Non-parametric graphical models, embedded in reproducing kernel Hilbert spaces, provide a framework to model multi-modal and arbitrary multi-variate distributions, which are essential when modeling complex protein structures. Non-parametric belief propagation requires the structure of the graphical model to be known a priori. Currently there are nonparametric structure learning algorithms avail...

in this paper, chaotic motion analysis and its control for a non-planar non-linear cantilever beam with axial and lateral parametric excitation in the free end is accomplished. this is a new method for control of chaotic motion of a non-linear planar beam. in this survey, internal resonance of the beam is 2:1 and the initial resonance is in the outside of plane and it is assumed a sub-resonance...

In this paper, we conduct simultaneous inference of the non-parametric multivariate function of a partially linear model when the covariate terms in both parametric and non-parametric parts are subject to the Berkson measurement errors. Based on semiparametric estimates of the model, we construct a simultaneous confidence region of the multivariate function for simultaneous inference. The devel...

in recent years, the existing competitions between investment companies have been increased largely by entering private investors in capital market. large and powerful companies try to achieve the goals predicted to increase the competition capacity. to analyze the efficiency of investment companies, parametric and non-parametric methods are used. in this research, based on the dissociation pow...

In this paper, we study a partially linear varying-coefficient errors-invariables (EV) model under additional restricted condition. Both of the parametric and nonparametric components are measured with additive errors. The restricted estimators of parametric and nonparametric components are established based on modified profile least-squares method and local correction method, and their asympto...

In this paper, we propose a new method for checking randomness of non-Gaussian stable data based on a characterization result. This method is more sensitive with respect to non-random data compared to the well-known non-parametric randomness tests.

In this paper we develop an approach that synthesizes the best features of the two main methods in the estimation of production efficiency. Specically, our approach first allows for statistical noise, similar to Stochastic frontier analysis, and second, it allows modeling multiple-inputs-multiple-outputs technologies without imposing parametric assumptions on production relationship, similar to...

This paper presents a non-parametric MAP framework for tracking non-rigid video objects. We formulate the region tracking problem as a maximum a posteriori probability (MAP) problem and define the probabilistic models in terms of the distances between the intensity distribution of the object and that of its spatialand temporal-neighborhood. Furthermore, in order to better model the complex inte...

In this paper we explore the usefulness of induced-order statistics in the characterization of integrated series and of cointegration relationships. We propose a non-parametric test statistic for testing the null hypothesis of two independent random walks against wide cointegrating alternatives including monotonic nonlinearities and certain types of level shifts in the cointegration relationshi...

Nowadays, one of the most important topics in risk management of banks, financial, and credit institutions is credit risk management. In this research, the researchers used survival analytic methods for credit risk modeling in terms of the conditional distribution function of default time. As a practical task, the authors considered the reward credit portfolio of Tose'e Ta'avon Bank of Guilan P...