Choi Taeryon, Schervish Mark J, Schmitt Ketra A, Small Mitchell J
Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, Maryland 21250, USA.
Biometrics. 2008 Jun;64(2):424-30. doi: 10.1111/j.1541-0420.2007.00887.x. Epub 2007 Aug 30.
We consider a set of independent Bernoulli trials with possibly different success probabilities that depend on covariate values. However, the available data consist only of aggregate numbers of successes among subsets of the trials along with all of the covariate values. We still wish to estimate the parameters of a modeled relationship between the covariates and the success probabilities, e.g., a logistic regression model. In this article, estimation of the parameters is made from a Bayesian perspective by using a Markov chain Monte Carlo algorithm based only on the available data. The proposed methodology is applied to both simulation studies and real data from a dose-response study of a toxic chemical, perchlorate.
我们考虑一组独立的伯努利试验，其成功概率可能不同，且依赖于协变量值。然而，可用数据仅包括试验子集中成功的总数以及所有协变量值。我们仍然希望估计协变量与成功概率之间建模关系的参数，例如逻辑回归模型。在本文中，通过仅基于可用数据使用马尔可夫链蒙特卡罗算法，从贝叶斯角度进行参数估计。所提出的方法应用于模拟研究以及来自有毒化学物质高氯酸盐剂量反应研究的实际数据。
