Information and Posterior Probability Criteria for Model Selection in Local Likelihood Estimation
Information and Posterior Probability Criteria for Model Selection in Local Likelihood Estimation
复制标题
局部似然估计中模型选择的信息和后验概率准则
DOI：
10.1198/016214501750332875
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发表时间：
2001
影响因子：
3.7
通讯作者：
R. Irizarry
中科院分区：
文献类型：
--
作者：
R. Irizarry
局部似然估计已被证明是获得随协变量变化的参数估计的有效方法。为了获得这些参数的有用估计，使用近似模型。在这种情况下，考虑基于窗口的估计是有用的。我们可能需要在相互竞争的近似模型之间做出选择。在这篇文章中，我们提出了一个修改的方法来激励许多信息和后验概率标准加权似然情况。我们推导出两个最广为人知的标准，即AIC和BIC的加权版本。通过一个简单的修改，该标准也适用于窗口跨度选择。这些标准的加权版本的有用性证明通过模拟研究和应用程序的三个数据集。
Local likelihood estimation has proven to be an effective method for obtaining estimates of parameters that vary with a covariate. To obtain useful estimates of such parameters, approximating models are used. In such cases it is useful to consider window based estimates. We may need to choose between competing approximating models. In this article, we propose a modification to the methods used to motivate many information and posterior probability criteria for the weighted likelihood case. We derive weighted versions for two of the most widely known criteria, namely the AIC and BIC. Via a simple modification, the criteria are also made useful for window span selection. The usefulness of the weighted version of these criteria is demonstrated through a simulation study and an application to three datasets.