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DTSTART:20190310T070000
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DTSTART;TZID=America/Toronto:20200121T100000
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URL:/statistics-and-actuarial-science/events/department
 -seminar-lu-yang-university-amsterdam
SUMMARY:Department seminar by Lu Yang\, University of Amsterdam
CLASS:PUBLIC
DESCRIPTION:Summary \n\nDIAGNOSTICS FOR REGRESSION MODELS WITH DISCRETE OUT
 COMES\n\nMaking informed decisions about model adequacy has been an outsta
 nding\nissue for regression models with discrete outcomes. Standard residu
 als\nsuch as Pearson and deviance residuals for such outcomes often show a
 \nlarge discrepancy from the hypothesized pattern even under the true\nmod
 el and are not informative especially when data are highly\ndiscrete. To f
 ill this gap\, we propose a surrogate empirical residual\ndistribution fun
 ction for general discrete (e.g. ordinal and count)\noutcomes that serves 
 as an alternative to the empirical Cox-Snell\nresidual distribution functi
 on. When at least one continuous covariate\nis available\, we show asympto
 tically that the proposed function\nconverges uniformly to the identity fu
 nction under the correctly\nspecified model\, even with highly discrete (e
 .g. binary) outcomes.\nThrough simulation studies\, we demonstrate empiric
 ally that the\nproposed surrogate empirical residual distribution function
  is highly\neffective for various diagnostic tasks\, since it is close to 
 the\nhypothesized pattern under the true model and significantly departs\n
 from this pattern under model misspecification.\n 
DTSTAMP:20250520T020928Z
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