Cross-Validation and Non-Parametric k Nearest-Neighbour Estimation  

Oleh:

Qi, Li ; Dong, Li ; Desheng, Ouyang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The Econometrics Journal vol. 9 no. 3 (2006), page 448-471.
Topik: nonparametric; k nearest neighbour; non parametric smoothing; cross validation; asymptotic normality
Fulltext: 448.pdf (345.92KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: EE39.2 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In this paper we consider the problem of estimating a non - parametric regression function using the k nearest - neighbour method. We provide asymptotic theories for the least - squares cross validation (CV) selected smoothing parameter k for both local constant and local linear estimation methods. We also establish the asymptotic normality results for the resulting non - parametric regression function estimators. Some limited Monte Carlo experiments show that the CV method performs well in finite sample applications.

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