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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Online Access: | https://doi.org/10.2478/jos-2018-0022 |
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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 |
_version_ |
1717765441562083328 |