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For a skew normal random sequence, convergence rates of the distribution of its partial maximum to the Gumbel extreme value distribution are derived. The asymptotic expansion of the distribution of the normalized maximum is given under an optimal choice of norming constants. We find that the optimal convergence rate of the normalized maximum to the Gumbel extreme value distribution is proportional to $1/log n$.
This paper considers the problem of estimating probabilities of the form $mathbb{P}(Y leq w)$, for a given value of $w$, in the situation that a sample of i.i.d. observations $X_1, ldots, X_n$ of $X$ is available, and where we explicitly know a funct
In this paper, we study the asymptotic behaviors of the extreme of mixed skew-t distribution. We considered limits on distribution and density of maximum of mixed skew-t distribution under linear and power normalization, and further derived their hig
The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This article reviews recent progress i
Rejoinder to Statistical Modeling of Spatial Extremes by A. C. Davison, S. A. Padoan and M. Ribatet [arXiv:1208.3378].
Often in the analysis of first-order methods, assuming the existence of a quadratic growth bound (a generalization of strong convexity) facilitates much stronger convergence analysis. Hence the analysis is done twice, once for the general case and on