Recovering Gradients from Sparsely Observed Functional Data

DOI10.1111/biom.12011zbMath1274.62343OpenAlexW1896080967WikidataQ30588717 ScholiaQ30588717MaRDI QIDQ2846447

Ian W. McKeague, Sara López-Pintado

Publication date: 5 September 2013

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3718453

zbMATH Keywords

functional data analysisill-posed inverse problemgrowth trajectoriesnonparametric empirical Bayestied-down Brownian motion

Mathematics Subject Classification ID

Order statistics; empirical distribution functions (62G30)

Related Items (2)

Growth dynamics and heritability for plant high‐throughput phenotyping studies using hierarchical functional data analysis ⋮ Estimation of partial derivative functionals with application to human mortality data analysis

Uses Software

• growthrate

Cites Work

• Functional linear regression analysis for longitudinal data
• Bayesian inverse problems with Gaussian priors
• Adaptive Bayesian estimation using a Gaussian random field with inverse gamma bandwidth
• Rates of contraction of posterior distributions based on Gaussian process priors
• Convergence rates of posterior distributions for non iid observations
• Functional data analysis.
• Properties of principal component methods for functional and longitudinal data analysis
• Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves
• A Constrainedℓ₁Minimization Approach to Sparse Precision Matrix Estimation
• Latent Curve Models
• Practical Approximate Solutions to Linear Operator Equations When the Data are Noisy
• Prediction of Growth: A Hierarchical Bayesian Approach
• Principal component models for sparse functional data
• A Bayesian Model for Growth Curve Analysis
• Convergence Rates of Approximate Least Squares Solutions of Linear Integral and Operator Equations of the First Kind
• Estimating Derivatives for Samples of Sparsely Observed Functions, With Application to Online Auction Dynamics
• Functional Data Analysis for Sparse Longitudinal Data

This page was built for publication: Recovering Gradients from Sparsely Observed Functional Data