In the text, the function unify was used several times, but it has not been deened yet. As mentioned, we need just ordinary rst-order uniication, so we give its deenition here for the sake of completeness. The algorithm uses a simple divide-and-conquer strategy, i.e. it uniies two terms by unifying their subterms and composing the resulting uniiers. unify x (Tvar n) = unify (Tvar n) x unify (Tc...

(ABSTRACT) Instantaneous Frequency (IF) estimation based on time-varying autoregressive (TVAR) modeling has been shown to perform well in practical scenarios when the IF variation is rapid and/or non-linear and only short data records are available for modeling. A challenging aspect of implementing IF estimation based on TVAR modeling is the efficient computation of the time-varying coefficient...

In this paper, we address risk aggregation and capital allocation problems in the presence of dependence between risks. The structure is defined by a mixed Bernstein copula which represents generalization well-known Archimedean copulas. Using new copula, probability density function cumulative distribution aggregate are obtained. Then, closed-form expressions for basic measures, such as tail va...

We explore generalizations of the pari-mutuel model (PMM), a formalization of an intuitive way of assessing an upper probability from a precise one. We discuss a naive extension of the PMM considered in insurance, compare the PMM with a related model, the Total Variation Model, and generalize the natural extension of the PMM introduced by P. Walley and other pertained formulae. The results are ...

This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modeling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian diffusion processes. The main aim of the paper is to perform on-line estimation of the clean speech and model parameters and to determine the adequacy of the chosen statistica...

This paper describes a method of extracting time-varying features that is effective for speech signals with high fundamental frequencies. The proposed method adopts a speech production model that consists of a Time-Varying AutoRegressive (TVAR) process for an articulatory filter and a Hidden Markov Model (HMM) for an excitation source. The model represents waveform amplitude variations by timev...

We explore generalizations of the pari-mutuel model (PMM), a formalization of an intuitive way of assessing an upper probability from a precise one. We discuss a naive extension of the PMM considered in insurance and generalize the natural extension of the PMM introduced by P. Walley and other related formulae. The results are subsequently given a risk measurement interpretation: in particular ...

In this work, we study the problem of aggregating a finite number of predictors for non stationary sub-linear processes. We provide oracle inequalities relying essentially on three ingredients: 1) a uniform bound of the `1 norm of the time-varying sub-linear coefficients, 2) a Lipschitz assumption on the predictors and 3) moment conditions on the noise appearing in the linear representation. Tw...