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We propose a novel general algorithm LHAC that efficiently uses second-order information to train a class of large-scale l1-regularized problems. Our method executes cheap iterations while achieving f...
The goal of a learner, in standard online learning, is to have the cumulative loss not much larger compared with the best-performing function from some fixed class. Numerous algorithms were shown to h...
In the asymptotic theory of quantum hypothesis testing, the error probability of the first kind jumps sharply from zero to one when the error exponent of the second kind passes by the point of the rel...
We study the nonparametric estimators of the infinitesimal coefficients of the second-order jump-diffusion models. Under the mild conditions, we obtain the weak consistency and the asymptotic normalit...
We give the distribution function of Mn, the maximum of a sequence of n observations from an autoregressive process of order 2. Solutions are first given in terms of repeated integrals and then for ...
We consider the asymptotic behaviour of the second-order correlation function of the characteristic polynomial of a real symmetric random matrix. Our main result is that the existing result for a rand...
Classical extreme value index estimators are known to be quite sensitive to the number k of top order statistics used in the estimation. The recently developed second order reduced-bias estimators s...
Second order conditions ruling the rate of convergence in any first order condition involving regular variation and assuring a unified extreme value limiting distribution function for the sequence o...
In this paper the relation between goodness-of-fit testing and the optimal selection of the sample fraction for tail estimation, for instance using Hill’s estimator, is examined. We consider this pr...
We consider the problem of estimating the slope parameter in functional linear regression, where scalar responses Y1; : : : ; Yn are modeled in dependence of second order stationary random functions...
Modelling excesses over a high threshold using the Pareto or generalized Pareto distribution (PD/GPD) is the most popular approach in extreme value statistics. This method typically requires high th...

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