Browsing by Subject "Weak convergence"
Now showing items 1-7 of 7
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Article
Average consensus in the presence of delays in directed graph topologies
(2014)Classical distributed algorithms for asymptotic average consensus typically assume timely and reliable exchange of information between neighboring components of a given multi-component system. These assumptions are not ...
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Article
Bootstrapping locally stationary processes
(2015)We propose a non-parametric method to bootstrap locally stationary processes which combines a time domain wild bootstrap approach with a non-parametric frequency domain approach. The method generates pseudotime series which ...
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Conference Object
Directed information on abstract spaces: Properties and extremum problems
(2012)This paper describes a framework in which directed information is defined on abstract spaces. The framework is employed to derive properties of directed information such as convexity, concavity, lower semicontinuity, by ...
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Article
Directed Information on Abstract Spaces: Properties and Variational Equalities
(2016)Directed information or its variants are utilized extensively in the characterization of the capacity of channels with memory and feedback, nonanticipative lossy data compression, and their generalizations to networks. In ...
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Article
Retrospective change detection for binary time series models
(2014)Detection of changes in health care performance, financial markets, and industrial processes have recently gained momentum due to the increased availability of complex data in real-time. As a consequence, there has been a ...
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Article
Validating stationarity assumptions in time series analysis by rolling local periodograms
(2010)We propose a simple and powerful procedure to validate the assumption of weak stationarity in time series analysis. Our focus is on processes with a slowly varying autocovariance structure. The procedure evaluates the ...
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Article
Weak convergence of dependent empirical measures with application to subsampling in function spaces
(1999)Consider the problem of inference for a parameter of a stationary time series, where the parameter takes values in a metric space (such as a function space). In this paper, we develop asymptotic theory based on subsampling ...