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• W2135798889 abstract "Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward-constrained regression (FCR) manner. The proposed algorithm selects significant kernels one at a time, while the leave-one-out (LOO) test score is minimized subject to a simple positivity constraint in each forward stage. The model parameter estimation in each forward stage is simply the solution of jackknife parameter estimator for a single parameter, subject to the same positivity constraint check. For each selected kernels, the associated kernel width is updated via the Gauss-Newton method with the model parameter estimate fixed. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate the efficacy of the proposed approach." @default.
• W2135798889 created "2016-06-24" @default.
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• W2135798889 date "2008-01-01" @default.
• W2135798889 modified "2023-10-03" @default.
• W2135798889 title "A Forward-Constrained Regression Algorithm for Sparse Kernel Density Estimation" @default.
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• W2135798889 doi "https://doi.org/10.1109/tnn.2007.908645" @default.
• W2135798889 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18269951" @default.
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