• Publications
  • Influence
Functional Data Analysis
  • H. Müller
  • Computer Science
  • International Encyclopedia of Statistical Science
  • 2011
An overview of FDA is provided, starting with simple statistical notions such as mean and covariance functions, then covering some core techniques, the most popular of which is Functional Principal Component Analysis (FPCA), an important dimension reduction tool and in sparse data situations can be used to impute functional data that are sparsely observed. Expand
Functional Data Analysis for Sparse Longitudinal Data
We propose a nonparametric method to perform functional principal components analysis for the case of sparse longitudinal data. The method aims at irregularly spaced longitudinal data, where theExpand
Kernel estimation of regression functions
For the nonparametric estimation of regression functions with a one-dimensional design parameter, a new kernel estimate is defined and shown to be superior to the one introduced by Priestley and ChaoExpand
Kernels for Nonparametric Curve Estimation
SUMMARY The choice of kernels for the nonparametric estimation of regression functions and of their derivatives is investigated. Explicit expressions are obtained for kernels minimizing theExpand
Estimating regression functions and their derivatives by the kernel method
A kernel estimate is introduced for obtaining a nonparametric estimate of a regression function, as well as of its derivatives. In many fields of engineering and biomedicine, the estimation ofExpand
an otherwise smooth regression model are proposed. The assumptions needed are much weaker than those made in parametric models. The proposed estimators apply as well to the detection ofExpand
Modelling sparse generalized longitudinal observations with latent Gaussian processes
In longitudinal data analysis one frequently encounters non-Gaussian data that are repeatedly collected for a sample of individuals over time. The repeated observations could be binomial, Poisson orExpand
Smooth optimum kernel estimators near endpoints
SUMMARY Kernel estimators for smooth curves like density, spectral density or regression functions require modifications when estimating near endpoints of the support, both for practical andExpand
Hazard rate estimation under random censoring with varying kernels and bandwidths.
The estimation of hazard rates under random censoring with the kernel method is discussed and a practically feasible method incorporating the new boundary kernels and local bandwidth choices is implemented and illustrated with survival data from a leukemia study. Expand