Bayesian Posterior Estimation of Logit Parameters with Small Samples

By F. Galindo‐Garre, Jeroen K. Vermunt, Wicher P. Bergsma. 2004

56 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9fac9dd2c3325dd56dbe0ece3cee0f25021067c4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. F. G. Garre
2. Jeroen K. Vermunt

In Statistics and Probability

1. Jeroen K. Vermunt
2. F. G. Garre
3. L. DeCarlo
4. Karen Nylund-Gibson
5. Katherine E. Masyn
6. Jay Magidson
7. B. Muthén
8. Maria Iannario

Citations in the corpus

• AVOIDING BOUNDARY ESTIMATES IN LATENT CLASS ANALYSIS BY BAYESIAN POSTERIOR MODE ESTIMATION (2006, methodology): “Similar results were obtained in a simulation study conducted by Galindo et al. (2004) for logit models.”
• On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix (2011, unknown): “…suggested that posterior mode estimation gave more reliable parameter estimates and standard errors than either MLE or parametric bootstrapping; Galindo-Garre, Vermunt, and Bergsma (2004) presented a simulation that suggested advantages of posterior mode estimation over expected a posteriori…”