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Wavelet Regression With an Emphasis on Singularity Detection A thesis presented by Denying Huang to The Department of Mathematics and Statistics in partial fulfillment of the requirements for the

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What is wavelet regression with an?

Wavelet regression is a statistical method used for analyzing and modeling signals or time series data. It combines concepts of regression analysis with wavelet transformation to capture both local and global features of the data.

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Wavelet regression with an is typically performed by researchers, statisticians, or data analysts who aim to analyze and model signals or time series data.

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To fill out wavelet regression with an, one needs to follow the specific steps and algorithms of wavelet regression analysis. This typically involves selecting an appropriate wavelet basis, decomposing the signal into wavelet coefficients, estimating the model parameters, and evaluating the goodness of fit.

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The purpose of wavelet regression with an is to model and analyze signals or time series data in a way that captures both local and global characteristics. It can help identify patterns, trends, and relationships within the data, making it useful for forecasting, noise reduction, data compression, and feature extraction.

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The specific information reported in wavelet regression with an depends on the nature of the analysis and the goals of the researcher. It typically includes the input signal or time series data, the chosen wavelet basis, the model parameters, the estimated coefficients, and the evaluation metrics or measures of fit.

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