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Abstract:

In this paper, we propose estimators based on the minimum distance for the unknown parameters of a parametric density on the unit sphere. We show that these estimators are consistent and asymptotically normally distributed. Also, we apply our proposal to develop a method that allows us to detect potential atypical values. The behavior under small samples of the proposed estimators is studied using Monte Carlo simulations. Two applications of our procedure are illustrated with real data sets. © 2017 Springer-Verlag Berlin Heidelberg

Registro:

Documento: Artículo
Título:Minimum distance method for directional data and outlier detection
Autor:Sau, M.F.; Rodriguez, D.
Filiación:Ciclo Básico Común, Universidad de Buenos Aires, Buenos Aires, Argentina
Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina
Buenos Aires, Argentina
Palabras clave:Asymptotic properties; Directional data; Outlier detection; Robust estimation; Data handling; Intelligent systems; Normal distribution; Statistics; Asymptotic properties; Directional data; Minimum distance; Outlier Detection; Real data sets; Robust estimation; Small samples; Unit spheres; Monte Carlo methods
Año:2017
Página de inicio:1
Página de fin:17
DOI: http://dx.doi.org/10.1007/s11634-017-0287-9
ISSN:18625347
Registro:http://digital.bl.fcen.uba.ar/collection/paper/document/paper_18625347_v_n_p1_Sau

Citas:

---------- APA ----------
Sau, M.F. & Rodriguez, D. (2017) . Minimum distance method for directional data and outlier detection, 1-17.
http://dx.doi.org/10.1007/s11634-017-0287-9
---------- CHICAGO ----------
Sau, M.F., Rodriguez, D. "Minimum distance method for directional data and outlier detection" (2017) : 1-17.
http://dx.doi.org/10.1007/s11634-017-0287-9
---------- MLA ----------
Sau, M.F., Rodriguez, D. "Minimum distance method for directional data and outlier detection" , 2017, pp. 1-17.
http://dx.doi.org/10.1007/s11634-017-0287-9
---------- VANCOUVER ----------
Sau, M.F., Rodriguez, D. Minimum distance method for directional data and outlier detection. 2017:1-17.
http://dx.doi.org/10.1007/s11634-017-0287-9