Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …

This article reviews the Akaike information criterion (AIC) and the Bayesian information
criterion (BIC) in model selection and the appraisal of psychological theory. The focus is on …

In a 1935 paper and in his book Theory of Probability, Jeffreys developed a methodology for
quantifying the evidence in favor of a scientific theory. The centerpiece was a number, now …

It is argued that P-values and the tests based upon them give unsatisfactory results,
especially in large samples. It is shown that, in regression, when there are many candidate …

Standard statistical practice ignores model uncertainty. Data analysts typically select a
model from some class of models and then proceed as if the selected model had generated …

This paper describes and compares various hierarchical mixture prior formulations of
variable selection uncertainty in normal linear regression models. These include the …

Survival analysis arises in many fields of study including medicine, biology, engineering,
public health, epidemiology, and economics. This book provides a comprehensive treatment …

In contrast to a posterior analysis given a particular sampling model, posterior model
probabilities in the context of model uncertainty are typically rather sensitive to the …

The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …

This book describes how to conduct a thorough, honest and complete environmental risk
assessment. Coverage includes the philosophy of uncertainty and the frailties of human …