Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
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DOI：
10.2139/ssrn.1000681
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发表时间：
2007-07
影响因子：
3.7
通讯作者：
Remy Cottet;R. Kohn;D. Nott
中科院分区：
文献类型：
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作者：
Remy Cottet;R. Kohn;D. Nott
我们将双指数回归模型中的均值和方差项表示为预测因子的加性函数，并使用贝叶斯变量选择来确定哪些预测因子进入模型以及它们是线性进入还是灵活进入。当方差项为零时，得到一个广义加性模型，当预测因子线性进入均值时，得到一个广义线性模型。利用马尔可夫链蒙特卡罗模拟对模型进行了估计，并用真实和模拟数据集说明了该方法。
We express the mean and variance terms in a double-exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model and whether they enter linearly or flexibly. When the variance term is null, we obtain a generalized additive model, which becomes a generalized linear model if the predictors enter the mean linearly. The model is estimated using Markov chain Monte Carlo simulation, and the methodology is illustrated using real and simulated data sets.