Robust regression in monthly business survey
AbstractThere 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.
|Journal series||Statistics in Transition, ISSN 1234-7655, e-ISSN 2450-0291, (B 15 pkt)|
|Publication size in sheets||0.75|
|Keywords in Polish||regresja odporna, detekcja obserwacji odstających, statystyka przedsiębiorstw|
|Keywords in English||robust regression, outlier detection, business statistics|
|Score|| = 15.0, 19-12-2019, ArticleFromJournal|
= 15.0, 19-12-2019, ArticleFromJournal
|Publication indicators||: 2016 = 0.26|
|Citation count*||6 (2020-09-12)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.