Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves
John A. Rice and
Colin O. Wu
Biometrics, 2001, vol. 57, issue 1, 253-259
Summary. We propose a method of analyzing collections of related curves in which the individual curves are modeled as spline functions with random coefficients. The method is applicable when the individual curves are sampled at variable and irregularly spaced points. This produces a low‐rank, low‐frequency approximation to the covariance structure, which can be estimated naturally by the EM algorithm. Smooth curves for individual trajectories are constructed as best linear unbiased predictor (BLUP) estimates, combining data from that individual and the entire collection. This framework leads naturally to methods for examining the effects of covariates on the shapes of the curves. We use model selection techniques—Akaike information criterion (AIC), Bayesian information criterion (BIC), and cross‐validation—to select the number of breakpoints for the spline approximation. We believe that the methodology we propose provides a simple, flexible, and computationally efficient means of functional data analysis.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24) Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:57:y:2001:i:1:p:253-259
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X
Access Statistics for this article
More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().