BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//jEvents 2.0 for Joomla//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
UID:75e942ae2889b726ace54395942448fe
CATEGORIES:Seminars
CREATED:20170418T191238
SUMMARY:Ruey Tsay - University of Chicago Booth School of Business
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:<p style="text-align: justify;"><strong>Analysis of Big Dependent Data in E
 conomics and Finance</strong></p><p style="text-align: justify;">Abstract:<
 /p><p style="text-align: justify;">Big data are common in many scientific f
 ields and have attracted much research interest in machine learning, comput
 er science, optimization and statistics. Econometrics and finance are no ex
 ception.</p><p style="text-align: justify;">The goal of analyzing big data 
 is to extract its useful information effectively and timely. In this talk, 
 we introduce analysis of big dependent data by focusing on economics and fi
 nance.</p><p style="text-align: justify;">We review methods available for a
 nalyzing big data and discuss their advantages and limitations. We compare 
 the concepts of sparsity and parsimony in modeling, and emphasize the impac
 t of serial dependence on methods developed for independent data.</p><p sty
 le="text-align: justify;">Examples are used to demonstrate the analysis. Th
 e methods discussed include (a) various penalized likelihood methods for st
 atistical modeling such as LASSO regression and its extensions, (b) transfo
 rmation to functional data for simplification, (c) tree-based methods for c
 lassification and prediction such as bagging, boosting, and random forests,
  (d) some methods for analyzing big dependent data.</p><p style="text-align
 : justify;">The demonstration is carried out using the R software. Cross-va
 lidation (both leave-one-out and K folds) is used to select the penalty (or
  smoothing) parameter. If time permits, we further illustrate some applicat
 ions.</p>
DTSTAMP:20260406T151200Z
DTSTART:20160929T173000Z
DTEND:20160929T190000Z
SEQUENCE:0
TRANSP:OPAQUE
END:VEVENT
END:VCALENDAR