ABSTRACT

We introduce a new variable selection method, called SgenoLasso, that handles extreme data. Our method relies on the construction of a specific statistical test, a transformation of the data and by the knowledge of the correlation between regressors. It is appropriate in genomics since once the genetic map has been built, the correlation is perfectly known. This new technique is inspired by stochastic processes arising from statistical genetics. We prove that the signal to noise ratio is largely increased by considering the extremes. Our approach and existing methods are compared on simulated and real data, and the results point to the validity of our approach.

摘要

我们介绍了一种新的变量选择方法，称为SgenoLasso，它可以处理极端数据。我们的方法依赖于特定统计检验的构建，数据的转换以及对回归变量之间相关性的了解。由于一旦建立了遗传图谱，相关性就众所周知了，因此在基因组学中是合适的。这项新技术的灵感来自统计遗传学的随机过程。通过考虑极端因素，我们证明了信噪比已大大提高。在模拟和真实数据上比较了我们的方法和现有方法，结果表明了我们方法的有效性。