Outlier detection in regression using an iterated one-step approximation to the Huber-skip estimator

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Standard

Outlier detection in regression using an iterated one-step approximation to the Huber-skip estimator. / Johansen, Søren; Nielsen, Bent.

I: Econometrics, Bind 1, Nr. 1, 2013, s. 53-70.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Johansen, S & Nielsen, B 2013, 'Outlier detection in regression using an iterated one-step approximation to the Huber-skip estimator', Econometrics, bind 1, nr. 1, s. 53-70. https://doi.org/10.3390/econometrics1010053

APA

Johansen, S., & Nielsen, B. (2013). Outlier detection in regression using an iterated one-step approximation to the Huber-skip estimator. Econometrics, 1(1), 53-70. https://doi.org/10.3390/econometrics1010053

Vancouver

Johansen S, Nielsen B. Outlier detection in regression using an iterated one-step approximation to the Huber-skip estimator. Econometrics. 2013;1(1):53-70. https://doi.org/10.3390/econometrics1010053

Author

Johansen, Søren ; Nielsen, Bent. / Outlier detection in regression using an iterated one-step approximation to the Huber-skip estimator. I: Econometrics. 2013 ; Bind 1, Nr. 1. s. 53-70.

Bibtex

@article{e13c1d356567473884cb5d8c38c0c39d,
title = "Outlier detection in regression using an iterated one-step approximation to the Huber-skip estimator",
abstract = " In regression we can delete outliers based upon a preliminary estimator and reestimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber-skip estimator. We provide a stochastic recursion equation for the estimation error in terms of a kernel, the previous estimation error and a uniformly small error term. The main contribution is the analysis of the solution of the stochastic recursion equation as a fixed point, and the results that the normalized estimation errors are tight and are close to a linear function of the kernel, thus providing a stochastic expansion of the estimators, which is the same as for the Huber-skip. This implies that the iterated estimator is a close approximation of the Huber-skip",
keywords = "Faculty of Social Sciences, Huber-skip, iteration, one-step M-estimators, unit roots",
author = "S{\o}ren Johansen and Bent Nielsen",
year = "2013",
doi = "10.3390/econometrics1010053",
language = "English",
volume = "1",
pages = "53--70",
journal = "Econometrics",
issn = "2225-1146",
publisher = "MDPI AG",
number = "1",

}

RIS

TY - JOUR

T1 - Outlier detection in regression using an iterated one-step approximation to the Huber-skip estimator

AU - Johansen, Søren

AU - Nielsen, Bent

PY - 2013

Y1 - 2013

N2 - In regression we can delete outliers based upon a preliminary estimator and reestimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber-skip estimator. We provide a stochastic recursion equation for the estimation error in terms of a kernel, the previous estimation error and a uniformly small error term. The main contribution is the analysis of the solution of the stochastic recursion equation as a fixed point, and the results that the normalized estimation errors are tight and are close to a linear function of the kernel, thus providing a stochastic expansion of the estimators, which is the same as for the Huber-skip. This implies that the iterated estimator is a close approximation of the Huber-skip

AB - In regression we can delete outliers based upon a preliminary estimator and reestimate the parameters by least squares based upon the retained observations. We study the properties of an iteratively defined sequence of estimators based on this idea. We relate the sequence to the Huber-skip estimator. We provide a stochastic recursion equation for the estimation error in terms of a kernel, the previous estimation error and a uniformly small error term. The main contribution is the analysis of the solution of the stochastic recursion equation as a fixed point, and the results that the normalized estimation errors are tight and are close to a linear function of the kernel, thus providing a stochastic expansion of the estimators, which is the same as for the Huber-skip. This implies that the iterated estimator is a close approximation of the Huber-skip

KW - Faculty of Social Sciences

KW - Huber-skip

KW - iteration

KW - one-step M-estimators

KW - unit roots

U2 - 10.3390/econometrics1010053

DO - 10.3390/econometrics1010053

M3 - Journal article

VL - 1

SP - 53

EP - 70

JO - Econometrics

JF - Econometrics

SN - 2225-1146

IS - 1

ER -

ID: 44881283