activity
20082021
most citedNonparametric Time Series Summary Statistics for High-Frequency Accelerometry Data from Individuals with Advanced Dementia

14 citations · 24 across the 3 of their papers we have counts for

collaborators

7 papers

stat.ML20215 cited

Multivariate Probabilistic Regression with Natural Gradient Boosting

Michael O'Malley, Adam M. Sykulski, Rick Lumpkin +1

Many single-target regression problems require estimates of uncertainty along with the point predictions. Probabilistic regression algorithms are well-suited for these tasks. Howev…

physics.ao-ph2020

Separating Mesoscale and Submesoscale Flows from Clustered Drifter Trajectories

Sarah Oscroft, Adam M. Sykulski, Jeffrey J. Early

Drifters deployed in close proximity collectively provide a unique observational data set with which to separate mesoscale and submesoscale flows. In this paper we provide a princi…

stat.AP2020

Estimating the parameters of ocean wave spectra

Jake P. Grainger, Adam M. Sykulski, Philip Jonathan +1

Wind-generated waves are often treated as stochastic processes. There is particular interest in their spectral density functions, which are often expressed in some parametric form.…

stat.AP202014 cited

Nonparametric Time Series Summary Statistics for High-Frequency Accelerometry Data from Individuals with Advanced Dementia

Keerati Suibkitwanchai, Adam M. Sykulski, Guillermo Perez Algorta +2

Accelerometry data has been widely used to measure activity and the circadian rhythm of individuals across the health sciences, in particular with people with advanced dementia. Mo…

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…

stat.ME2019

Smoothing and Interpolating Noisy GPS Data with Smoothing Splines

Jeffrey J. Early, Adam M. Sykulski

A comprehensive methodology is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tensi…