A tutorial on generalizing the default Bayesian t-test via posterior sampling and encompassing priors
| Autor: | Faulkenberry, Thomas J. |
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| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Communications for Statistical Applications and Methods. 26 (2019) 217-238 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.29220/CSAM.2019.26.2.217 |
| Popis: | With the advent of so-called default Bayesian hypothesis tests, scientists in applied fields have gained access to a powerful and principled method for testing hypotheses. However, such default tests usually come with a compromise, requiring the analyst to accept a one-size-fits-all approach to hypothesis testing. Further, such tests may not have the flexibility to test problems the scientist really cares about. In this tutorial, I demonstrate a flexible approach to generalizing one specific default test (the JZS t-test; Rouder et al., 2009) that is becoming increasingly popular in the social and behavioral sciences. The approach uses two theoretical results, the Savage-Dickey density ratio (Dickey and Lientz, 1980) and the technique of encompassing priors (Klugkist et al., 2005) in combination with MCMC sampling via an easy-to-use probabilistic modeling package for R called Greta. Through a comprehensive mathematical description of the techniques as well as illustrative examples, the reader is presented with a general, flexible workflow that can be extended to solve problems relevant to his or her own work. Comment: in press at Communications for Statistical Methods and Applications |
| Databáze: | arXiv |
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