This paper studies the nonparametric identification and estimation of voters' preferences when voters are ideological. We establish that voter preference distributions and other parameters of interest can be identified from aggregate electoral data. We also show that these objects can be...

An elliptical copula model is a distribution function whose copula is that of an elliptical distribution. The tail dependence function in such a bivariate model has a parametric representation with two parameters: a tail parameter and a correlation parameter. The correlation parameter can be...

The Poisson distribution, in general remains sensitive to small departure of frequencies especially at the right tail of the distribution. In many situations it may happen that the Generalized Poisson Distribution (GPD) or a compound distribution provides a closer fit to a frequency distribution...

In this paper, we propose a likelihood ratio and Markov chain based method to evaluate density forecasting. This method can jointly evaluate the unconditional forecasted distribution and dependence of the outcomes. This method is an extension of the widely applied evaluation method for interval...

We solve a moment problem to compute the best upper and lower bounds on the expected value E[φ(X)], subject to constraints E[X^i] = μ_i for i = 1, 2,...,n. By setting φ(x)=I_(-\inf,t], the indicator function for the event X ≤ t, we calculate the bounds on Pr(X ≤ t) = E[I_(-\inf,t]]. The...

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and...

In the world of multivariate extremes, estimation of the dependence structure still presents a challenge and an interesting problem. A procedure for the bivariate case is presented that opens the road to a similar way of handling the problem in a truly multivariate setting. We consider a...

We define the local empirical process, based on n i.i.d. random vectors in dimension d, in the neighborhood of the boundary of a fixed set. Under natural conditions on the shrinking neighborhood, we show that for these local empirical processes, indexed by classes of sets that vary with n and...