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Generalized, Linear, and Mixed Models
Journal of the American Statistical Association, 2006(2006). Generalized, Linear, and Mixed Models. Journal of the American Statistical Association: Vol. 101, No. 476, pp. 1724-1724.
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Linear and generalized linear mixed models
2015AbstractGeneralized linear mixed models (GLMMs) are a powerful class of statistical models that combine the characteristics of generalized linear models and mixed models (models with both fixed and random predictor variables). This chapter: reviews the conceptual and theoretical background of GLMMs, focusing on the definition and meaning of random ...
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Generalized linear mixed-effects models
2008Generalized linear mixed-effects models, more commonly known as generalized linear mixed models, are very popular in longitudinal data analysis. They are a natural combination of two modeling strands, linear mixed models and generalized linear models.
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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Using Generalized Linear (Mixed) Models in HCI
2016In HCI we often encounter dependent variables which are not (conditionally) normally distributed: we measure response-times, mouse-clicks, or the number of dialog steps it took a user to complete a task. Furthermore, we often encounter nested or grouped data; users are grouped within companies or institutes, or we obtain multiple observations within ...
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Linear and generalized linear mixed effects models
2009In Chapter 8 we learned about the concept of hierarchical modeling, a data analysis approach that is appropriate when we have multiple measurements within each of several groups. In that chapter, variation in the data was represented with a between-group sampling model for group-specific means, in addition to a within-group sampling model to represent ...
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
exaly