Richly parameterized linear models: additive, time series, and spatial models using random effects

A First Step toward a Unified Theory of Richly Parameterized Linear ModelsUsing mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based unders...

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Detalles Bibliográficos
Autor principal: Hodges, James S
Lenguaje:eng
Publicado: Taylor and Francis 2013
Materias:
Mathematical Physics and Mathematics
Acceso en línea:http://cds.cern.ch/record/1633686
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author Hodges, James S
author_facet Hodges, James S
author_sort Hodges, James S
collection CERN
description A First Step toward a Unified Theory of Richly Parameterized Linear ModelsUsing mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based understanding of models with random effects.Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results. The aut
id cern-1633686
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2013
publisher Taylor and Francis
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spelling cern-16336862021-04-21T21:32:08Zhttp://cds.cern.ch/record/1633686engHodges, James SRichly parameterized linear models: additive, time series, and spatial models using random effectsMathematical Physics and Mathematics A First Step toward a Unified Theory of Richly Parameterized Linear ModelsUsing mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based understanding of models with random effects.Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results. The autTaylor and Francisoai:cds.cern.ch:16336862013
spellingShingle Mathematical Physics and Mathematics
Hodges, James S
Richly parameterized linear models: additive, time series, and spatial models using random effects
title Richly parameterized linear models: additive, time series, and spatial models using random effects
title_full Richly parameterized linear models: additive, time series, and spatial models using random effects
title_fullStr Richly parameterized linear models: additive, time series, and spatial models using random effects
title_full_unstemmed Richly parameterized linear models: additive, time series, and spatial models using random effects
title_short Richly parameterized linear models: additive, time series, and spatial models using random effects
title_sort richly parameterized linear models: additive, time series, and spatial models using random effects
topic Mathematical Physics and Mathematics
url http://cds.cern.ch/record/1633686
work_keys_str_mv AT hodgesjamess richlyparameterizedlinearmodelsadditivetimeseriesandspatialmodelsusingrandomeffects

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