Handling Missing Data in Ranked Set Sampling

The existence of missing observations is a very important aspect to be considered in the application of survey sampling, for example. In human populations they may be caused by a refusal of some interviewees to give the true value for the variable of interest. Traditionally, simple random sampling i...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Bouza-Herrera, Carlos N. (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:SpringerBriefs in Statistics,
Temas:
Statistics .
Biometry.
Social sciences-Statistical methods.
Statistical Theory and Methods.
Biostatistics.
Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Preface
  • Missing Observations and Data Quality Improvement
  • Sampling Using Ranked Sets: Basic Concepts
  • The Non Response  Problem: Sub-sampling among the Non Respondents
  • Imputation of the Missing Data
  • Some Numerical Studies of the Behavior of RSS.

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