Michael Evans

Checking for prior-data conflict

Inference proceeds from ingredients chosen by the analyst and data. To validate any inferences drawn it is essential that the inputs chosen be deemed appropriate for the data. In the Bayesian context these inputs consist of both the sampling model and the prior. There are thus two possibilities for failure: the data may not have arisen from the sampling model, or the prior may place most of its mass on parameter values that are not feasible in light of the data (referred to here as prior-data conflict). Failure of the sampling model can only be fixed by modifying the model, while prior-data conflict can be overcome if sufficient data is available. We examine how to assess whether or not a prior-data conflict exists, and how to assess when its effects can be ignored for inferences. The concept of prior-data conflict is seen to lead to a partial characterization of what is meant by a noninformative prior or a noninformative sequence of priors.

Bayesian ikference procedures derived via the concept of relative surprise

We consider the problem of deriving Bayesian inference procedures via the concept of relative surprise. The mathematical concept of surprise has been developed by I.J. Good in a long sequence of papers. We make a modification to this development that permits the avoidance of a serious defect; namely, the change of variable problem. We apply relative surprise to the development of estimation, hypothesis testing and model checking procedures. Important advantages of the relative surprise approach to inference include the lack of dependence on a particular loss function and complete freedom to the statistician in the choice of prior for hypothesis testing problems. Links are established with common Bayesian inference procedures such as highest posterior density regions, modal estimates and Bayes factors. From a practical perspective new inference procedures arise that possess good properties.

Random Variable Generation Using Concavity Properties of Transformed Densities

Abstract Algorithms are developed for constructing random variable generators for families of densities. The generators depend on the concavity structure of a transformation of the density. The resulting algorithms are rejection algorithms and the methods of this article are concerned with constructing good rejection algorithms for general densities.

Weak Informativity and the Information in One Prior Relative to Another

A question of some interest is how to characterize the amount of information that a prior puts into a statistical analysis. Rather than a general characterization, we provide an approach to characterizing the amount of information a prior puts into an analysis, when compared to another base prior. The base prior is considered to be the prior that best reflects the current available information. Our purpose then is to characterize priors that can be used as conservative inputs to an analysis relative to the base prior. The characterization that we provide is in terms of a priori measures of prior-data conflict.

Bayesian Analysis of Stochastically Ordered Distributions of Categorical Variables

Abstract This article considers a finite set of discrete distributions all having the same finite support. The problem of interest is to assess the strength of evidence produced by sampled data for a hypothesis of a specified stochastic ordering among the underlying distributions and to estimate these distributions subject to the ordering. We present a Bayesian approach that is an alternative to using the posterior probability of the hypothesis and the Bayes factor in favor of the hypothesis. We develop computational methods for the implementation of Bayesian analyses. We analyze examples to illustrate inferential and computational developments. The methodology used for testing a hypothesis is seen to apply to a wide class of problems in Bayesian inference and has some distinct advantages.

Hypothesis Assessment and Inequalities for Bayes Factors and Relative Belief Ratios

We discuss the denition of a Bayes factor and develop some inequalities relevant to Bayesian inferences. An approach to hypothesis assessment based on the computation of a Bayes factor, a measure of the strength of the evidence given by the Bayes factor via a posterior probability, and the point where the Bayes factor is maximized is recommended. It is also recommended that the a priori properties of a Bayes factor be considered to assess possible bias inherent in the Bayes factor. This methodology can be seen to deal with many of the issues and controversies associated with hypothesis assessment. We present an application to a two-way analysis.

Measuring statistical evidence using relative belief

A fundamental concern of a theory of statistical inference is how one should measure statistical evidence. Certainly the words “statistical evidence,” or perhaps just “evidence,” are much used in statistical contexts. It is fair to say, however, that the precise characterization of this concept is somewhat elusive. Our goal here is to provide a definition of how to measure statistical evidence for any particular statistical problem. Since evidence is what causes beliefs to change, it is proposed to measure evidence by the amount beliefs change from a priori to a posteriori. As such, our definition involves prior beliefs and this raises issues of subjectivity versus objectivity in statistical analyses. This is dealt with through a principle requiring the falsifiability of any ingredients to a statistical analysis. These concerns lead to checking for prior-data conflict and measuring the a priori bias in a prior.

Utilization and Expenditure on Blood Glucose Test Strips in Canada

ABSTRACT OBJECTIVE: The objective of this study was to explore utilization patterns and expenditures on blood glucose test strips (BGTSs) in Canada according to concurrently prescribed diabetes treatments. METHODS: We conducted a retrospective utilization analysis using administrative claims data from available public and private drug plans in Canada. Utilization and expenditures on BGTSs were calculated, as was the average daily frequency of BGTS use by concurrent diabetes pharmacotherapy. RESULTS: Expenditures on BGTSs in Canada in 8 public drug plans in 2006 were

Distribution theory and inference for polynomial-normal densities

247 million, while those in private drug plans were in excess of

Optimal properties of some Bayesian inferences

81 million. Almost half of total expenditures were for patients not using insulin, despite a lower average number of BGTSs claimed per day compared with those using insulin. INTERPRETATION: In private and public drug plans in Canada, current utilization and expenditure on BGTSs is considerable. Given the size of the investment and lack of convincing evidence that routine self-monitoring of blood glucose is beneficial for patients not using insulin, there may be more cost-effective strategies for improving the health of this population.