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Other Statistics and Probability Commons™
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- Bayesian Model Averaging and Semiparametric Regression (2)
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Articles 1 - 3 of 3
Full-Text Articles in Other Statistics and Probability
The Dirty “S” Word: Innovative Teaching Techniques For Counselor Educators Facilitating Learning In Statistics And Research, Rebecca L. Tadlock-Marlo, Megan Michalak
The Dirty “S” Word: Innovative Teaching Techniques For Counselor Educators Facilitating Learning In Statistics And Research, Rebecca L. Tadlock-Marlo, Megan Michalak
Rebecca L Tadlock-Marlo
Innovative pedagogy will be presented and discussed to help make research a less painful class to both teach and learn. Foci include teaching methods, potential assignments, and suggestions for activities to help facilitate a more fluid learning process for counselors. Attendees will explore aspects of helping students overcome their fear of both statistics and research.
Modeling Dependence Using Skew T Copulas: Bayesian Inference And Applications, Michael S. Smith, Quan Gan, Robert Kohn
Modeling Dependence Using Skew T Copulas: Bayesian Inference And Applications, Michael S. Smith, Quan Gan, Robert Kohn
Michael Stanley Smith
[THIS IS AN AUGUST 2010 REVISION THAT REPLACES ALL PREVIOUS VERSIONS.]
We construct a copula from the skew t distribution of Sahu, Dey & Branco (2003). This copula can capture asymmetric and extreme dependence between variables, and is one of the few copulas that can do so and still be used in high dimensions effectively. However, it is difficult to estimate the copula model by maximum likelihood when the multivariate dimension is high, or when some or all of the marginal distributions are discrete-valued, or when the parameters in the marginal distributions and copula are estimated jointly. We therefore propose …
Estimation Of Copula Models With Discrete Margins Via Bayesian Data Augmentation, Michael S. Smith, Mohamad A. Khaled
Estimation Of Copula Models With Discrete Margins Via Bayesian Data Augmentation, Michael S. Smith, Mohamad A. Khaled
Michael Stanley Smith
Estimation of copula models with discrete margins is known to be difficult beyond the bivariate case. We show how this can be achieved by augmenting the likelihood with latent variables, and computing inference using the resulting augmented posterior. To evaluate this we propose two efficient Markov chain Monte Carlo sampling schemes. One generates the latent variables as a block using a Metropolis-Hasting step with a proposal that is close to its target distribution, the other generates them one at a time. Our method applies to all parametric copulas where the conditional copula functions can be evaluated, not just elliptical copulas …