Asymptotic results for the empirical process of stationary sequences

By István Berkes, Siegfried Hormann, Johannes Schauer. 2009

77 citations recorded by the source corpus.

Subfields: Finance, Mathematical Physics

View Paper: https://www.semanticscholar.org/paper/83501a62b50e321d4772f64d04bb13644926b2b7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

In Mathematical Physics

Citations in the corpus

Weakly dependent functional data (2010, background): “Functional data often arise from measurements on fine time grids and are obtained by separating an almost continuous time record into natural consecutive intervals, for example, days.”
Split invariance principles for stationary processes. (2011, background): “Such a definition requires no moment assumptions and turns out to provide a useful dependence measure for studying empirical processes (see [3]).”
Mixing properties of ARCH and time-varying ARCH processes (2011, methodology): “[3] have considered a different type of dependence, namely a version of the m-dependence moment measure, for ARCH-type processes.”
Uniform change point tests in high dimension (2015, background): “In related cases, the whole past is replaced with copies; see [8, 44] and [3] for a multivariate version.”
Detecting relevant changes in time series models (2014, background): “These results have been extended to more complex regression models (see Andrews (1993) and Bai and Perron (1998)) and to change point inference on the secondorder characteristics of a time series (see Berkes et al. (2009), Wied et al. (2012) and Preuss et al. (2014)).”
Extensions of some classical methods in change point analysis (2014, methodology): “Results establishing (3.3) under various dependence conditions can be found in Berkes et al. (2008, 2009a), Berkes and Horváth (2001, 2003), Berkes and Philipp (1977), Billingsley (1968), Louhichi (2000), Shao and Yu (1996) and Yu (1993).”