On constrained least-squares estimation with distorted data

The paper presents a constrained least squares estimator for “distorted” data and analyses its asymptotic properties. The proposed estimator is unbiased (although inefficient) and asymptotically normal, provided that the true data were distorted by replacing them by a code that meets the Grenander c...

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Detalles Bibliográficos
Autor principal: Frank, Luis
Formato: Artículo publishedVersion
Lenguaje:Español
Publicado: CIMBAGE - IADCOM - Facultad de Ciencias Económicas - Universidad de Buenos Aires 2018
Materias:
mínimos cuadrados restringidos
datos faltantes
regresión
modelos lineales
constrained least squares
missing data
regression
linear models
Acceso en línea:https://ojs.economicas.uba.ar/CIMBAGE/article/view/1214
https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=cimbage&d=1214_oai
Aporte de:
Repositorio Digital de la Universidad de Buenos Aires (UBA) de Universidad de Buenos Aires
id I28-R145-1214_oai
record_format dspace
spelling I28-R145-1214_oai2025-02-11 Frank, Luis 2018-11-21 The paper presents a constrained least squares estimator for “distorted” data and analyses its asymptotic properties. The proposed estimator is unbiased (although inefficient) and asymptotically normal, provided that the true data were distorted by replacing them by a code that meets the Grenander conditions. Nevertheless, the properties of the estimator in the context of small samples remain unknown. El artículo presenta un estimador de mínimo cuadrático restringido para datos “falseados” y analiza sus propiedades asintóticas. El estimador propuesto es insesgado (aunque ineficiente) y asintóticamente normal, siempre que los datos verdaderos hayan sido falseados reemplazándolos por un código que satisfaga las condiciones de Grenander. Sin embargo, las propiedades del estimador en el contexto de muestras pequeñas permanecen aún desconocidas. application/pdf https://ojs.economicas.uba.ar/CIMBAGE/article/view/1214 spa CIMBAGE - IADCOM - Facultad de Ciencias Económicas - Universidad de Buenos Aires https://ojs.economicas.uba.ar/CIMBAGE/article/view/1214/1842 Cuadernos del CIMBAGE; Vol. 2 No. 20 (2018): Cuadernos del CIMBAGE; 27-38 Cuadernos del CIMBAGE; Vol. 2 Núm. 20 (2018): Cuadernos del CIMBAGE; 27-38 1669-1830 1666-5112 mínimos cuadrados restringidos datos faltantes regresión modelos lineales constrained least squares missing data regression linear models On constrained least-squares estimation with distorted data Acerca de la estimación mínimo cuadrática restringida con datos falseados info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=cimbage&d=1214_oai
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-145
collection Repositorio Digital de la Universidad de Buenos Aires (UBA)
language Español
orig_language_str_mv spa
topic mínimos cuadrados restringidos
datos faltantes
regresión
modelos lineales
constrained least squares
missing data
regression
linear models
spellingShingle mínimos cuadrados restringidos
datos faltantes
regresión
modelos lineales
constrained least squares
missing data
regression
linear models
Frank, Luis
On constrained least-squares estimation with distorted data
topic_facet mínimos cuadrados restringidos
datos faltantes
regresión
modelos lineales
constrained least squares
missing data
regression
linear models
description The paper presents a constrained least squares estimator for “distorted” data and analyses its asymptotic properties. The proposed estimator is unbiased (although inefficient) and asymptotically normal, provided that the true data were distorted by replacing them by a code that meets the Grenander conditions. Nevertheless, the properties of the estimator in the context of small samples remain unknown.
format Artículo
publishedVersion
author Frank, Luis
author_facet Frank, Luis
author_sort Frank, Luis
title On constrained least-squares estimation with distorted data
title_short On constrained least-squares estimation with distorted data
title_full On constrained least-squares estimation with distorted data
title_fullStr On constrained least-squares estimation with distorted data
title_full_unstemmed On constrained least-squares estimation with distorted data
title_sort on constrained least-squares estimation with distorted data
publisher CIMBAGE - IADCOM - Facultad de Ciencias Económicas - Universidad de Buenos Aires
publishDate 2018
url https://ojs.economicas.uba.ar/CIMBAGE/article/view/1214
https://repositoriouba.sisbi.uba.ar/gsdl/cgi-bin/library.cgi?a=d&c=cimbage&d=1214_oai
work_keys_str_mv AT frankluis onconstrainedleastsquaresestimationwithdistorteddata
AT frankluis acercadelaestimacionminimocuadraticarestringidacondatosfalseados
_version_ 1825551031292919808

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