Estimation of Population Mean in the Presence of Non-Response and Measurement Error

Under classical survey sampling theory the errors mainly studied in the estimation are sampling errors. However, often non-sampling errors are more influential to the properties of the estimator than sampling errors. This is recognized by practitioners, researchers and many great works of literature...

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Autores:
Kumar, Sunil
Bhogal, Sandeep
Nataraja, N. S.
Viswanathaiah, M.
Tipo de recurso:
Article of journal
Fecha de publicación:
2015
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/66546
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/66546
http://bdigital.unal.edu.co/67574/
Palabra clave:
51 Matemáticas / Mathematics
31 Colecciones de estadística general / Statistics
Estimation
Mean Squared Error
Measurement Error
Nonresponse
Ratio Estimator
Sampling Error
Error cuadrático medio
Error de medición
Error de muestreo
Estimación
Estimador de razón.
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Under classical survey sampling theory the errors mainly studied in the estimation are sampling errors. However, often non-sampling errors are more influential to the properties of the estimator than sampling errors. This is recognized by practitioners, researchers and many great works of literature regarding non-sampling errors have been published during last two decades, especially regarding non-response error which is one of the cornerstones of the non-sampling errors. The literature handles one kind of non-sampling error at a time, although in real surveys more than one non-sampling error is usually present.In this paper, two kinds of non-sampling errors are considered at the estimation stage: non-response and measurement error. An exponential ratio type estimator has been developed to estimate the population mean of the response variable in the presence of non-response and measurement errors. Theoretically and empirically, it has been shown that the proposed estimator is more efficient than usual unbiased estimator and other existing estimators.