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• W71186215 abstract "We introduce a semiparametric extension of multi-level regression models that includes mixed and fixed effects models as its two extreme cases. In some practical cases, one could consider the fixed effects model as an over parametrized model without modeling but just plugging in dummies. In other words, it suffers from “too many parameters but too little model”. The mixed effects model tries to overcome this by using just random effects and therefore has “too few parameters but too much model”, where “too much model” refers to the necessary assumptions made. We propose including a nonparametric term that allows the practitioner to shift the model smoothly between these extremes, depending on its data and underlying problem. Thereby, the smoothing parameter serves as its switcher. We will show that so we can filter out possible dependency between covariates and random effects. We further provide consistent bootstrap procedures for possible inference and to analyze prediction power. The positive implications of using this model are highlighted in particular for small area statistics and econometrics. This is underlined by simulation studies and a real data application.1" @default.
• W71186215 created "2016-06-24" @default.
• W71186215 creator A5053073638 @default.
• W71186215 creator A5081248463 @default.
• W71186215 date "2008-01-01" @default.
• W71186215 modified "2023-10-16" @default.
• W71186215 title "Smooth Transition from Fixed Effects to Mixed Effects Models in Multi-level regression Models" @default.
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• W71186215 hasPublicationYear "2008" @default.
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