A uniform central limit theorem for neural network-based autoregressive processes with applications to change-point analysis

DOI10.1080/02331888.2013.872646zbMath1320.60074OpenAlexW2102415923MaRDI QIDQ2934853

Claudia Kirch, Joseph Tadjuidje Kamgaing

Publication date: 22 December 2014

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: http://kluedo.ub.uni-kl.de/frontdoor/index/index/docId/2302

zbMATH Keywords

neural networkuniform central limit theoremchange-point analysisnonlinear autoregressive time series

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Parametric hypothesis testing (62F03) Central limit and other weak theorems (60F05) General nonlinear regression (62J02) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (5)

Non-parametric regression for spatially dependent data with wavelets ⋮ A change-point problem in relative error-based regression ⋮ Detection of Changes in INAR Models ⋮ On the use of estimating functions in monitoring time series for change points ⋮ Comments on: ``Extensions of some classical methods in change point analysis

Cites Work

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• On the detection of changes in autoregressive time series. I: Asymptotics.
• On the residuals of autoregressive processes and polynomial regression
• Change in autoregressive processes
• Central limit theorems for empirical measures
• Rates of convergence for U-statistic processes and their bootstrapped versions
• Testing for a change in the parameter values and order of an autoregressive model
• Inference in hidden Markov models.
• Testing for parameter stability in nonlinear autoregressive models
• CONTROL CHARTS WITH WARNING LINES
• LEAST SQUARES ESTIMATION OF A SHIFT IN LINEAR PROCESSES
• On geometric ergodicity of CHARME models

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