{"id":15061,"date":"2025-07-30T09:03:03","date_gmt":"2025-07-30T14:03:03","guid":{"rendered":"https:\/\/uwm.edu\/math\/?post_type=tribe_events&p=15061"},"modified":"2025-07-30T09:03:03","modified_gmt":"2025-07-30T14:03:03","slug":"phd-dissertation-defense-mr-marco-vaassen","status":"publish","type":"tribe_events","link":"https:\/\/uwm.edu\/math\/event\/phd-dissertation-defense-mr-marco-vaassen\/","title":{"rendered":"PhD Dissertation Defense: Mr. Marco Vaassen"},"content":{"rendered":"

\"\"<\/h1>\n

A Bootstrap Goodness-of-Fit Test for Parametric Survival Models<\/h1>\n

Mr. Marco Vaassen
\nGraduate Student<\/em>
\nUniversity of Wisconsin-Milwaukee<\/p>\n

In many scientific disciplines, finding a suitable model compatible with real-world observations is the basis for statistical inference and prediction. In survival analysis, this task is further complicated by censoring. This dissertation introduces a new bootstrap approach to goodness-of-fit testing for parametric survival models, based on the Kaplan\u2013Meier process with estimated parameters. The test statistic compares the nonparametric Kaplan\u2013Meier estimator to a fitted parametric model, quantifying deviations from the null via functionals that yield Kolmogorov\u2013Smirnov or Cram\u00e9r\u2013von Mises-type tests. We establish the asymptotic correctness of our method by showing that the original and bootstrap test statistics have the same weak limit under the null. The result is a consistent, easily implementable framework for assessing model fit in censored settings.<\/p>\n

Advisor:
\nProf. Richard Stockbridge, Prof. Gerhard Dikta<\/p>\n

Committee Members:
\nProf. Richard Stockbridge, Prof. Gerhard Dikta, Prof. Chao Zhu, Prof. David Spade, and Prof. Vincent Larson<\/p>\n","protected":false},"excerpt":{"rendered":"

A Bootstrap Goodness-of-Fit Test for Parametric Survival Models Mr. Marco Vaassen Graduate Student University of Wisconsin-Milwaukee In many scientific disciplines, finding a suitable model compatible with real-world observations is the basis for statistical inference and prediction. 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