Grace Wahba, Jing Kong, Sijian Wang
Publikováno v: Statistics in Medicine. 34:1708-1720
Variable selection is of increasing importance to address the difficulties of high dimensionality in many scientific areas. In this paper, we demonstrate a property for distance covariance, which is incorporated in a novel feature screening procedure
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2ff6420310c14c0b83b75990b18abbfd
https://doi.org/10.1002/sim.6441
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8
Group variable selection via convex log-exp-sum penalty with application to a breast cancer survivor study
Autor: Victoria L. Champion, Zhigeng Geng, Patrick O. Monahan, Sijian Wang, Menggang Yu, Grace Wahba
Publikováno v: Biometrics. 71:53-62
In many scientific and engineering applications, covariates are naturally grouped. When the group structures are available among covariates, people are usually interested in identifying both important groups and important variables within the selecte
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8db1295cac75c5fcc7a76d669391fd39
https://doi.org/10.1111/biom.12230
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9
Using distance correlation and SS-ANOVA to assess associations of familial relationships, lifestyle factors, diseases, and mortality
Autor: Grace Wahba, Ronald Klein, Barbara E.K. Klein, Kristine E. Lee, Jing Kong
Publikováno v: Proceedings of the National Academy of Sciences. 109:20352-20357
We present a method for examining mortality as it is seen to run in families, and lifestyle factors that are also seen to run in families, in a subpopulation of the Beaver Dam Eye Study. We observe that pairwise distance between death age in related
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cecf7e00c01290a9ba4411ab7f02fab5
https://doi.org/10.1073/pnas.1217269109
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10
Backward multiple imputation estimation of the conditional lifetime expectancy function with application to censored human longevity data
Autor: Jing Kong, Grace Wahba, Ronald Klein, Barbara E.K. Klein
Publikováno v: Proceedings of the National Academy of Sciences of the United States of America. 112(39)
The conditional lifetime expectancy function (LEF) is the expected lifetime of a subject given survival past a certain time point and the values of a set of explanatory variables. This function is attractive to researchers because it summarizes the e
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::810a40c4f002ffaec090a946e5133279
https://pubmed.ncbi.nlm.nih.gov/26371300
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