Zuoxiang Peng

Asymptotics of maxima of strongly dependent Gaussian processes

Let

On Piterbarg theorem for maxima of stationary Gaussian sequences

\{X_{n}(t), t\in[0,\infty)\}, n\in\mathbb{N}

A class of unbiased location invariant Hill-type estimators for heavy tailed distributions

be a sequence of centered dependent stationary Gaussian processes. The limit distribution of

Limiting distributions of extreme order statistics under power normalization and random index

\sup_{t\in[0,T(n)]}|X_{n}(t)|

Tail asymptotic expansions for L-statistics

is established as

Tail Behavior and Limit Distribution of Maximum of Logarithmic General Error Distribution

r_{n}(t)

A location invariant moment-type estimator II

, the correlation function of

CONVERGENCE RATES FOR THE MOMENTS OF EXTREMES

X_{n}

SECOND ORDER REGULAR VARIATION AND ITS APPLICATIONS TO RATES OF CONVERGENCE IN EXTREME-VALUE DISTRIBUTION

satisfies the local and long range strong dependence conditions, which extends the results obtained by Seleznjev (1991).

Moment convergence of powered normal extremes

Limit distributions of maxima of dependent Gaussian sequence are different according to the convergence rate of their correlations. For three different conditions on convergence rate of the correlations, in this paper, we establish the Piterbarg theorem for maxima of stationary Gaussian sequences.