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Mar 13, 2013 · Linear mixed effects models are a powerful technique for the analysis of ecological data, especially in the presence of nested or hierarchical variables. But unlike their purely fixed-effects cousins, they lack an obvious criterion to assess model fit.

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Innovative Forms Supporting Safe Methods of Work in Safety Engineering for the Development of Intelligent Specializations. NASA Astrophysics Data System (ADS) Gembalska-Kwiecień,

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The debate on the forecasting ability of non-linear models has a long history, and the Great Recession episode provides an interesting opportunity for a re-assessment of the forecasting performances of several classes of non-linear models. An extensive analysis is performed over a broad cross-country database of the main macroeconomic indicators.

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Linear mixed-effects (LME) modeling is a widely used statistical method for analyzing repeated measures or longitudinal data. Applications of linear mixed-effects modeling concentrate on adolescent health research. It is important to note if the trend is linear or

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Nonlinear multilevel mixed-effects models. 3 November 2017 Houssein Assaad, Senior Statistician and Software Developer. Tweet. You have a model that is nonlinear in the parameters. Perhaps it is a model of tree growth and therefore asymptotes to a maximum value.

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MERLIN: Stata module to fit mixed effects regression for linear and non-linear models Michael J. Crowther MESA: RATS procedure to compute and graph a spectrum using Maximum Entropy Method Tom Doan META_LR: Stata module to graph positive and negative likelihood ratios in diagnostic test Aijing Shang

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