activity
20082026
most citedNonparametric Time Series Summary Statistics for High-Frequency Accelerometry Data from Individuals with Advanced Dementia

14 citations · 24 across the 7 of their papers we have counts for

collaborators
Showing stat.MEShow all

8 papers · 1 filter

stat.ME2025

The Whittle likelihood for mixed models with application to groundwater level time series

Jakub J. Pypkowski, Adam M. Sykulski, James S. Martin +1

Understanding the processes that influence groundwater levels is crucial for forecasting and responding to hazards such as groundwater droughts. Mixed models, which combine a fixed…

stat.ME2025

Causal tail coefficient for compound extremes in multivariate time series

Cathy Yin, Adam M. Sykulski, Almut E. D. Veraart

Extreme events are often multivariate in nature. A compound extreme occurs when a combination of variables jointly produces a significant impact, even if individual components are…

stat.ME20241 cited

Isotropy testing in spatial point patterns: nonparametric versus parametric replication under misspecification

Jakub J. Pypkowski, Adam M. Sykulski, James S. Martin

Several hypothesis testing methods have been proposed to validate the assumption of isotropy in spatial point patterns. A majority of these methods are characterised by an unknown…

stat.ME2024

Bias correction of quadratic spectral estimators

Lachlan Astfalck, Adam Sykulski, Edward Cripps

The three cardinal, statistically consistent, families of non-parametric estimators to the power spectral density of a time series are lag-window, multitaper and Welch estimators.…

stat.ME2023

Debiasing Welch's Method for Spectral Density Estimation

Lachlan C. Astfalck, Adam M. Sykulski, Edward J. Cripps

Welch's method provides an estimator of the power spectral density that is statistically consistent. This is achieved by averaging over periodograms calculated from overlapping seg…

stat.ME2019

Identifying and Responding to Outlier Demand in Revenue Management

Nicola Rennie, Catherine Cleophas, Adam M. Sykulski +1

Revenue management strongly relies on accurate forecasts. Thus, when extraordinary events cause outlier demand, revenue management systems need to recognise this and adapt both for…