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CATEGORIES:Lectures and Guest Speakers
DESCRIPTION:DrPH Candidate\, Biostatistics\n\nTitle\n\nVariable Selection i
n Accelerated Failure Time (AFT) frailty models: An application of Penalize
d Quasi-Likelihood\n\nSummary \n\nVariable selection is one of the standard
ways of selecting models in large scale datasets. It has applications in m
any fields of research study\, especially in large multi-center clinical tr
ials. One of the prominent methods in variable selection is the penalized l
ikelihood\, which is both consistent and efficient. However\, the penalized
selection is significantly challenging under the influence of random (frai
lty) covariates. It is even more complicated when there is involvement of c
ensoring as it may not have a closed-form solution for the marginal log-lik
elihood. Therefore\, we applied the penalized quasi-likelihood (PQL) approa
ch that approximates the solution for such a likelihood. In addition\, we i
ntroduce an adaptive penalty function that makes the selection on both fixe
d and frailty effects in a left-censored dataset for a parametric AFT frail
ty model. We also compared our penalty function with other established proc
edures via their performance on accurately choosing the significant coeffic
ients and shrinking the non-significant coefficients to zero.
DTEND:20191205T150000Z
DTSTAMP:20240712T200352Z
DTSTART:20191205T140000Z
LOCATION:Hendricks Hall\, 3001
SEQUENCE:0
SUMMARY:Dissertation Defense: Sarbesh Pandeya
UID:tag:localist.com\,2008:EventInstance_31944243483099
URL:https://calendar.georgiasouthern.edu/event/dissertation_defense_sarbesh
_pandeya
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