Data augmentation and parameter expansion for independent or spatially correlated ordinal data

作者：Schliep Erin M^*; Hoeting Jennifer A

来源：Computational Statistics & Data Analysis, 2015, 90: 1-14.

DOI：10.1016/j.csda.2015.03.020

• Data augmentation
• Parameter expansion
• Ordinal data
• Probit model
• Spatial correlation

Data augmentation and parameter expansion can lead to improved iterative sampling algorithms for Markov chain Monte Carlo (MCMC). Data augmentation allows for simpler and more feasible simulation from a posterior distribution. Parameter expansion accelerates convergence of iterative sampling algorithms by increasing the parameter space. Data augmentation and parameter-expanded data augmentation MCMC algorithms are proposed for fitting probit models for independent ordinal response data. The algorithms are extended for fitting probit linear mixed models for spatially correlated ordinal data. The effectiveness of data augmentation and parameter-expanded data augmentation is illustrated using the probit model and ordinal response data, however, the approach can be used broadly across model and data types.

• 出版日期2015-10