151 citations · 692 across the 40 of their papers we have counts for
5 papers · 1 filter
Functional Principal Components Analysis of Spatially Correlated Data
Chong Liu, Surajit Ray, Giles Hooker
This paper focuses on the analysis of spatially correlated functional data. The between-curve correlation is modeled by correlating functional principal component scores of the fun…
Maximal Autocorrelation Functions in Functional Data Analysis
Giles Hooker, Steven Roberts
This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-represent a functional subspace in terms of directi…
Truncated Linear Models for Functional Data
Peter Hall, Giles Hooker
A conventional linear model for functional data involves expressing a response variable in terms of the explanatory function , via the model: $Y=a+\int_I b(t)X(t)dt+\hbox…
Formal Hypothesis Tests for Additive Structure in Random Forests
Lucas Mentch, Giles Hooker
While statistical learning methods have proved powerful tools for predictive modeling, the black-box nature of the models they produce can severely limit their interpretability and…
Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests
Lucas Mentch, Giles Hooker
This work develops formal statistical inference procedures for machine learning ensemble methods. Ensemble methods based on bootstrapping, such as bagging and random forests, have…