In this paper, we propose a method for designing multimedia protocols using both parametric model checking and functional testing. Especially, we focus on designing media synchronization protocols. We specify a given media synchronization protocol as concurrent periodic timed automata with temporal properties where QoS parameters of the underlying network and timing parameters of the protocol a...

In the world of multivariate extremes, estimation of the dependence structure still presents a challenge and an interesting problem. A procedure for the bivariate case is presented that opens the road to a similar way of handling the problem in a truly multivariate setting. We consider a semi-parametric model in which the stable tail dependence function is parametrically modeled. Given a random...

Motivated from the bandwidth selection problem in local likelihood density estimation and from the problem of assessing a final model chosen by a certain model selection procedure, we consider estimation of the Kullback–Leibler divergence. It is known that the best bandwidth choice for the local likelihood density estimator depends on the distance between the true density and the ‘vehicle’ para...

I will not describe the underlying assumptions in details. These are the usual sorts of assumptions one makes for parametric models, in order to be able to establish sensible results. See Page 11 of Chapter 3 of Wellner’s notes for a detailed description of the conditions involved. For a multidimensional parametric model {p(x, θ) : θ ∈ Θ ⊂ Rk}, the information matrix I(θ) is given by: I(θ) = Eθ...

In this paper, a parametric model order reduction (pMOR) technique is proposed to find a simplified system representation of a large-scale and complex thermal system. The main principle behind this technique is that any change of the physical parameters in the high-fidelity model can be updated directly in the simplified model. For deriving the parametric reduced model, a Krylov subspace method...

The task of parametric model selection is cast in terms of a statistical mechanics on the space of probability distributions. Using the techniques of low-temperature expansions, I arrive at a systematic series for the Bayesian posterior probability of a model family that significantly extends known results in the literature. In particular, I arrive at a precise understanding of how Occam’s razo...

When predicting scalar responses in the situation where the explanatory variables are functions, it is sometimes the case that some functional variables are related to responses linearly while other variables have more complicated relationships with the responses. In this paper, we propose a new semi-parametric model to take advantage of both parametric and nonparametric functional modeling. As...

In this research, we proposed a new method to model complex range data using a parametric model. We first compute local surface curvatures by quadratic fitting of local surfaces, and then extract the curved surfaces and plane regions from the range data by examining the probability distribution of local surface curvatures. After extracting these regions, one shape may be divided into multiple r...

the aim of this study is to introduce a parametric mixture model to analysis the competing-risks data with two types of failure. in mixture context, i t h type of failure is i th component. the baseline failure time for the first and second types of failure are modeled as proportional hazard models according to weibull and gompertz distributions, respectively. the covariates affect on both the ...