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DTSTART:20190310T070000
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DTSTART:20191103T060000
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DTSTART;TZID=America/Toronto:20200121T100000
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URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/department
 -seminar-lu-yang-university-amsterdam
LOCATION:M3 - Mathematics 3 200 University Avenue West Room 3127 Waterloo O
 N N2L 3G1 Canada
SUMMARY:Department seminar by Lu Yang\, University of Amsterdam
CLASS:PUBLIC
DESCRIPTION:DIAGNOSTICS FOR REGRESSION MODELS WITH DISCRETE OUTCOMES\n\nMak
 ing informed decisions about model adequacy has been an outstanding\nissue
  for regression models with discrete outcomes. Standard residuals\nsuch as
  Pearson and deviance residuals for such outcomes often show a\nlarge disc
 repancy from the hypothesized pattern even under the true\nmodel and are n
 ot informative especially when data are highly\ndiscrete. To fill this gap
 \, we propose a surrogate empirical residual\ndistribution function for ge
 neral discrete (e.g. ordinal and count)\noutcomes that serves as an altern
 ative to the empirical Cox-Snell\nresidual distribution function. When at 
 least one continuous covariate\nis available\, we show asymptotically that
  the proposed function\nconverges uniformly to the identity function under
  the correctly\nspecified model\, even with highly discrete (e.g. binary) 
 outcomes.\nThrough simulation studies\, we demonstrate empirically that th
 e\nproposed surrogate empirical residual distribution function is highly\n
 effective for various diagnostic tasks\, since it is close to the\nhypothe
 sized pattern under the true model and significantly departs\nfrom this pa
 ttern under model misspecification.
DTSTAMP:20260410T113708Z
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