Background Often, there is no access to sufficient sample size to estimate the prevalence using the method of direct estimator in all areas. The aim of this study was to compare small area’s Bayesian method and direct method in estimating the prevalence of steatosis in obese and overweight children. Materials and Methods: In this cross-sectional study, was conducted on 150 overweight and obese ...

The Semiparametric Least Squares (SLS) estimation for single index models is studied. Applying the isometric regression by Ayer et al (1955), the method minimizes the mean squared errors with respect to both finite and infinite dimensional parameters. A proof of consistency and an upper bound of convergence rates is offered. As an application example of the SLS estimation, asymptotic normality ...

Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: -mdrawsfor deriving draws from the standard uniform density using either Halton or pseudo-random sequences, and an egen function -mvnp()for calc...

This paper assesses the relative economic value of volatility and correlation timing in the context of asset allocation strategies. Using exchange rate data, we model the dynamic covariance matrix of daily returns by implementing a set of multivariate models based on Dynamic Conditional Correlation (DCC) model of Engle (2002). Our analysis takes a Bayesian approach in both estimation and asset ...

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...

A least squares estimation approach for the estimation of a GARCH (1,1) model is developed. The asymptotic properties of the estimator are studied given mild regularity conditions, which require only that the error term has a conditional moment of some order. We establish the consistency, asymptotic normality and the law of iterated logarithm for our estimate. The finite sample properties are a...

Testing whether two parameters have the same sign is a nonstandard problem due to the non-convex shape of the parameter subspace satisfying the composite null hypothesis, which is a nonlinear inequality constraint. We describe a simple example where the ordering of likelihood ratio (LR), Wald, and Bayesian sign equality tests reverses the “usual” ordering: the Wald rejection region is a subset ...

This paper reviews the analysis of the threshold autoregressive, smooth threshold autoregressive, and Markov switching autoregressive models from the Bayesian perspective. For each model we start by describing a baseline model and discussing possible extensions and applications. Then we review the choice of prior, inference, tests against the linear hypothesis, and conclude with models selectio...

This paper examines how robust economic, political, and demographic variables are related to water and air pollution. Employing Bayesian Averaging of Classical Estimates (BACE) for a cross section of up to 74 countries, 33 variables and 3 proxies for air and water pollution over a period from 1980 to 1995 we confirm the Environmental Kuznets Curve hypothesis, highlight the relevance of efficien...

The Gaussian process is a useful prior on functions for Bayesian kernel regression and classification. Density estimation with a Gaussian process prior is difficult, however, as densities must be nonnegative and integrate to unity. The statistics community has explored the use of a logistic Gaussian process for density estimation, relying on approximations of the normalization constant (e.g. [1...