Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Faulkenberry, Thomas J."'
Autor:
Faulkenberry, Thomas J.
In this paper, I present three closed-form approximations of the two-sample Pearson Bayes factor. The techniques rely on some classical asymptotic results about gamma functions. These approximations permit simple closed-form calculation of the Pearso
Externí odkaz:
http://arxiv.org/abs/2310.11313
Bayes factors are an increasingly popular tool for indexing evidence from experiments. For two competing population models, the Bayes factor reflects the relative likelihood of observing some data under one model compared to the other. In general, co
Externí odkaz:
http://arxiv.org/abs/2209.08159
Autor:
Faulkenberry, Thomas J.
To investigate the structure of individual differences in performance on behavioral tasks, Haaf and Rouder (2017) developed a class of hierarchical Bayesian mixed models with varying levels of constraint on the individual effects. The models are then
Externí odkaz:
http://arxiv.org/abs/2112.05503
Autor:
Faulkenberry, Thomas J.
Publikováno v:
Biometrical Letters. 58 (2021). 1-26
In Bayesian hypothesis testing, evidence for a statistical model is quantified by the Bayes factor, which represents the relative likelihood of observed data under that model compared to another competing model. In general, computing Bayes factors is
Externí odkaz:
http://arxiv.org/abs/2011.09549
Autor:
Faulkenberry, Thomas J.
Publikováno v:
Metodoloski Zvezki: Advances in Methodology and Statistics. 17 (2020) 1-17
In this paper, I develop a formula for estimating Bayes factors directly from minimal summary statistics produced in repeated measures analysis of variance designs. The formula, which requires knowing only the $F$-statistic, the number of subjects, a
Externí odkaz:
http://arxiv.org/abs/1905.05569
Autor:
Faulkenberry, Thomas J.
Publikováno v:
Communications for Statistical Applications and Methods. 26 (2019) 217-238
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 anal
Externí odkaz:
http://arxiv.org/abs/1812.03092
Autor:
Faulkenberry, Thomas J.
Publikováno v:
Biometrical Letters 55 (2018) 31-43
Bayesian inference affords scientists with powerful tools for testing hypotheses. One of these tools is the Bayes factor, which indexes the extent to which support for one hypothesis over another is updated after seeing the data. Part of the hesitanc
Externí odkaz:
http://arxiv.org/abs/1803.00360
Autor:
Faulkenberry, Thomas J.
A popular method for indexing numerical representations is to compute an individual estimate of a response time effect, such as the SNARC effect or the numerical distance effect. Classically, this is done by estimating individual linear regression sl
Externí odkaz:
http://arxiv.org/abs/1710.08171
Autor:
Faulkenberry, Thomas J.
In this paper, I extend a method of Masson (2011) to develop an easy-to-use formula for performing Bayesian hypothesis tests from minimal ANOVA summaries.
Externí odkaz:
http://arxiv.org/abs/1710.02351