WFMM is a Windows command-line application that implements a Bayesian wavelet-based functional mixed model methodology for functional data analysis introduced in Morris and Carroll (2006). The method extends linear mixed models to functional data consisting of n curves sampled on the same grid. The user provides a file in Matlab data file format (*.mat) containing a matrix of data samples of the set of n curves sampled T times and a description of the model by the design matrix X and random effects matrix Z, and other parameters controlling the computation. It provides as output nonparametric estimates of fixed and random effects functions that have been adaptively regularized as a result of the nonlinear shrinkage prior imposed on the fixed effects wavelet coefficients.
Keywords for this software
References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
- Bauer, Alexander; Scheipl, Fabian; Küchenhoff, Helmut; Gabriel, Alice-Agnes: An introduction to semiparametric function-on-scalar regression (2018)
- Greven, Sonja; Scheipl, Fabian: A general framework for functional regression modelling (2017)
- Scheipl, Fabian; Greven, Sonja: Identifiability in penalized function-on-function regression models (2016)
- Morris, Jeffrey S.; Baladandayuthapani, Veerabhadran; Herrick, Richard C.; Sanna, Pietro; Gutstein, Howard: Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data (2011)
- Morris, Jeffrey S.; Arroyo, Cassandra; Coull, Brent A.; Ryan, Louise M.; Herrick, Richard; Gortmaker, Steven L.: Using wavelet-based functional mixed models to characterize population heterogeneity in accelerometer profiles: a case study (2006)
- Morris, Jeffrey S.; Carroll, Raymond J.: Wavelet-based functional mixed models (2006)