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Activity Number: 460
Type: Topic Contributed
Date/Time: Wednesday, August 9, 2006 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #306339
Title: Nonparametric Harmonic Regression for Estuarine Water Quality Data
Author(s): Melanie Autin*+ and Don Edwards
Companies: University of South Carolina and University of South Carolina
Address: 219D LeConte College, Columbia, SC, 29208,
Keywords: periodicity ; generalized additive models ; harmonic regression
Abstract:

Periodicity is omnipresent in environmental time series data. For modeling estuarine water quality variables, harmonic regression analysis has long been the standard for dealing with periodicity. Generalized additive models (GAMs) allow more flexibility in the response function, permitting parametric, semiparametric, and nonparametric regression functions of the predictor variables. We compare harmonic regression, GAMs with cubic regression splines, and GAMs with cyclic regression splines in simulations and using water quality data collected from the National Estuarine Research Reserve System (NERRS). The generalized additive models are more adaptive and require less user intervention.

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