多維及相關數據的非參數估計問題(1/2)
Date Issued
2003-07-31
Date
2003-07-31
Author(s)
DOI
912118M002001
Abstract
Kernel methods for nonparametric estimation have been widely used in
practice. Local linear regression has many advantages. We study quadratic
interpolation of local linear smoothers and show that it substantially enhances the
stability. This is of particular value when estimating multivariate surfaces. Problems
of nonparametric filtering arise frequently in engineering and financial economics.
Nonparametric filters often involve some filtering parameters. These parameters can
be chosen to optimize performance locally at each time point or globally over a time
interval. We propose to choose the filtering parameters via minimizing the prediction
error and show that, under mild conditions, the adaptive filter performs nearly as well
as the ideal filter. The techniques can also be applied to volatility estimation in
financial economics.
Subjects
adaptive filtering
autoregression
exponential smoothing
interpolation
local linear regression
variance reduction
volatility
Publisher
臺北市：國立臺灣大學數學系暨研究所
Type
report
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