BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Satish Iyengar (Department of Statistics\, University of Pittsburg h\, Pittsburgh\, PA\, USA) DTSTART:20240925T111500Z DTEND:20240925T120000Z DTSTAMP:20260422T161048Z UID:gbgstats/65 DESCRIPTION:Title: A clustering problem arising in psychiatry\nby Satish Iyengar (Depar tment of Statistics\, University of Pittsburgh\, Pittsburgh\, PA\, USA) as part of Gothenburg statistics seminar\n\nLecture held in MVL14.\n\nAbstra ct\nCurrent psychiatric diagnoses are based primarily on self-reported exp eriences. Unfortunately\, treatments for the diagnoses are not effective f or all patients. One hypothesized reason is that ``artificial grouping of heterogeneous syndromes with different pathophysiological mechanisms into one disorder.'' To address this problem\, the US National Institute of Men tal Health instituted the Research Domain Criteria framework in 2009. This research framework calls for integrating data from many levels of informa tion: genes\, cells\, molecules\, circuits\, physiology\, behavior\, and s elf-report. Clustering comes to the forefront as a key tool in this effort . In this talk\, I present a case study of the use of mixture models to cl uster older adults based on measures of sleep from three domains: diary\, actigraphy\, and polysomnography. Challenges in this effort include the us e of mixtures of skewed distributions\, a large number of potential cluste ring variables\, and seeking clinically meaningful solutions. We present n ovel variable selection algorithms\, study them by simulation\, and demons trate our methods on the sleep data. This work is joint with Meredith Wall ace.\n LOCATION:https://researchseminars.org/talk/gbgstats/65/ END:VEVENT END:VCALENDAR