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We investigate the question of when sampling a stochastic process X = {X(t): t≥0} at the times of an independent point process ψ leads to the same empirical distribution as the time-average limiting d...
We study online learning under logarithmic loss with regular parametric models. Hedayati and Bartlett (2012b) showed that a Bayesian prediction strategy with Jeffreys prior and sequential normalized m...
Recently, an extension of independent component analysis (ICA) from one to multiple datasets, termed independent vector analysis (IVA), has been the subject of significant research interest. IVA has a...
Given a set of k networks, possibly with different sizes and no overlaps in nodes or edges, how can we quickly assess similarity between them, without solving the node-correspondence problem? Analogou...
A factor effect study was conducted on a set of observations at the contingency of a series of plant species and bacteria species regarding the antibacterial activity of essential oil extracts.
Let X1,X2, . . . ,Xn be independent and identically distributed random variables. We present an analytic method for computing the moments of Sn = Pn i=1 Xi. The method is illustrated with a simple...
In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent effort...
For statistical analysis of functional Magnetic Resonance Imaging (fMRI) data sets, we propose a data-driven approach based on Independent Component Analysis (ICA) implemented in a new version of the ...
In this paper, we consider the implications of the fact that parallel raw-power can be exploited by a generic Metropolis--Hastings algorithm if the proposed values are independent. In particular, we p...
Independent component analysis (ICA) aims at decomposing an observed random vector into statistically independent variables. Deflation-based implementations, such as the popular one-unit FastICA algo...
We discuss several tests for whether a given set of independent and identically distributed (i.i.d.) draws does not come from a specified probability density function. The most commonly used are Kol...
This note gives the convergence rate in the central limit theorem and the random central limit theorem of some functions of the average of independent random variables.
On the limit behaviour of random sums of independent random variables。
Weak convergence of random sums of infima of independent random variables。

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