In this review, we give a general overview of latent variable models. We introduce the general model and discuss various inferential approaches. Afterward, we present several commonly applied special cases, including ...
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In this review, we give a general overview of latent variable models. We introduce the general model and discuss various inferential approaches. Afterward, we present several commonly applied special cases, including mixture or latent class models, as well as mixed models. We apply many of these models to a single data set with simple structure, allowing for easy comparison of the results. This allows us to discuss advantages and disadvantages of the various approaches, but also to illustrate several problems inherently linked to models incorporating latent structures. Finally, we touch on model extensions and applications and highlight several issues often ignored when applying latent variable models.
Organizing a graduate program in statistics and data science raises many questions, offering a variety of opportunities while presenting a multitude of choices. The call for graduate programs in statistics and data sc...
Organizing a graduate program in statistics and data science raises many questions, offering a variety of opportunities while presenting a multitude of choices. The call for graduate programs in statistics and data science is overwhelming. How does it align with other (future) study programs at the secondary and postsecondary levels? What could or should be the natural home for data science in academia? Who meets the entry criteria, and who does not? Which strategic choices inevitably play a prominent role when developing a curriculum? We share our views on the why, when, where, who and what.
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