Bayesian Procedures as a Numerical Tool for the Estimation of Dynamic Discrete Choice Models Dynamic discrete choice models usually require a general specification of unobserved heterogeneity. In this paper, we apply Bayesian procedures as a numerical tool for the estimation of a female labor supply model based on a sample size which is typical for common household panels. We provide two import...

We propose to model endogeneity bias using prior distributions of moment conditions. The estimator can be obtained both as a method-of-moments estimator and in a Ridge penalized regression framework. We show the estimator’s relation to a Bayesian estimator.

The feasibility of developing a neural network to perform nonlinear Bayesian estimation from sparse data is explored using an example from clinical pharmacology. The problem involves estimating parameters of a dynamic model describing the pharmacokinetics of the bronchodilator theophylline from limited plasma concentration measurements of the drug obtained in a patient. The estimation performan...

We establish the consistency, asymptotic normality, and efficiency for estimators derived by minimizing the median of a loss function in a Bayesian context. We contrast this procedure with the behavior of two Frequentist procedures, the least median of squares (LMS) and the least trimmed squares (LTS) estimators, in regression problems. The LMS estimator is the Frequentist version of our estima...

The demand made upon computational analysis of observed symbolic sequences has been increasing in the last decade. Here, the concept of entropy receives applications, and the generalizations according to Tsallis H (T) q and R enyi H (R) q provide whole-spectra of entropies characterized by an order q. An enduring practical problem lies in the estimation of these entropies from observed data. Th...

This letter proposes a low-computational Bayesian algorithm for noisy sparse recovery in the context of one bit compressed sensing with sensing matrix perturbation. The proposed algorithm which is called BHT-MLE comprises a sparse support detector and an amplitude estimator. The support detector utilizes Bayesian hypothesis test, while the amplitude estimator uses an ML estimator which is obtai...

Regional flood frequency analysis is a convenient way to reduce estimation uncertainty when few data are available at the gauging site. In this work, a model that allows a non null probability to a regional fixed shape parameter is presented. This methodology is integrated within a Bayesian framework and uses reversible jump techniques. The performance on stochastic data of this new estimator i...

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discr...

This work considers the problem of Bayesian estimation of a hidden Markov source corrupted by additive noise. We develop sequential and complete sequence Bayesian de-coders for noisy sources with memory and apply them to the log-area ratio (LAR) coeecients of speech corrupted by additive white Gaussian noise. To this end, we follow a model-based approach in which the source is approximated by a...

In this paper, we show how to use Bayesian approach in the multiplicative heteroscedasticity model proposed by Harvey (1976), where the Gibbs sampler and the Metropolis-Hastings (MH) algorithm are applied. Some candidate-generating densities are considered in our Metropolis-Hastings algorithm. We carry out Monte Carlo study to examine the properties of the estimates via Bayesian approach and it...