Structured Families of Linear Models with Orthogonal Base - Moreira, Elsa
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In statistical inference, the structured family of models (s.f.m) are constituted by unit models corresponding to the treatments of a linear model called the base model, which can be orthogonal with for instance fixed effects and cross-nesting of factors. On the side, the unit models can be for instance multiple linear regressions or log-linear models on the same parameters. Through this approach, we can study the influence of the factors of the base model on the linear combinations of unit models parameters. Two applications to real data are presented to illustrate the approach. In these case…mehr

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Produktbeschreibung
In statistical inference, the structured family of models (s.f.m) are constituted by unit models corresponding to the treatments of a linear model called the base model, which can be orthogonal with for instance fixed effects and cross-nesting of factors. On the side, the unit models can be for instance multiple linear regressions or log-linear models on the same parameters. Through this approach, we can study the influence of the factors of the base model on the linear combinations of unit models parameters. Two applications to real data are presented to illustrate the approach. In these case studies, s.f.m. with log-linear unit models are applied to hydrology, through the analysis of drought classes transitions for comparing sub-periods of the same time series or to compare sub-regions.
Autorenporträt
Elsa Moreira has PhD in Statistics by the School of Sciences and Technology, Nova University of Lisbon. She is a researcher since 2005, at the research units CMA-Center of Mathematics and Applications and LEAF-Landscape, Environment, Agriculture and Food. She specializes in developing and applying statistical and stochastic methods to hydrology.