Authors’ reply to the Discussion of ‘Automatic change-point detection in time series via deep learning’ at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’-Reference-Cited by-同舟云学术

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Authors’ reply to the Discussion of ‘Automatic change-point detection in time series via deep learning’ at the Discussion Meeting on ‘Probabilistic and statistical aspects of machine learning’

Published:2024-01-29 Issue:2 Volume:86 Page:332-334
ISSN:1369-7412
Container-title:Journal of the Royal Statistical Society Series B: Statistical Methodology
language:en
Short-container-title:

Author:

Li Jie¹^ORCID,Fearnhead Paul²^ORCID,Fryzlewicz Piotr¹,Wang Tengyao¹

Affiliation:

1. Department of Statistics, London School of Economics and Political Science , Columbia House, Houghton Street, London, WC2A 2AE , UK

2. Department of Mathematics and Statistics, Lancaster University , Lancaster, LA1 4YF , UK

Publisher

Oxford University Press (OUP)

Link

https://academic.oup.com/jrsssb/advance-article-pdf/doi/10.1093/jrsssb/qkae008/56446779/qkae008.pdf

Reference5 articles.

1. Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks;Bartlett;Journal of Machine Learning Research,2019

2. A MOSUM procedure for the estimation of multiple random change points;Eichinger;Bernoulli,2018

3. Kolmogorov–Smirnov type testing for structural breaks: A new adjusted-range based self-normalization approach;Hong;Journal of Econometrics,2024

4. Foundation Models for Natural Language Processing

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