Zobrazeno 1 - 10
of 208
pro vyhledávání: '"Fryzlewicz, P."'
Autor:
Fearnhead, Paul, Fryzlewicz, Piotr
A manuscript version of the chapter "The Multiple Change-in-Gaussian-Mean Problem" from the book "Change-Point Detection and Data Segmentation" by Fearnhead and Fryzlewicz, currently in preparation. All R code and data to accompany this chapter and t
Externí odkaz:
http://arxiv.org/abs/2405.06796
Autor:
Kostic, Anica, Fryzlewicz, Piotr
For estimating the proportion of false null hypotheses in multiple testing, a family of estimators by Storey (2002) is widely used in the applied and statistical literature, with many methods suggested for selecting the parameter $\lambda$. Inspired
Externí odkaz:
http://arxiv.org/abs/2309.10017
We consider the problem of uncertainty quantification in change point regressions, where the signal can be piecewise polynomial of arbitrary but fixed degree. That is we seek disjoint intervals which, uniformly at a given confidence level, must each
Externí odkaz:
http://arxiv.org/abs/2307.03639
This paper studies reinforcement learning (RL) in doubly inhomogeneous environments under temporal non-stationarity and subject heterogeneity. In a number of applications, it is commonplace to encounter datasets generated by system dynamics that may
Externí odkaz:
http://arxiv.org/abs/2211.03983
Publikováno v:
Journal of the Royal Statistical Society Series B: Statistical Methodology, Volume 86, Issue 2, April 2024
Detecting change-points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient methods for detecting a change will depend on both of these features, and
Externí odkaz:
http://arxiv.org/abs/2211.03860
Autor:
Fearnhead, Paul, Fryzlewicz, Piotr
This chapter overviews some of the work on detecting and estimating the location of a single change. We first consider the most common change-point problem, namely that of detecting a change in mean, before looking at extensions to detecting other ty
Externí odkaz:
http://arxiv.org/abs/2210.07066
We consider offline reinforcement learning (RL) methods in possibly nonstationary environments. Many existing RL algorithms in the literature rely on the stationarity assumption that requires the system transition and the reward function to be consta
Externí odkaz:
http://arxiv.org/abs/2203.01707
Autor:
Fryzlewicz, Piotr
We propose Robust Narrowest Significance Pursuit (RNSP), a methodology for detecting localized regions in data sequences which each must contain a change-point in the median, at a prescribed global significance level. RNSP works by fitting the postul
Externí odkaz:
http://arxiv.org/abs/2109.02487
Autor:
Cho, Haeran, Fryzlewicz, Piotr
We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based on the wild contrast maximisation principle, and an information crite
Externí odkaz:
http://arxiv.org/abs/2011.13884
Autor:
Fryzlewicz, Piotr
We propose Narrowest Significance Pursuit (NSP), a general and flexible methodology for automatically detecting localised regions in data sequences which each must contain a change-point (understood as an abrupt change in the parameters of an underly
Externí odkaz:
http://arxiv.org/abs/2009.05431