Combining information in statistical modelling

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dc.contributor.author Peña, Daniel
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned 2009-06-25T12:49:42Z
dc.date.available 2009-06-25T12:49:42Z
dc.date.issued 1995-11
dc.identifier.uri http://hdl.handle.net/10016/4516
dc.description.abstract How to combine information from different sources is becoming an important statistical area of research under the name of Meta Analysis. This paper shows that the estimation of a parameter or the forecast of a random variable can also be seen as a process of combining information. It is shown that this approach can provide sorne useful insights on the robustness properties of sorne statistical procedures, and it also allows the comparison of statistical models within a common framework. Sorne general combining rules are illustrated using examples from ANOVA analysis, diagnostics in regression, time series forecasting, missing value estimation and recursive estimation using the Kalman Filter.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries 1995-51-17
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Analysis of variance
dc.subject.other Diagnostics
dc.subject.other Forecasting
dc.subject.other Kalman filter
dc.subject.other Linear regression
dc.subject.other Meta-Analysis
dc.subject.other Time series
dc.title Combining information in statistical modelling
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
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