This paper presents a new approach to shape and motion estimation based on geometric primitives and relations in a model-based framework. A description of a scene in terms of structured geometric elements sharing relationships allows to derive a parametric model with Euclidian constraints, and a camera model is also proposed to reduce the problem dimensionality. It leads to a sequential MAP est...

background : kernel smoothing method is a non-parametric or graphical method for statistical estimation. in the present study was used a kernel smoothing method for finding the death hazard rates of patients with acute myocardial infarction. methods : by employing non-parametric regression methods, the curve estimation, may have some complexity.  in this article, four indices of epanechnikov, b...

A method is given to estimate the geometry and motion of a moving body surface from image sequences. To this aim a parametric model of the surface is used, in order to reformulate the problem to one of parameter estimation. After linearization of the model standard linear estimation methods can be used to estimate the parameters. The main contribution of this paper is that a method is provided ...

We propose a novel approach to online estimation of probability density functions, which is based on kernel density estimation (KDE). The method maintains and updates a non-parametric model of the observed data, from which the KDE can be calculated. We propose an online bandwidth estimation approach and a compression/revitalization scheme which maintains the KDE’s complexity low. We compare the...

This paper models zero response data from willingness to pay surveys by employing parametric and semi-parametric estimation methods. The result of the specification test indicates the semi-parametric estimation outperforms the parametric estimation significantly.  2001 Published by Elsevier Science B.V.

This study extends the parametric estimation of the structural misclassification model (Arrieta, 2008) to analyze over-treatment of medical procedures to a semi-parametric estimation. The estimation is based on a doubleindex semi-parametric maximum likelihood with partial observability. The document shows that misspecification error due to an incorrect assumption about the error distribution ma...

This chapter deals with the description, the parametric estimation, the state estimation, and the parametric and state estimation conjointly of nonlinear systems. The focus is on the class of nonlinear systems, which are described by Wiener state-space discrete-time mathematical models. Thus, the authors develop a new recursive parametric estimation algorithm, which is based on least squares te...

outbreak of 2009 european sovereign debt (leverage) crisis has been one of the most crucial economic events of recent years. accordingly, researchers devoted a great deal of efforts to elucidate origins and consequences of this crisis, particularly focusing on its potential effect on international trade flows. yet in the literature, there have been rare studies on exploring the effects of sover...

Univariate Density Estimation via Numerical Derivatives Consider the problem of estimating the density function f(x) of a scalar, continuously-distributed i.i.d. sequence xi at a particular point x. If the density f is in a known parametric family (e.g., Gaussian), estimation of the density reduces to estimation of the finite-dimensional parameters that characterize that particular density in t...