Andere Kunden interessierten sich auch für
![Stopped Random Walks]( "Stopped Random Walks")
Stopped Random Walks37,99 €![Intersections of Random Walks]( "Intersections of Random Walks")
Intersections of Random Walks66,99 €![Correlation Theory of Stationary and Related Random Functions]( "Correlation Theory of Stationary and Related Random Functions")
Correlation Theory of Stationary and Related Random Functions125,99 €![A Lifetime of Excursions Through Random Walks and Lévy Processes]( "A Lifetime of Excursions Through Random Walks and Lévy Processes")
A Lifetime of Excursions Through Random Walks and Lévy Processes95,99 €![Non-Homogeneous Random Walks]( "Non-Homogeneous Random Walks")
Non-Homogeneous Random Walks154,99 €![Branching Random Walks]( "Branching Random Walks")
Branching Random Walks28,99 €![Analysis of Random Walks]( "Analysis of Random Walks")
Analysis of Random Walks132,99 €
The study of stationary processes is an important
topic both in the probability and statistics
literatures. This monograph continues the
investigation along the classical lines, but with a
modern viewpoint. For example, to study the behavior
of the partial sums, it features the interplay
between ergodic theory and probability from an
operator-theoretical point of view. As a
consequence, the results tend to be much more
refined, and nearly optimal on many occasions. With
the tools developed to understand the probabilistic
behavior of the processes, one statistical
application is considered in the context of global
warming. The goal is to nonparametrically estimate
the trend of a time series under monotonicity
assumptions. Some interesting features in time
series analysis are carefully explored.
topic both in the probability and statistics
literatures. This monograph continues the
investigation along the classical lines, but with a
modern viewpoint. For example, to study the behavior
of the partial sums, it features the interplay
between ergodic theory and probability from an
operator-theoretical point of view. As a
consequence, the results tend to be much more
refined, and nearly optimal on many occasions. With
the tools developed to understand the probabilistic
behavior of the processes, one statistical
application is considered in the context of global
warming. The goal is to nonparametrically estimate
the trend of a time series under monotonicity
assumptions. Some interesting features in time
series analysis are carefully explored.