Extremes of Stationary Processes

  • David Aldous
Part of the Applied Mathematical Sciences book series (AMS, volume 77)

Abstract

Consider a stationary real-valued process (X n; n≥1) or (Xt; t≥0). Time may be discrete or continuous; the marginal distribution may be discrete or continuous; the process may or may not be Markov. Let
$$ {M_n} = \mathop {\max }\limits_{1 \leqslant j \leqslant n} {X_j};{M_t} = \mathop {\sup }\limits_{0 \leqslant s \leqslant t} {X_s} $$

Keywords

Stationary Distribution Poisson Process Point Process Gaussian Process Sojourn Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 1989

Authors and Affiliations

  • David Aldous
    • 1
  1. 1.Department of StatisticsUniversity of California-BerkeleyBerkeleyUSA

Personalised recommendations