The dilation phenomenon in robust Bayesian inference

Upper and lower probabilities may become uniformly less precise after conditioning. We call this dilation. We review some results about dilation, present some examples and explore the effect of Bayesian updating. Also, we show a connection between dilation and nonconglomerability. Finally, we consider the implications of this phenomenon.     

Multiple testing using the posterior probabilities of directional alternatives,with application to genomic studies

This paper aims to connect Bayesian analysis and frequentist theory in the context of multiple comparisons. The authors show that when testing the equality of two sample means, the posterior probability of the one‐sided alternative hypothesis, defined as a half‐space, shares with the frequentist P‐value the property of uniformity under the null hypothesis. Ultimately, the posterior probability may thus be used in the same spirit as a P‐value in the Benjamini‐Hochberg procedure, or in any of its extensions.     

Practicing safe statistics with the mid-p

The mid-p-value is the standard p-value for a test minus half the difference between it and the nearest lower possible value. Its smaller size lends it an obvious appeal to users — it provides a more significant-looking summary of the evidence against the null hypothesis. This paper examines the possibility that the user might overstate the significance of the evidence by using the smaller mid-p in place of the standard p-value. Routine use of the mid-p is shown to control a quantity related to the Type I error rate. This related quantity is appropriate to consider when the decision to accept or reject the null hypothesis is not always firm. The natural, subjective interpretation of a p-value as the probability that the null hypothesis is true is also examined. The usual asymptotic correspondence between these two probabilities for one-sided hypotheses is shown to be strengthened when the standard p-value is replaced by the mid-p.     

Belief functions and statistical inference

The Dempster Shafer theory of belief functions is a method of quantifying uncertainty that generalizes probability theory. We review the theory of belief functions in the context of statistical inference. We mainly focus on a particular belief function based on the likelihood function and its application to problems with partial prior information. We also consider connections to upper and lower probabilities and Bayesian robustness.     

Estimating the Probability of Rare Events: Addressing Zero Failure Data

Traditional statistical procedures for estimating the probability of an event result in an estimate of zero when no events are realized. Alternative inferential procedures have been proposed for the situation where zero events have been realized but often these are ad hoc, relying on selecting methods dependent on the data that have been realized. Such data‐dependent inference decisions violate fundamental statistical principles, resulting in estimation procedures whose benefits are difficult to assess. In this article, we propose estimating the probability of an event occurring through minimax inference on the probability that future samples of equal size realize no more events than that in the data on which the inference is based. Although motivated by inference on rare events, the method is not restricted to zero event data and closely approximates the maximum likelihood estimate (MLE) for nonzero data. The use of the minimax procedure provides a risk adverse inferential procedure where there are no events realized. A comparison is made with the MLE and regions of the underlying probability are identified where this approach is superior. Moreover, a comparison is made with three standard approaches to supporting inference where no event data are realized, which we argue are unduly pessimistic. We show that for situations of zero events the estimator can be simply approximated with , where n is the number of trials.     

National and subnational projections of elderly living arrangements: An application of the net transition probability macrosimulation model

Recently, McDonaldet al. (2006) outlined a new method of projecting living arrangements, households and dwellings at the national and subnational level, using quinquennial census data. The purpose of this paper is to apply this new simulation method to project the composition of elderly living arrangements at the national and subnational level in Australia over the period 2001 to 2016. This study presents projections of living arrangements for Temporal Statistical Districts within New South Wales and for Australia as a whole. Results show a strong increase in the number of the elderly living alone, particularly elderly males. The rate of growth in lone-person households is particularly strong in coastal and regional New South Wales, underlining the importance of capturing subnational differences in probabilities of births, deaths, migration and household movements when producing regional projections of living arrangements. This paper concludes by considering implications of the findings and potential uses of the net transition probability method.     

Representability of Ordinal Relations on a Set of Conditional Events

Any dynamic decision model should be based on conditional objects and must refer to (not necessarily structured) domains containing only the elements and the information of interest. We characterize binary relations, defined on an arbitrary set of conditional events, which are representable by a coherent generalized decomposable conditional measure and we study, in particular, the case of binary relations representable by a coherent conditional probability.     

Probabilities and beliefs

Choice-theoretic definitions of subjective probabilities originated with the work of Ramsey and de Finetti and attained their definitive form in the work of Savage. These probabilities are intended to provide a numerical representation of a decision maker's beliefs regarding the likely realization of alternative events. In this article, I argue that the choice-theoretic definitions of subjective probabilities involve a tacit convention—namely, state-independent utility functions—that is not implied by the axioms, and, as a consequence, choice-theoretic subjective probabilities, even when they exist, do not necessarily represent the decision makers' beliefs.     

The impossibility of experimental elicitation of subjective probabilities

We show that if decision makers may have stakes in certain events then the experimental elicitation of their subjective probabilities of these events is impossible.We thank Eyal Sulganik for helpful comments on an earlier draft of this paper.