Approximate Selective Inference via Maximum Likelihood
Approximate Selective Inference via Maximum Likelihood
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DOI：
10.1080/01621459.2022.2081575
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发表时间：
2019-02
影响因子：
3.7
通讯作者：
Snigdha Panigrahi;Jonathan E. Taylor
中科院分区：
文献类型：
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作者：
Snigdha Panigrahi;Jonathan E. Taylor
摘要最近已经开发了几种策略来确保模型选择后的有效推理;其中一些很容易计算，而另一些则在推理能力方面更好。在这篇文章中，我们考虑了高斯数据的选择性推理框架。我们提出了一种新的推断方法，通过近似极大似然估计。我们的目标是：（a）在随机化的帮助下实现更好的推理能力，（B）从难以以封闭形式评估的精确条件分布中绕过昂贵的MCMC采样。我们构造近似推理，例如p值、置信区间等，通过解决一个相当简单的凸优化问题我们说明了我们的方法的潜力，在模拟信号噪声比的大范围的值。在癌症基因表达数据集上，我们发现我们的方法改进了一些常用的选择性推理策略的推理能力。本文的补充材料可在网上查阅。
Abstract Several strategies have been developed recently to ensure valid inference after model selection; some of these are easy to compute, while others fare better in terms of inferential power. In this article, we consider a selective inference framework for Gaussian data. We propose a new method for inference through approximate maximum likelihood estimation. Our goal is to: (a) achieve better inferential power with the aid of randomization, (b) bypass expensive MCMC sampling from exact conditional distributions that are hard to evaluate in closed forms. We construct approximate inference, for example, p-values, confidence intervals etc., by solving a fairly simple, convex optimization problem. We illustrate the potential of our method across wide-ranging values of signal-to-noise ratio in simulations. On a cancer gene expression dataset we find that our method improves upon the inferential power of some commonly used strategies for selective inference. Supplementary materials for this article are available online.