Jara Alejandro, Hanson Timothy E, Quintana Fernando A, Müller Peter, Rosner Gary L
Universidad de Concepción.
J Stat Softw. 2011 Apr 1;40(5):1-30.
Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian non- and semi-parametric models in R, DPpackage. Currently DPpackage includes models for marginal and conditional density estimation, ROC curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison, and for eliciting the precision parameter of the Dirichlet process prior. To maximize computational efficiency, the actual sampling for each model is carried out using compiled FORTRAN.
数据分析有时需要放宽参数假设，以便在概率模型误设的情况下获得建模灵活性和稳健性。在贝叶斯框架下，这是通过在函数空间（如所有概率分布的空间或所有回归函数的空间）上放置先验分布来实现的。不幸的是，函数空间上的后验分布非常复杂，因此抽样方法起着关键作用。本文介绍了一组简单而全面的程序，用于在R语言的DPpackage中实现一些贝叶斯非参数和半参数模型。目前，DPpackage包括用于边际和条件密度估计的模型、ROC曲线分析、区间删失数据、二元回归数据、项目反应数据、使用广义线性混合模型的纵向和聚类数据，以及使用广义相加模型的回归数据。该软件包还包含用于计算模型比较的伪贝叶斯因子以及引出狄利克雷过程先验精度参数的函数。为了最大限度地提高计算效率，每个模型的实际抽样使用编译后的FORTRAN进行。
