Quantile regression with nominated samples: An application to a bone mineral density study.
This paper studies quantile regression analysis with maxima or minima nomination sampling designs. These designs are often used to obtain more representative samples from the tails of the underlying distribution using the easy to access rank information during the sampling process. We propose new lo...
| Publicado en: | Statistics in Medicine Vol. 37; no. 14; pp. 2267 - 2284 |
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| Autores principales: | , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/30/2018
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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=129952949&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129952949 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 6/30/2018 vid: 37 iid: 14 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 129952949 129952949 NLM29642267 129952949 10.1002/sim.7655 NLM29642267 129952949 ppf: 2267 ppct: 17 formats: tig: atl: Quantile regression with nominated samples: An application to a bone mineral density study. aug: au: Jafari Jozani, Mohammad Ayilara, Olawale F. Leslie, William D. affil: Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada sug: subj: Sample Size Regression Human Manitoba Cost Benefit Analysis Middle Age Aged Time Bone Density Computer Simulation Male Probability Validation Studies Comparative Studies Evaluation Research Multicenter Studies Middle Aged: 45-64 years Aged: 65+ years Male ab: This paper studies quantile regression analysis with maxima or minima nomination sampling designs. These designs are often used to obtain more representative samples from the tails of the underlying distribution using the easy to access rank information during the sampling process. We propose new loss functions to incorporate the rank information of nominated samples in the estimation process. Also, we provide an alternative approach that translates estimation problems with nominated samples to corresponding problems under simple random sampling (SRS). Strategies are given to choose proper nomination sampling designs for a given population quantile. Numerical studies show that quantile regression models with maxima (or minima) nominated samples have higher relative efficiencies compared with their counterparts under SRS for analyzing the upper (or lower) tail quantiles of the distribution of the response variable. Results are then implemented on a large cohort study in the Canadian province of Manitoba to analyze quantiles of bone mineral density using available covariates. We show that in some cases, methods based on nomination sampling designs require about one-tenth of the sample used in SRS to estimate the lower or upper tail conditional quantiles with comparable mean squared errors. This is a dramatic reduction in time and cost compared with the usual SRS approach. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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