A note on the monotonicity of the ES-algorithm

Publication Type

Journal Article

Publication Date

1-2011

Abstract

In the study of the robust nonparametric regression problem, Oh et al. [The role of pseudo data for robust smoothing with application to wavelet regression, Biometrika 94 (2007), pp. 893–904] developed and named the ES algorithm. In the event that the ES algorithm converges, the robust estimator can be obtained through a sequence of conventional penalized least-squares estimates, the computation of which is fast and straightforward. However, the convergence of the ES algorithm was not established theoretically in Oh et al. In this note, we show that under a certain simple condition, the ES algorithm is monotonic. In particular, the ES algorithm does converge globally in the setting of Oh et al.

Keywords

ES algorithm, M-estimation, penalized least-squares, pseudo-data, robust smoothing

Discipline

Theory and Algorithms

Research Areas

Economic Theory

Publication

Journal of Statistical Computation and Simulation

Volume

82

Issue

5

First Page

759

Last Page

761

ISSN

0094-9655

Identifier

10.1080/00949655.2011.553195

Publisher

Taylor & Francis: STM, Behavioural Science and Public Health Titles

Additional URL

http://doi.org./10.1080/00949655.2011.553195

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