Distortion representations of multivariate distributions

Statistical Methods & Applications - The univariate distorted distributions were introduced in risk theory to represent changes (distortions) in the expected distributions of some risks. Later,...     

Posterior ranges of functions of parameters under priors with specified quantiles

Suppose some quantiles of the prior distribution of a nonnegative parameter θ are specified. Instead of eliciting just one prior density function, consider the class Γ of all the density functions compatible with the quantile specification. Given a likelihood function, find the posterior upper and lower bounds for the expected value of any real-valued function h(θ), as the density varies in Γ. Such a scheme agrees with a robust Bayesian viewpoint. Under mild regularity conditions about h(θ) and the likelihood, a procedure for finding bounds is derived and applied to an example, after transforming the given functional optimisation problems into finite-dimensional ones.     

On the Use of the Concentration Function in Medical Fraud Assessment

We propose a simple, but effective, tool to detect possible anomalies in the services prescribed by a health care provider (HP) compared to his/her colleagues in the same field and environment. Our method is based on the concentration function that is an extension of the Lorenz curve widely used in describing uneven distribution of wealth in a population. The proposed tool provides a graphical illustration of a possible anomalous behavior of the HPs and it can be used as a prescreening device for further investigations of potential medical fraud.     

A Bootstrap Likelihood Approach to Bayesian Computation

There is an increasing amount of literature focused on Bayesian computational methods to address problems with intractable likelihood. One approach is a set of algorithms known as Approximate Bayesian Computational (ABC) methods. One of the problems with these algorithms is that their performance depends on the appropriate choice of summary statistics, distance measure and tolerance level. To circumvent this problem, an alternative method based on the empirical likelihood has been introduced. This method can be easily implemented when a set of constraints, related to the moments of the distribution, is specified. However, the choice of the constraints is sometimes challenging. To overcome this difficulty, we propose an alternative method based on a bootstrap likelihood approach. The method is easy to implement and in some cases is actually faster than the other approaches considered. We illustrate the performance of our algorithm with examples from population genetics, time series and stochastic differential equations. We also test the method on a real dataset.     

Soccer Scores,Short-Term Mood and Fertility

European Journal of Population - Previous research has shown that seemingly irrelevant events such as unexpected outcomes in sporting events can affect mood and have relevant consequences for...     

THE POLITICAL COLOR OF FISCAL RESPONSIBILITY

We propose a dynamic general equilibrium model that yields testable implications about the fiscal policy run by governments of different political color. Successive generations of voters choose taxation, expenditure, and government debt through repeated elections. Voters are heterogeneous by age and by the intensity of their preferences for public good provision. The political equilibrium switches stochastically between left‐ (pro‐public goods) and right‐leaning (pro‐private consumption) governments. A shift to the left (right) is associated with a fall (increase) in government debt, an increase (fall) in taxation, and an increase (fall) in government expenditures. However, left‐leaning governments engage in more debt accumulation during recessions. These predictions are shown to be consistent with the time‐series evidence for the United States in the postwar period, and also with the evidence for a panel of OECD countries. (JEL: D72, E62, H41, H62, H63)     

Concentration function and sensitivity to the prior

Summary In robust bayesian analysis, ranges of quantities of interest (e. g. posterior means) are usually considered when the prior probability measure varies in a class Γ. Such quantities describe the variation of just one aspect of the posterior measure. The concentration function describes changes in the posterior probability measure more globally, detecting differences in probability concentration and providing, simultaneously, bounds on the posterior probability of all measurable subsets. In this paper, we present a novel use of the concentration function, and two concentration indices, to study such posterior changes for a general class Γ, restricting then our attention to some ∈-contamination classes of priors.