Standard and Robust Methods in Regression Imputation

Autor/inn/en	Moraveji, Behjat; Jafarian, Koorosh
Titel	Standard and Robust Methods in Regression Imputation
Quelle	In: International Journal of Education and Literacy Studies, 2 (2014) 3, S.32-36 (5 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2202-9478
Schlagwörter	Mathematics; Computation; Robustness (Statistics); Regression (Statistics); Statistical Data; Simulation; Statistical Distributions; Research Methodology; Error of Measurement; Multivariate Analysis + Suchen Sie Ihr Suchwort? Mathematik; Widerstandsfähigkeit; Regression; Regressionsanalyse; Simulation program; Simulationsprogramm; Wahrscheinlichkeitsverteilung; Research method; Forschungsmethode; Messfehler; Multivariate Analyse
Abstract	The aim of this paper is to provide an introduction of new imputation algorithms for estimating missing values from official statistics in larger data sets of data pre-processing, or outliers. The goal is to propose a new algorithm called IRMI (iterative robust model-based imputation). This algorithm is able to deal with all challenges like representative and non-representative outliers and a mixture of different distributions of variables. This algorithm is compared to the algorithm IVEWARE to illuminate the advantages and disadvantages of different techniques for imputation in artificial data and real data sets from official statistics, with respect to robustness are proposed, especially in presence of outliers the model-based of new algorithm is preferable. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01