Estimation and testing based on data subject to measurement errors: from parametric to non-parametric likelihood methods.
Measurement error (ME) problems can cause bias or inconsistency of statistical inferences. When investigators are unable to obtain correct measurements of biological assays, special techniques to quantify MEs need to be applied. Sampling based on repeated measurements is a common strategy to allow f...
| Publicado en: | Statistics in Medicine Vol. 31; no. 22; pp. 2498 - 2513 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Sep2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104367096&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104367096 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: Sep2012 vid: 31 iid: 22 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104367096 104367096 NLM21805485 2011682488 10.1002/sim.4304 NLM21805485 PMC3886575 104367096 ppf: 2498 ppct: 15 formats: tig: atl: Estimation and testing based on data subject to measurement errors: from parametric to non-parametric likelihood methods. aug: au: Vexler A Tsai WM Malinovsky Y Vexler, Albert Tsai, Wan-Min Malinovsky, Yaakov affil: Department of Biostatistics, The State University of New York, Buffalo, NY 14214, USA sug: subj: Biological Markers Analysis Data Analysis, Statistical Probability Cholesterol Blood Computer Simulation Systems Analysis Myocardial Infarction Blood ab: Measurement error (ME) problems can cause bias or inconsistency of statistical inferences. When investigators are unable to obtain correct measurements of biological assays, special techniques to quantify MEs need to be applied. Sampling based on repeated measurements is a common strategy to allow for ME. This method has been well addressed in the literature under parametric assumptions. The approach with repeated measures data may not be applicable when the replications are complicated because of cost and/or time concerns. Pooling designs have been proposed as cost-efficient sampling procedures that can assist to provide correct statistical operations based on data subject to ME. We demonstrate that a mixture of both pooled and unpooled data (a hybrid pooled-unpooled design) can support very efficient estimation and testing in the presence of ME. Nonparametric techniques have not been well investigated to analyze repeated measures data or pooled data subject to ME. We propose and examine both the parametric and empirical likelihood methodologies for data subject to ME. We conclude that the likelihood methods based on the hybrid samples are very efficient and powerful. The results of an extensive Monte Carlo study support our conclusions. Real data examples demonstrate the efficiency of the proposed methods in practice. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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