Bernoulli

Functional data analysis in an operator-based mixed-model framework

Bo Markussen

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Abstract

Functional data analysis in a mixed-effects model framework is done using operator calculus. In this approach the functional parameters are treated as serially correlated effects giving an alternative to the penalized likelihood approach, where the functional parameters are treated as fixed effects. Operator approximations for the necessary matrix computations are proposed, and semi-explicit and numerically stable formulae of linear computational complexity are derived for likelihood analysis. The operator approach renders the usage of a functional basis unnecessary and clarifies the role of the boundary conditions.

Article information

Source
Bernoulli, Volume 19, Number 1 (2013), 1-17.

Dates
First available in Project Euclid: 18 January 2013

Permanent link to this document
https://projecteuclid.org/euclid.bj/1358531738

Digital Object Identifier
doi:10.3150/11-BEJ389

Mathematical Reviews number (MathSciNet)
MR3019483

Zentralblatt MATH identifier
1259.62001

Keywords
determinant approximation Gaussian process Green’s function random effect serial correlation operator approximation

Citation

Markussen, Bo. Functional data analysis in an operator-based mixed-model framework. Bernoulli 19 (2013), no. 1, 1--17. doi:10.3150/11-BEJ389. https://projecteuclid.org/euclid.bj/1358531738


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References

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