We consider a general scheme to construct Bayesian incentive-compatible mechanisms using a suitable ‘variable mechanism parametrization.’ The key idea is to perturb a given direct mechanism, which might not be truth revealing, introducing sufficient variability as a function of agents’ announcements to generate incentives for truthful revelation. We discuss a variable-price auction in a general...

Consider a non-standard parametric estimation problem, such as the estimation of the AR(1) coefficient close to the unit root. We develop a numerical algorithm that determines an estimator that is nearly (mean or median) unbiased, and among all such estimators, comes close to minimizing a weighted average risk criterion. We demonstrate the usefulness of our generic approach by also applying it ...

We consider the estimation of Markov transition matrices by Bayes’ methods. We obtain large and moderate deviation principles for the sequence of Bayesian posterior distributions. MSC 2000 subject classification: 60F10, 62M05

A semi parametric profil~ likelihood method is proposed for estimation of sample selection models. The method is a two step scoring semi parametric estimation procedure based on index formulation and kernel density estimation. Under some regularity conditions, the estimator is asymptotically normal. This method can be applied to estimation of general sample selection models with multiple regime...

We provide easily-verifiable sufficient conditions on the primitives of a Bayesian game to guarantee the existence of a behavioral-strategy Bayes–Nash equilibrium. We allow players’ payoff functions to be discontinuous in actions, and illustrate the usefulness of our results via an example of an all-pay auction with general tie-breaking rules which cannot be handled by extant results. © 2015 El...

In this paper we develop a novel classification approach for high and very high resolution polarimetric synthetic aperture radar (SAR) amplitude images. This approach combines the Markov random field model to Bayesian image classification and a finite mixture technique for probability density function estimation. The finite mixture modeling is done via a recently proposed dictionary-based stoch...

The issue of whether government capital is productive has received a great deal of attention recently, yet empirical analyses of public capital productivity have generally been limited to the official capital stock estimates available in a small sample of countries. Alternatively, many researchers have investigated the output effects of public investment—recognizing that investment may be a poo...

This paper provides a uni…ed framework to study how capital adjustment costs and uncertainty a¤ect investment dynamics and capital accumulation. It considers an ongoing …rm with stochastic downward sloping demand curve and facing three possible forms of adjustment costs: complete or partial irreversibility, …xed costs of undertaking any investment and the traditional quadratic adjustment costs....

in this paper, first a great number of inverse problems which arise in instrumentation, in computer imaging systems and in computer vision are presented. then a common general forward modeling for them is given and the corresponding inversion problem is presented. then, after showing the inadequacy of the classical analytical and least square methods for these ill posed inverse problems, a baye...

We consider nonparametric Bayesian estimation inference using a rescaled smooth Gaussian field as a prior for a multidimensional function. The rescaling is achieved using a Gamma variable and the procedure can be viewed as choosing an inverse Gamma bandwidth. The procedure is studied from a frequentist perspective in three statistical settings involving replicated observations (density estimati...