K-nearest-neighbour non-parametric estimation of regression functions in the presence of irrelevant variables  

Oleh:

Li, Rui ; Gong, Guan

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The Econometrics Journal vol. 11 no. 2 (2008), page 396-408.
Topik: k-nearest-Neighbour; Cross-validation; Irrelevant Variables; Simulations
Fulltext: 396.pdf (109.73KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: EE39.4 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We show that when estimating a non-parametric regression model, the k-nearest-neighbour non-parametric estimation method has the ability to remove irrelevant variables provided one uses a product weight function with a vector of smoothing parameters, and the least-squares cross-validation method is used to select the smoothing parameters. Simulation results are consistent with our theoretical analysis and show that the performance of the k-nn estimator is comparable to the popular kernel estimator; and it dominates a non-parametric series (spline) estimator when there exist irrelevant regressors.

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