A system of multivariate semiparametric nonlinear time series models is studied with possible dependence structures and nonstationarities in the parametric and nonparametric components. The parametric regressors may be endogenous while the nonparametric regressors are assumed to be strictly exogenous. The parametric regressors may be stationary or nonstationary and the nonparametric regressors ...

Reynolds’ theory of parametric polymorphism captures the invariance of polymorphically typed programs under change of data representation. Semantically, reflexive graph categories and fibrations are both known to give a categorical understanding of parametric polymorphism. This paper contributes further to this categorical perspective by showing the relevance of bifibrations. We develop a bifib...

We propose a new approach to the correspondence problem that makes use of non-parametric local transforms as the basis for correlation. Non-parametric local transforms rely on the relative ordering of local intensity values, and not on the intensity values themselves. Correlation using such transforms can tolerate a signiicant number of outliers. This can result in improved performance near obj...

In this paper, a Monte Carlo simulation is performed to investigate the finite sample properties of various estimators, based on discretely sampled observations, of the continuous-time Itô diffusion process. The simulation study aims to compare the performance of the nonparametric estimators proposed in Jiang and Knight (1996) with common parametric estimators based on those diffusion processes...

A parametric analysis is an analysis whose input and output are parametrized with a number of parameters which can be instantiated to abstract properties after analysis is completed. This paper proposes to use Cousot and Cousot’s Cardinal power domain to capture dependencies of analysis output on its input and obtain a parametric analysis by parametrizing a base analysis. We illustrate the meth...

Multi-parametric magnetic resonance imaging (mpMRI) offers a unique insight into tumor biology by combining functional MRI techniques that inform on cellularity (diffusion-weighted MRI), vascular properties (dynamic contrast-enhanced MRI), and metabolites (magnetic resonance spectroscopy) and has scope to provide valuable information for prognostication and response assessment. Challenges in th...

In this paper, we briefly present a classification scheme of information-based network complexity measures. We will see that existing as well as novel measures can be divided into four major categories: (i) partition-based measures, (ii) non partition-based measures, (iii) non-parametric local measures and (iv) parametric local measures. In particular, it turns out that (ii)-(iv) can be obtaine...

In this paper, we discuss how to approximate the conditional expectation of a random variable Y given a random variable X, i.e. E(Y|X). We propose and compare two different non parametric methodologies to approximate E(Y|X). The first approach (namely the OLP method) is based on a suitable approximation of the σ-algebra generated by X. A second procedure is based on the well known kernel non-pa...

This paper discusses the application of three different nonparametric methods for decomposing images into regions which exhibit special stochashc es, together with the statistics in connection With steps in the emplrical estimated distribution functions; 2) detection of stochastic informahon wi image by hypothesis testing; 3) rank order stahstics to ose the different types of stochastic mthin a...