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UID:20260601T025650EDT-5044uW0LzX@132.216.98.100
DTSTAMP:20260601T065650Z
DESCRIPTION:Quantile Regression with Nominated Samples: An Application to a
  Bone Mineral Density Study.\n\nThis talk focuses on quantile regression a
 nalysis with maxima or minima nomination sampling designs. These designed 
 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 ran
 k information of nominated sam- ples in the estimation process. Also\, we 
 provide an alternative approach that translates estimation problems with n
 ominated samples to correspond- ing problems under simple random sampling 
 (SRS). Strategies are given to choose proper nomination sampling designs f
 or a given population quantile. Numerical studies show that quantile regre
 ssion models with maxima (or minima) nominated samples have higher relativ
 e eciencies compared with their counterparts under SRS for analyzing the u
 pper (or lower) tail quantiles of the distribution of the response variabl
 e. Results are then implemented on a large cohort study in the Canadian pr
 ovince of Manitoba to analyze quantiles of bone mineral density using avai
 lable covariates. We show that in some cases\, methods based on nomination
  sampling designs require about one tenth of the sample used in SRS to est
 imate the lower or upper tail con- ditional quantiles with comparable mean
  squared errors. This is a dramatic reduction in time and cost compared wi
 th the usual SRS approach.\n
DTSTART:20170601T193000Z
DTEND:20170601T203000Z
LOCATION:Room D4-2019\, CA\, QC\, Sherbrooke\, Seminar Statistique Sherbroo
 ke\, 2500\, boul. de l'Université
SUMMARY:Olawale Fatai Ayilara\, University of Manitoba
URL:/mathstat/channels/event/olawale-fatai-ayilara-uni
 versity-manitoba-268420
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