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Autor
Dehnel Grażyna (Poznań University of Economics, Poland)
Tytuł
Robust Regression in Monthly Business Survey
Źródło
Statistics in Transition, 2015, vol. 16, nr 1, s. 137-152, rys., tab., bibliogr. s. 151-152
Słowa kluczowe
Odporne metody statystyczne, Statystyka, Obserwacje nietypowe
Robust statistical methods, Statistics, Outliers
Uwagi
summ., Materiały z konferencji Multivariate Statistical Analysis 2014, Łódź.
Abstrakt
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. (original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach
Pełny tekst
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Bibliografia
Pokaż
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  9. ROUSSEEUW, P. J., LEROY, A. M., (1987). Robust Regression and Outlier Detection. Wiley-Interscience, New York.
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Cytowane przez
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ISSN
1234-7655
Język
eng
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