المؤلفون: Bradley C. Saul, Michael G. Hudgens

المصدر: Journal of Statistical Software, Vol 92, Iss 1, Pp 1-15 (2020)

مصطلحات موضوعية: empirical sandwich variance estimator, estimating equations, m-estimation, robust statistics, Statistics, HA1-4737

وصف الملف: electronic resource

العلاقة: https://www.jstatsoft.org/index.php/jss/article/view/3470Test; https://doaj.org/toc/1548-7660Test

الوصول الحر: https://doaj.org/article/eecd8647dd5e488089b990661a66e96cTest

المؤلفون: Jung Yeon Lee, Myeong-Kyu Kim, Wonkuk Kim

المصدر: Mathematics; Volume 8; Issue 2; Pages: 217

مصطلحات موضوعية: allele read counts, low-coverage, mixture model, next-generation sequencing, sandwich variance estimator

وصف الملف: application/pdf

العلاقة: Computational and Applied Mathematics; https://dx.doi.org/10.3390/math8020217Test

الإتاحة: https://doi.org/10.3390/math8020217Test

المؤلفون: Boos, Dennis D.

المصدر: Scandinavian Journal of Statistics, 2004 Sep 01. 31(3), 387-401.

الوصول الحر: https://www.jstor.org/stable/4616837Test

المؤلفون: Rizopoulos, Dimitris, Verbeke, Geert, Molenberghs, Geert

المصدر: Biometrika, 2008 Mar 01. 95(1), 63-74.

الوصول الحر: https://www.jstor.org/stable/20441443Test

المؤلفون: Lai, Tze Leung, Shih, Mei-Chiung

المصدر: Biometrika, 2003 Dec 01. 90(4), 859-879.

الوصول الحر: https://www.jstor.org/stable/30042093Test

المؤلفون: Zhang, Min, Yu, Youfei, Wang, Shikun, Salvatore, Maxwell, G. Fritsche, Lars, He, Zihuai, Mukherjee, Bhramar

مصطلحات موضوعية: type I error, gene‐environment interaction, generalized additive model (GAM), independence, joint tests, power, robust tests, sandwich variance estimator, Statistics and Numeric Data, Public Health, Medicine (General), Science, Social Sciences, Health Sciences

وصف الملف: application/pdf

العلاقة: Zhang, Min; Yu, Youfei; Wang, Shikun; Salvatore, Maxwell; G. Fritsche, Lars; He, Zihuai; Mukherjee, Bhramar (2020). "Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions." Statistics in Medicine 39(11): 1675-1694.; https://hdl.handle.net/2027.42/154951Test; Statistics in Medicine; Tchetgen Tchetgen EJ, Kraft P. On the robustness of tests of genetic associations incorporating gene‐environment interaction when the environmental exposure is misspecified. Epidemiology. 2011; 22 ( 2 ): 257 ‐ 261.; Lin X, Lee S, Christiani DC, Lin X. Test for interactions between a genetic marker set and environment in generalized linear models. Biostatistics. 2013; 14 ( 4 ): 667 ‐ 681.; Sun R, Carroll RJ, Christiani DC, Lin X. Testing for gene‐environment interaction under exposure misspecification. Biometrics. 2018; 74 ( 2 ): 653 ‐ 662.; He Z, Zhang M, Lee S, et al. Set‐based tests for the gene‐environment interaction in longitudinal studies. J Am Stat Assoc. 2017; 112 ( 519 ): 966 ‐ 978.; Voorman A, Lumley T, McKnight B, Rice K. Behavior of QQ‐plots and genomic control in studies of gene‐environment interaction. PLoS One. 2011; 6 ( 5 ): e19416.; Dai JY, LeBlanc M, Kooperberg C. Semiparametric estimation exploiting covariate independence in two‐phase randomized trials. Biometrics. 2009; 65 ( 1 ): 178 ‐ 187.; Dai JY, Kooperberg C, Leblanc M, Prentice RL. Two‐stage testing procedures with independent filtering for genome‐wide gene‐environment interaction. Biometrika. 2012; 99 ( 4 ): 929 ‐ 944.; Hastie TJ, TRJ. Generalized additive models. Chapman & Hall/CRC Monographs on Statistics and Applied Probability. 1st ed. Chapman and Hall/CRC; 1990.; Duchon J. Splines minimizing rotation‐invariant semi‐norms in Sobolev spaces. In: Schempp W, Zeller K, eds. Constructive Theory of Functions of Several Variables. Lecture Notes in Mathematics. Vol 571. Berlin, Heidelberg: Springer; 1977: 85 ‐ 100.; Boos DD, Stefanski LA. Essential Statistical Inference: Theory and Methods. New York: Springer US; 2013.; Fritsche LG, Gruber SB, Wu Z, et al. Association of polygenic risk scores for multiple cancers in a phenome‐wide study: results from the Michigan genomics initiative. Am J Hum Genet. 2018 Jun 7; 102 ( 6 ): 1048 ‐ 1061. https://doi.org/10.1016/j.ajhg.2018.04.001Test.; Fokkema M Christoffersen B. pre: Prediction Rule Ensembles. R package version 0.7.1; 2019. https://CRAN.RTest‐project.org/package=pre. Accessed November 29, 2019.; Friedmand JH, Popescu BE. Predictive learning via rule ensemble. Ann Appl Stat. 2008; 2 ( 3 ): 916 ‐ 954.; Bateson W. Mendel’s Principles of Heredity. Cambridge, UK: Cambridge University Press; 1909.; Vansteelandt S, Vanderweele TJ, Tchetgen EJ, Robins JM. Multiply robust inference for statistical interactions. J Am Stat Assoc. 2008; 103 ( 484 ): 1693 ‐ 1704.; Rosenblum M, van der Laan MJ. Using regression models to analyze randomized trials: asymptotically valid hypothesis tests despite incorrectly specified models. Biometrics. 2009; 65 ( 3 ): 937 ‐ 945.; Tchetgen Tchetgen EJ. Multiple‐Robust Estimation of an Odds Ratio Interaction. Harvard University Biostatistics Working Paper Series; 2012:paper 142.; Cornelis MC, Tchetgen Tchetgen EJ, Liang L, et al. Gene‐environment interactions in genome‐wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes. Am J Epidemiol. 2012; 175 ( 3 ): 191 ‐ 202.; van der Vaart AW. Asymptotic Statistics. Cambridge, United Kingdom: Cambridge University Press; 1998.

الإتاحة: https://doi.org/10.1002/sim.8505Test
https://doi.org/10.1016/j.ajhg.2018.04.001Test
https://hdl.handle.net/2027.42/154951Test

المؤلفون: Michael G. Hudgens, Bradley Saul

المساهمون: NIH

المصدر: Journal of Statistical Software; Vol 92 (2020); 1-15
J Stat Softw
Journal of Statistical Software, Vol 92, Iss 1, Pp 1-15 (2020)

مصطلحات موضوعية: FOS: Computer and information sciences, Statistics and Probability, Heteroscedasticity, empirical sandwich variance estimator, estimating equations, M-estimation, robust statistics, R, Robust statistics, Estimating equations, Statistics - Applications, 01 natural sciences, Article, m-estimation, Set (abstract data type), 010104 statistics & probability, 03 medical and health sciences, 0302 clinical medicine, Applied mathematics, Applications (stat.AP), Point estimation, 0101 mathematics, lcsh:Statistics, lcsh:HA1-4737, Mathematics, Autocorrelation, Estimator, Variance (accounting), Statistics, Probability and Uncertainty, 030217 neurology & neurosurgery, Software

وصف الملف: PB

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39777c982bfa609f07bb5e799f4b7d3eTest
https://www.jstatsoft.org/index.php/jss/article/view/v092i02Test

المؤلفون: Jonathan Koh, Anthony C. Davison, Erwan Koch

المصدر: Statist. Sci. 34, no. 4 (2019), 584-590

مصطلحات موضوعية: Statistics and Probability, sandwich variance estimator, General Mathematics, Design of experiments, Inference, Estimator, Variance (accounting), infinitesimal jackknife, Bootstrap, regression diagnostics, Statistics, model mis-specification, Statistics, Probability and Uncertainty, Regression diagnostic, Statistic, designed experiment, Mathematics

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4d23a1978946ad5c625b7bde854c9333Test
https://projecteuclid.org/euclid.ss/1578474023Test

المؤلفون: Shu, Di, Young, Jessica G., Toh, Sengwee, Wang, Rui

المصدر: Harvard University Biostatistics Working Paper Series

مصطلحات موضوعية: Clustered data, Cox model, Inverse probability weighting, Marginal hazard ratio, sandwich variance estimator, Biostatistics, stat, envir

العلاقة: https://biostats.bepress.com/harvardbiostat/paper219Test

الإتاحة: https://biostats.bepress.com/harvardbiostat/paper219Test

المؤلفون: Davison, Anthony C., Koch, Erwan, Koh, Jonathan

مصطلحات موضوعية: Bootstrap, designed experiment, infinitesimal jackknife, model mis-specification, regression diagnostics, sandwich variance estimator

وصف الملف: application/pdf

العلاقة: https://projecteuclid.org/euclid.ss/1578474023Test; Statist. Sci. 34, no. 4 (2019), 584-590

الإتاحة: https://doi.org/10.1214/19-STS746Test
https://projecteuclid.org/euclid.ss/1578474023Test