An Information Theoretic Approach to Model Selection: A Tutorial with Monte Carlo Confirmation
An Information Theoretic Approach to Model Selection: A Tutorial with Monte Carlo Confirmation
复制标题
模型选择的信息论方法：蒙特卡罗确认教程
DOI：
10.1007/s40614-019-00206-1
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发表时间：
2019
影响因子：
2
通讯作者：
M. Newland
中科院分区：
文献类型：
--
作者：
M. Newland
对零假设显著性检验(NHST)的依赖及其结果的曲解被认为是造成复制危机的原因之一，同时阻碍了累积科学的发展。一种解决方案是一种名为信息理论(I-T)模型选择的数据分析方法，它建立在最大似然估计的基础上。在I-T方法中，科学家检查一组候选模型，并为每个模型确定它比集合中的所有其他模型更接近真理的概率。尽管理论的发展是微妙的，但I-T分析的实施是直截了当的。根据收集的数据，模型根据它们是最好的概率进行排序。它鼓励对多种模型进行检查，这是研究人员所希望的，也是NHST不鼓励的。本文旨在解决两个目标。首先是说明I-T数据分析在一个虚拟实验数据中的应用。产生了一个有噪声的延迟折扣数据集，并检验了七个定量模型。在插图中，演示了不必知道“真相”是确定最接近它的那个，并且最有可能的模型符合产生数据的模型。其次，我们使用蒙特卡罗模拟来检验I-T方法的倡导者提出的主张，在这些模拟中，生成并分析了10,000个不同的数据集。模拟表明，1)单个虚拟实验返回的与每个模型相关的概率与模拟产生的概率接近，2)被认为接近真实的模型产生了最精确的参数估计，3)增加一个重复项提高了识别最可能模型的能力。
A reliance on null hypothesis significance testing (NHST) and misinterpretations of its results are thought to contribute to the replication crisis while impeding the development of a cumulative science. One solution is a data-analytic approach called Information-Theoretic (I-T) Model Selection, which builds upon Maximum Likelihood estimates. In the I-T approach, the scientist examines a set of candidate models and determines for each one the probability that it is the closer to the truth than all others in the set. Although the theoretical development is subtle, the implementation of I-T analysis is straightforward. Models are sorted according to the probability that they are the best in light of the data collected. It encourages the examination of multiple models, something investigators desire and that NHST discourages. This article is structured to address two objectives. The first is to illustrate the application of I-T data analysis to data from a virtual experiment. A noisy delay-discounting data set is generated and seven quantitative models are examined. In the illustration, it is demonstrated that it is not necessary to know the “truth” is to identify the one that is closest to it and that the most likely models conform to the model that generated the data. Second, we examine claims made by advocates of the I-T approach using Monte Carlo simulations in which 10,000 different data sets are generated and analyzed. The simulations showed that 1) the probabilities associated with each model returned by the single virtual experiment approximated those that resulted from the simulations, 2) models that were deemed close to the truth produced the most precise parameter estimates, and 3) adding a single replicate sharpens the ability to identify the most probable model.
可变比率和可变间隔计划中响应时间的响应回合分析
影响因子：
1.3
作者：
丹野貴行;Takayuki Tanno
通讯作者：
Takayuki Tanno