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- W33010214 abstract "A majority of applications of wavelets in statistical data analysis are in the area of nonlinear regression and function estimation. Given a single time series, or image, it might not be possible to utilize various statistical techniques that for their implementation require more than a single observation at a fixed point in time/space. If the researcher has repeated measurements in form of func- tions/images, than wavelets can be successfully employed in a functional-type data analysis. In this paper we focus on wavelet based functional ANOVA proce- dure in which the noise is separated from the signal for different treatments, and at the same time the treatment responses are additionally split on the mean response and the treatment effect, in the spirit of traditional ANOVA. The key properties utilized are abilities of wavelets to decorrelate and regularize the inputs. Different strategies for (multivariate) shrinkage separation of treatment effects and signif- icance testing in the wavelet domain are discussed. The methods are illustrated and evaluated on test-functions and images with controlled signal-to-noise ratios and on real data from satellite monitoring." @default.
- W33010214 created "2016-06-24" @default.
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- W33010214 date "2001-01-01" @default.
- W33010214 modified "2023-09-27" @default.
- W33010214 title "Wavelet-Based Functional Data Analysis: Theory, Applications and Ramifications 1" @default.
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