Minimum distance method for directional data and outlier detection

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 pot...

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Detalles Bibliográficos
Autor principal: Rodríguez, Daniela Andrea
Publicado: 2018
Materias:
Asymptotic properties
Directional data
Outlier detection
Robust estimation
Data handling
Intelligent systems
Normal distribution
Statistics
Minimum distance
Outlier Detection
Real data sets
Small samples
Unit spheres
Monte Carlo methods
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_18625347_v12_n3_p587_Sau
http://hdl.handle.net/20.500.12110/paper_18625347_v12_n3_p587_Sau
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id paper:paper_18625347_v12_n3_p587_Sau
record_format dspace
spelling paper:paper_18625347_v12_n3_p587_Sau2023-06-08T16:29:32Z Minimum distance method for directional data and outlier detection Rodríguez, Daniela Andrea 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 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. © Springer-Verlag Berlin Heidelberg 2017. Fil:Rodriguez, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2018 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_18625347_v12_n3_p587_Sau http://hdl.handle.net/20.500.12110/paper_18625347_v12_n3_p587_Sau
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic 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
spellingShingle 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
Rodríguez, Daniela Andrea
Minimum distance method for directional data and outlier detection
topic_facet 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
description 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. © Springer-Verlag Berlin Heidelberg 2017.
author Rodríguez, Daniela Andrea
author_facet Rodríguez, Daniela Andrea
author_sort Rodríguez, Daniela Andrea
title Minimum distance method for directional data and outlier detection
title_short Minimum distance method for directional data and outlier detection
title_full Minimum distance method for directional data and outlier detection
title_fullStr Minimum distance method for directional data and outlier detection
title_full_unstemmed Minimum distance method for directional data and outlier detection
title_sort minimum distance method for directional data and outlier detection
publishDate 2018
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_18625347_v12_n3_p587_Sau
http://hdl.handle.net/20.500.12110/paper_18625347_v12_n3_p587_Sau
work_keys_str_mv AT rodriguezdanielaandrea minimumdistancemethodfordirectionaldataandoutlierdetection
_version_ 1768545392169844736

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