BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20260415T071947EDT-3622w8X0v7@132.216.98.100 DTSTAMP:20260415T111947Z 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:https://www.mcgill.ca/mathstat/channels/event/olawale-fatai-ayilara-uni versity-manitoba-268420 END:VEVENT END:VCALENDAR