ROBUST REGRESSION IN MONTHLY BUSINESS SURVEY
| Main Author: | Dehnel, Grażyna |
|---|---|
| Format: | Article |
| Terbitan: |
, 2017
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/1122313 |
| ctrlnum |
1122313 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Dehnel, Grażyna</creator><date>2017-12-21</date><description>There are many sample surveys of populations that contain outliers (extreme values). This is especially true in business, agricultural, household and medicine surveys. Outliers can have a large distorting influence on classical statistical methods that are optimal under the assumption of normality or linearity. As a result, the presence of extreme observations may adversely affect estimation, especially when it is carried out at a low level of aggregation. To deal with this problem, several alternative techniques of estimation, less sensitive to outliers, have been proposed in the statistical literature. In this paper we attempt to apply and assess some robust regression methods (LTS, M estimation, S-estimation, MM-estimation) in the business survey conducted within the framework of official statistics.</description><identifier>https://zenodo.org/record/1122313</identifier><identifier>10.21307/stattrans-2015-008</identifier><identifier>oai:zenodo.org:1122313</identifier><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>Statistics in Transition. New Series 16(1)</source><subject>robust regression</subject><subject>outlier detection</subject><subject>business statistics.</subject><title>ROBUST REGRESSION IN MONTHLY BUSINESS SURVEY</title><type>Journal:Article</type><type>Journal:Article</type><recordID>1122313</recordID></dc>
|
| format |
Journal:Article Journal |
| author |
Dehnel, Grażyna |
| title |
ROBUST REGRESSION IN MONTHLY BUSINESS SURVEY |
| publishDate |
2017 |
| topic |
robust regression outlier detection business statistics |
| url |
https://zenodo.org/record/1122313 |
| contents |
There are many sample surveys of populations that contain outliers (extreme values). This is especially true in business, agricultural, household and medicine surveys. Outliers can have a large distorting influence on classical statistical methods that are optimal under the assumption of normality or linearity. As a result, the presence of extreme observations may adversely affect estimation, especially when it is carried out at a low level of aggregation. To deal with this problem, several alternative techniques of estimation, less sensitive to outliers, have been proposed in the statistical literature. In this paper we attempt to apply and assess some robust regression methods (LTS, M estimation, S-estimation, MM-estimation) in the business survey conducted within the framework of official statistics. |
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2022-06-06T05:11:53Z |
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