Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead ...
We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the dynamic programming (DP) solution algorithm with the Bayesian Markov chain Monte Carlo algorithm ...
Abstract: In this paper, an attempt is made to show a general solution to nonlinear and/or non-Gaussian state-space modeling in a Bayesian framework, which corresponds to an extension of Carlin et al. (J. Amer. Statist. ...
Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences ...
This paper introduces a new Bayesian fusion algorithm to combine more than one trust component (data trust and communication trust) to infer the overall trust between nodes. This research work proposes that one trust ...
Factorization-based techniques explain arrays of observations using a relatively small number of factors and provide an essential arsenal for multi-dimensional data analysis. Most factorization models are, however, developed ...
Hurn, M; Green, Peter; Al-Awadhi, F(Blackwell Publishing, 2008)
The Sloan digital sky survey is an extremely large astronomical survey that is conducted with the intention of mapping more than a quarter of the sky. Among the data that it is generating are spectroscopic and photometric ...
This article uses a Bayesian hierarchical model to quantify the adverse health effects associated with in-utero exposure to methylmercury. By allowing for study-to-study as well as outcome-to-outcome variability, the ...
Abstract: Recent advances in simulation methods have made possible the systematic application of Bayesian methods to support decision making with econometric models. This paper outlines the key elements of Bayesian ...
This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and nonrandom selection. Mortality rates in patient discharge records are widely used to infer hospital ...
Bonevich, J. E.; Armstrong, N. G.; Cline, J. P.; Kalceff, W.(US Government Printing Office, 2004)
A single-step, self-contained method for
determining the crystallite-size distribution
and shape from experimental x-ray line
profile data is presented. It is shown that
the crystallite-size distribution can be
determined ...
Time-varying proportions arise frequently in economics. Market shares show the relative importance of firms in a market. Labor economists divide populations into different labor market segments. Expenditure shares describe ...
Using Gaussian numerical integration formula, the problem of estimating the particle size distribution (PSD) in ferrofluids can be converted into an electromagnetic inverse problem. Then we present two Bayesian analytical ...
Security and trust are two interdependent concepts and are often used
interchangeably when defining a secure wireless sensor network (WSN) system.
However, security is different from trust in that, it assumes no node is ...
Simple financial ratios such as book-to-market are often used to identify value stocks. This paper examines the extent to which fundamental accounting information can be used to better identify truly undervalued value ...
An important component of quantitative risk assessment involves characterizing the dose-response relationship between an environmental exposure and adverse health outcome and then computing a benchmark dose, or the exposure ...
This paper establishes a general framework for Bayesian model-based clustering, in which subset labels are exchangeable, and items are also exchangeable, possibly up to covariate e?ects. It is rich enough to encompass a ...
Bayesian econometrics provides a tidy theory and practical methods of comparing and combining several alternative, completely specified models for a common data set. It is always possible that none of the specified models ...