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In this paper, we examine in detail the principal branches of solutions that arise in vector discrete models with nonlinear intercomponent coupling and four wave mixing. The relevant four branches of ...
In this paper we study the problem of estimating the mixing coefficients between two random vari-ables. Three different mixing coefficients are studied,namely alpha-mixing, beta-mixing and phi-mixing ...
In this paper, we present a new algorithm for reverse-engineering gene interaction networks (GINs) from expression data, by viewing the expres-sion levels of various genes as coupled random variables....
In this paper, we present a new algorithm for reverse-engineering gene interaction networks (GINs) from expression data, by viewing the expres-sion levels of various genes as coupled random variables....
In many applications we are interested in making inference on latent time series from indirect measurements, which are often low-dimensional projections resulting from mixing or aggregation. Posit...
Many statistical problems involve mixture models and the need for computationally efficient methods to estimate the mixing distribution has increased dramatically in recent years. Newton [Sankhya Ser....
We show how to control the generalization error of time series models wherein past values of the outcome are used to predict future values.
Many statistical problems involve mixture models and the need for computationally efficient methods to estimate the mixing distribution has increased dramatically in recent years. Newton [Sankhya Ser....
There exist very few results on mixing for non-stationary processes. However, mixing is often required in statistical inference for non-stationary processes such as time-varying ARCH (tvARCH) models. ...
We consider the problem of estimating the mixing density f from n i.i.d. observations distributed according to amixture density with unknown mixing distribution. In contrast with finite mixtures model...
We prove that ergodicity and weak mixing coincide for the class of stationary symmetric semistable processes and give several characterizations of ergodic semistable processes.
For partial sums {S,) of a stationary ergodic sequence {X,} with zero mean we find conditions for m ny-'Pr {sup (S Jk) > E ] < m n= 1 k?n in terms of the strong mixing weficients {a,,) and moment...
In this Pa per we firstly study f, the inverse Laplace transform of F(s) = n,=(,s - 0 3 . The distribution f is then used to define a family of linear distribution processes. This family generalizes ...

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