What Foundations for Statistical Modeling and Inference?

statistical inference via empirical bayes approach for stationary and dynamic contingency tables

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Discussion: Foundations of Statistical Inference, Revisited

This is an invited contribution to the discussion on Professor Deborah Mayo’s paper, “On the Birnbaum argument for the strong likelihood principle,” to appear in Statistical Science. Mayo clearly demonstrates that statistical methods violating the likelihood principle need not violate either the sufficiency or conditionality principle, thus refuting Birnbaum’s claim. With the constraints of Bir...

Statistical Modeling and Inference for Genomics

Multi-rate Modeling, Model Inference, and Estimation for Statistical Classifiers

Multi-rate Modeling, Model Inference, and Estimation for Statistical Classifiers

Exact Statistical Inference for Some Parametric Nonhomogeneous Poisson Processes

Nonhomogeneous Poisson processes (NHPPs) are often used to model recurrent events, and there is thus a need to check model fit for such models. We study the problem of obtaining exact goodness-of-fit tests for certain parametric NHPPs, using a method based on Monte Carlo simulation conditional on sufficient statistics. A closely related way of obtaining exact confidence intervals in parametri...