摘要翻译:
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,我们得到了一个广义加性模型,当预测器线性进入均值时,该模型变成了一个广义线性模型。利用马尔可夫链Monte Carlo模拟对模型进行了估计,并用真实和模拟的数据集对方法进行了说明。
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英文标题:
《Variable Selection and Model Averaging in Semiparametric Overdispersed
Generalized Linear Models》
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作者:
Remy Cottet, Robert Kohn and David Nott
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最新提交年份:
2007
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分类信息:
一级分类:Statistics 统计学
二级分类:Methodology 方法论
分类描述:Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计,调查,模型选择,多重检验,多元方法,信号和图像处理,时间序列,平滑,空间统计,生存分析,非参数和半参数方法
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英文摘要:
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.
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PDF链接:
https://arxiv.org/pdf/707.2158