On Bandwidth Selection in Local Polynomial Regression Analysis and Its Application to Multi-resolution Analysis of Non-uniform Data

This paper studies adaptive bandwidth selection method for local polynomial regression (LPR) and its application to multi-resolution analysis (MRA) of non-uniformly sampled data. In LPR, the observations are modeled locally by a polynomial using least-squares criterion with a kernel having a certain support or bandwidth so that a better bias-variance tradeoff can be achieved. In this paper, two bandwidth selection methods, namely the Fan and Gijbels’s bandwidth selection (FGBS) method (Fan and Gijbels, Local Polynomial Modelling and Its Applications, Chapman and Hall, London, 1996; Fan and Gijbels, StatSin 57:371–394, 1995) in the statistical community and the intersection of confidence intervals (ICI) method commonly used in the signal and image processing communities, are reviewed and compared in terms of their performance and implementation complexity using standard testing data sets. Furthermore, using the result of Stankovi (IEEE Trans Signal Proc 52:1228–1234, 2004), a new refined ICI-based adaptive bandwidth selection method for LPR and its associated reliability analysis are proposed. In addition, recursive implementations of LPR with the two classes of bandwidth selection methods are considered for online applications. Simulation results show that the performances of the FGBS method and the refined ICI method are comparable for the data sets tested. Since LPR with adaptive bandwidths can be naturally applied to non-uniformly sampled noisy observations, we propose to use it as a pre-processing step to a conventional MRA so that a MRA of non-uniformly sampled data can be realized. Simulation results show that the proposed LPR-based MRA gives better results than conventional linear interpolation of the data.