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Creators/Authors contains: "Kuffner, Todd"

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  1. ; ; (, Annual Review of Statistics and Its Application)
    We discuss inference after data exploration, with a particular focus on inference after model or variable selection. We review three popular approaches to this problem: sample splitting, simultaneous inference, and conditional selective inference. We explain how each approach works and highlight its advantages and disadvantages. We also provide an illustration of these post-selection inference approaches. 
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  2. ; ; (, Annual review of statistics and its application)
    We discuss inference after data exploration, with a particular focus on inference after model or variable selection. We review three popular approaches to this problem: sample splitting, simultaneous inference, and conditional selective inference. For each approach, we explain how it works, and highlight its advantages and disadvantages. We also provide an illustration of these post-selection inference approaches. 
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    Accepted Manuscript Full Text Available
  3. ; ; (, Electronic Journal of Statistics)
    null (Ed.)
    Full Text Available 
  4. ; (, The Annals of Statistics)
    null (Ed.)
    Full Text Available 
  5. ; (, Statistical Science)
    Full Text Available 
  6. ; ; (, Variance)
    Predictive modeling is arguably one of the most important tasks actuaries face in their day-to-day work. In practice, actuaries may have a number of reasonable models to consider, all of which will provide different predictions. The most common strategy is first to use some kind of model selection tool to select a ``best model" and then to use that model to make predictions. However, there is reason to be concerned about the use of the classical distribution theory to develop predictions because this theory ignores the selection effect. Since accuracy of predictions is crucial to the insurer’s pricing and solvency, care is needed to develop valid prediction methods. This paper investigates the effects of model selection on the validity of classical prediction tools and makes some recommendations for practitioners. 
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    Accepted Manuscript Full Text Available
  7. ; (, Statistical Data Science)
    We discuss the challenges of principled statistical inference in modern data science. Conditionality principles are argued as key to achieving valid statistical inference, in particular when this is performed after selecting a model from sample data itself. 
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