Setting M-Estimation Parameters for Detection and Treatment of Influential Values

Recent research on the use of M-estimation methodology for detecting and treating verified influential values in economic surveys found that initial parameter settings affect effectiveness. In this article, we explore the basic question of how to develop initial settings for the M-estimation paramet...

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Bibliographic Details
Main Authors: Mulry Mary H., Kaputa Stephen, Thompson Katherine J.
Format: Article
Language:English
Published: Sciendo 2018-06-01
Series:Journal of Official Statistics
Subjects:
Online Access:https://doi.org/10.2478/jos-2018-0022
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spelling doaj-5b4893ad228544aca53d8e3a5fd0309f2021-09-06T19:41:47ZengSciendoJournal of Official Statistics2001-73672018-06-0134248350110.2478/jos-2018-0022jos-2018-0022Setting M-Estimation Parameters for Detection and Treatment of Influential ValuesMulry Mary H.0Kaputa Stephen1Thompson Katherine J.2U.S. Census Bureau. 4600 Silver Hill Road, Washington, DC20233, U.S.A.U.S. Census Bureau. 4600 Silver Hill Road, Washington, DC20233, U.S.A.U.S. Census Bureau. 4600 Silver Hill Road, Washington, DC20233, U.S.A.Recent research on the use of M-estimation methodology for detecting and treating verified influential values in economic surveys found that initial parameter settings affect effectiveness. In this article, we explore the basic question of how to develop initial settings for the M-estimation parameters. The economic populations that we studied are highly skewed and are consequently highly stratified. While we investigated settings for several parameters, the most challenging problem was to develop an “automatic” data-driven method for setting the initial value of the tuning constant φ, the parameter with the greatest influence on performance of the algorithm. Of all the methods that we considered, we found that methods defined in terms of the accuracy of published estimates can be implemented on a large scale and yielded the best performance. We illustrate the methodology with an empirical analysis of 36 consecutive months of data from 19 industries in the Monthly Wholesale Trade Survey.https://doi.org/10.2478/jos-2018-0022outliereconomic surveysmonthly wholesale trade survey
collection DOAJ
language English
format Article
sources DOAJ
author Mulry Mary H.
Kaputa Stephen
Thompson Katherine J.
spellingShingle Mulry Mary H.
Kaputa Stephen
Thompson Katherine J.
Setting M-Estimation Parameters for Detection and Treatment of Influential Values
Journal of Official Statistics
outlier
economic surveys
monthly wholesale trade survey
author_facet Mulry Mary H.
Kaputa Stephen
Thompson Katherine J.
author_sort Mulry Mary H.
title Setting M-Estimation Parameters for Detection and Treatment of Influential Values
title_short Setting M-Estimation Parameters for Detection and Treatment of Influential Values
title_full Setting M-Estimation Parameters for Detection and Treatment of Influential Values
title_fullStr Setting M-Estimation Parameters for Detection and Treatment of Influential Values
title_full_unstemmed Setting M-Estimation Parameters for Detection and Treatment of Influential Values
title_sort setting m-estimation parameters for detection and treatment of influential values
publisher Sciendo
series Journal of Official Statistics
issn 2001-7367
publishDate 2018-06-01
description Recent research on the use of M-estimation methodology for detecting and treating verified influential values in economic surveys found that initial parameter settings affect effectiveness. In this article, we explore the basic question of how to develop initial settings for the M-estimation parameters. The economic populations that we studied are highly skewed and are consequently highly stratified. While we investigated settings for several parameters, the most challenging problem was to develop an “automatic” data-driven method for setting the initial value of the tuning constant φ, the parameter with the greatest influence on performance of the algorithm. Of all the methods that we considered, we found that methods defined in terms of the accuracy of published estimates can be implemented on a large scale and yielded the best performance. We illustrate the methodology with an empirical analysis of 36 consecutive months of data from 19 industries in the Monthly Wholesale Trade Survey.
topic outlier
economic surveys
monthly wholesale trade survey
url https://doi.org/10.2478/jos-2018-0022
work_keys_str_mv AT mulrymaryh settingmestimationparametersfordetectionandtreatmentofinfluentialvalues
AT kaputastephen settingmestimationparametersfordetectionandtreatmentofinfluentialvalues
AT thompsonkatherinej settingmestimationparametersfordetectionandtreatmentofinfluentialvalues
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