We present a semi-parametric latent variable model based technique for density modelling, dimensionality reduction and visualization. Unlike previous methods, we estimate the latent distribution non-parametrically which enables us to model data generated by an underlying low dimensional, multimodal distribution. In addition, we allow the components of latent variable models to be drawn from the...

This paper reports on work which fuses three different appearance models to enable robust tracking of multiple objects on the basis of colour. Short-term variation in object colour is modelled non-parametrically using adaptive binning histograms. Appearance changes at intermediate time scales are represented by semi-parametric (Gaussian mixture) models while a parametric subspace method (Robust...

Bioequivalence assessment is an issue of great interest. Development of statistical methods for assessing bioequivalence is an important area of research for statisticians. Bioequivalence is usually determined based on the normal distribution. We relax this assumption and develop a semi-parametric mixed model for bioequivalence data. The proposed method is quite flexible and practically meaning...

The piecewise-constant exponential model is a semi-parametric continuous time duration model.1 The model is semi-parametric in the sense that the shape of the hazard rate with respect to time is not specified a priori but is determined by the data. The basic idea underlying the PCE model is that the duration time can be divided up into discrete units in each of which the hazard rate is assumed ...

AbstractWe observe a large number of functions differing from each other only by a translation parameter. While the main pattern is unknown, we propose to estimate the shift parameters using M -estimators. Fourier transform enables to transform this statistical problem into a semi-parametric framework. We study the convergence of the estimator and provide its asymptotic behavior. Moreover, we u...

At the border between control and verification, parametric verification can be used to synthesize constraints on the parameters to ensure that a system verifies given specifications. In this paper we propose a new framework for the parametric verification of time Petri nets with stopwatches. We first introduce a parametric extension of time Petri nets with inhibitor arcs (ITPNs) with temporal p...

Ordinal regression analysis is a convenient tool for analyzing ordinal response variables in the presence of covariates. In this paper we extend this methodology to the case of continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales measure subjects' perception of an ...

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly ...

Let P ,X and Y be Banach spaces. Suppose that f : P ×X → Y is continuously Fréchet differentiable function depend on the point (p, x) and F : X ⇒ 2 is a set-valued mapping with closed graph. Consider the following parametric generalized equation of the form: 0 ∈ f(p, x) + F (x). (1) In the present paper, we study an extended Newton-type method for solving parametric generalized equation (1). In...