Show simple item record

Improving estimation and prediction in linear regression incorporating external information from an established reduced model

dc.contributor.authorCheng, Wenting
dc.contributor.authorTaylor, Jeremy M. G.
dc.contributor.authorVokonas, Pantel S.
dc.contributor.authorPark, Sung Kyun
dc.contributor.authorMukherjee, Bhramar
dc.date.accessioned2018-05-15T20:15:56Z
dc.date.available2019-06-03T15:24:19Zen
dc.date.issued2018-04-30
dc.identifier.citationCheng, Wenting; Taylor, Jeremy M. G.; Vokonas, Pantel S.; Park, Sung Kyun; Mukherjee, Bhramar (2018). "Improving estimation and prediction in linear regression incorporating external information from an established reduced model." Statistics in Medicine 37(9): 1515-1530.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/143779
dc.publisherCRC Press
dc.publisherWiley Periodicals, Inc.
dc.subject.otherconstrained estimation
dc.subject.otherprediction models
dc.subject.otherBayesian methods
dc.titleImproving estimation and prediction in linear regression incorporating external information from an established reduced model
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/143779/1/sim7600_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/143779/2/sim7600.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/143779/3/sim7600-sup-0001-Supplementary.pdf
dc.identifier.doi10.1002/sim.7600
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceChen A, Owen AB, Shi M. Data enriched linear regression. Electron J Stat. 2015; 9 ( 1 ): 1078 ‐ 1112.
dc.identifier.citedreferenceImbens GW, Lancaster T. Combining micro and macro data in microeconometric models. The Rev Econ Stud. 1994; 61 ( 4 ): 655 ‐ 680.
dc.identifier.citedreferenceQin J. Combining parametric and empirical likelihoods. Biometrika. 2000; 87 ( 2 ): 484 ‐ 490.
dc.identifier.citedreferenceBell B, Rose CL, Damon A. The normative aging study: an interdisciplinary and longitudinal study of health and aging. The Int J Aging Human Dev. 1972; 3 ( 1 ): 5 ‐ 17.
dc.identifier.citedreferenceHu H, Shih R, Rothenberg S, Schwartz BS. The epidemiology of lead toxicity in adults: measuring dose and consideration of other methodologic issues. Environ Health Perspectives. 2007; 115 ( 3 ): 455 ‐ 462.
dc.identifier.citedreferenceBarry PSI, Mossman DB. Lead concentrations in human tissues. Br J Ind Med. 1970; 27 ( 4 ): 339 ‐ 351.
dc.identifier.citedreferencePark SK, Mukherjee B, Xia X, et al. Bone lead level prediction models and their application to examining the relationship of lead exposure and hypertension in the third national health and nutrition examination survey (nhanes‐iii). J Occup Environ Med / Am College Occup Environ Med. 2009; 51 ( 12 ): 1422 ‐ 1436.
dc.identifier.citedreferenceGilks WR, Roberts GO. Strategies for improving mcmc. In: Gilks WR, Richardson S, Spiegelhalter D, eds. Markov chain monte carlo in practice. London: Chapman and Hall; 1996: 89 ‐ 110.
dc.identifier.citedreferenceRoberts GO. Markov chain concepts related to sampling algorithms. In: Gilks WR, Richardson S, Spiegelhalter D, eds. Markov Chain Monte Carlo in Practice. London: Chapman and Hall; 1996: 45 ‐ 54.
dc.identifier.citedreferenceAbdi H. Partial regression coefficients. In: Lewis‐Beck MS, Bryman A, Liao TF, eds. The Encyclopedia of Social Science Research Methods. CA: Sage Publications, Inc.; 2004: 796 ‐ 798.
dc.identifier.citedreferenceCarpenter J, Bithell J. Bootstrap confidence intervals: when, which, what? a practical guide for medical statisticians. Stat Med. 2000; 19 ( 9 ): 1141 ‐ 1164.
dc.identifier.citedreferenceEfron B. Nonparametric standard errors and confidence intervals. The Can J Stat / La Revue Canadienne de Statistique. 1981; 9 ( 2 ): 139 ‐ 158.
dc.identifier.citedreferenceEfron B, Tibshirani R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci. 1986; 1 ( 1 ): 54 ‐ 75.
dc.identifier.citedreferenceLesaffre E, Lawson AB. Choosing the prior distribution. In: Lesaffre E, Lawson AB, eds. Bayesian Biostatistics. West Sussex: John Wiley and Sons, Ltd; 2012: 104 ‐ 138.
dc.identifier.citedreferenceCarlin BP, Louis TA. Bayesian Methods for Data Analysis. Boca Raton: CRC Press; 2009.
dc.identifier.citedreferenceChatterjee N, Chen Y‐H, Maas P, Carroll RJ. Constrained maximum likelihood estimation for model calibration using summary‐level information from external big data sources. J Am Stat Assoc. 2016; 111 ( 513 ): 107 ‐ 117.
dc.identifier.citedreferenceQin J, Zhang H, Li P, Albanes D, Yu K. Using covariate‐specific disease prevalence information to increase the power of case‐control studies. Biometrika. 2015; 102 ( 1 ): 169 ‐ 180.
dc.identifier.citedreferenceD’Agostino RB, Grundy S, Sullivan LM, Wilson P, the CHD Risk Prediction Group. Validation of the framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. The J Am Med Assoc. 2001; 286 ( 2 ): 180 ‐ 187.
dc.identifier.citedreferenceThompson IM, Ankerst DP, Chi C, et al. Assessing prostate cancer risk: results from the prostate cancer prevention trial. J Nat Cancer Inst. 2006; 98 ( 8 ): 529 ‐ 534.
dc.identifier.citedreferenceGail MH, Brinton LA, Byar DP, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Nat Cancer Inst. 1989; 81 ( 24 ): 1879 ‐ 1886.
dc.identifier.citedreferenceGeweke J. Exact inference in the inequality constrained normal linear regression model. J Appl Econometrics. 1986; 1 ( 2 ): 127 ‐ 141.
dc.identifier.citedreferenceDunson DB, Neelon B. Bayesian inference on order‐constrained parameters in generalized linear models. Biometrics. 2003; 59 ( 2 ): 286 ‐ 295.
dc.identifier.citedreferenceGunn LH, Dunson DB. A transformation approach for incorporating monotone or unimodal constraints. Biostatistics. 2005; 6 ( 3 ): 434 ‐ 449.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.