Weak convergence of stationary empirical processes
نویسندگان
چکیده
منابع مشابه
Weak Convergence of Stationary Empirical Processes
We offer an umbrella type result which extends the convergence of classical empirical process on the line to more general processes indexed by functions of bounded variation. This extension is not contingent on the type of dependence of the underlying sequence of random variables. As a consequence we establish the weak convergence for stationary empirical processes indexed by general classes of...
متن کامل@bullet , @bullet Weak Convergence of Multidimensional Empirical Processes for Stationary ¢-mixing Processes
متن کامل
Empirical Processes: General Weak Convergence Theory
The lack of measurability of the empirical process with respect to the sigma-field generated by the ‘natural’ l∞ metric, as illustrated in the previous notes, needs an extension of the standard weak convergence theory that can handle situations where the converging stochastic processes may no longer be measurable (though the limit will be a tight Borel measurable random element). Of course, an ...
متن کاملWeak Convergence of Blockwise Bootstrapped Empirical Processes for Stationary Random Fields with Statistical Applications
In this article, we consider a stationary α-mixing random field in IR. Under a large-sample scheme that is a mixture of the so-called “infill” and “increasing domain” asymptotics, we establish a functional central limit theorem for the empirical processes of this random field. Further, we apply a blockwise bootstrap to the samples. Under the condition that the side length of the block λl = O(λn...
متن کاملAn elementary proof of the weak convergence of empirical processes
This paper develops a simple technique for proving the weak convergence of a stochastic process Z̄n(g) := ∫ g dZn, indexed by functions g in some class G. The main novelty is a decoupling argument that allows to derive asymptotic equicontinuity of the process {Z̄n(g), g ∈ G} from that of the basic process {Zn(t), t ∈ R}, with Zn(t) = Z̄n(ft) and ft(x) = 1(−∞,t](x). The method leads to novel result...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Statistical Planning and Inference
سال: 2018
ISSN: 0378-3758
DOI: 10.1016/j.jspi.2017.09.006