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From Decoupling and Self-Normalization to Machine Learning

Introduction We now live in the age of the digital revolution, where machine learning and artificial intelligence have transformed the way data is generated, processed, and analyzed to solve complex research problems. One of the goals in the development of algorithms in machine learning is to extract as much information from the data as possible that cannot be achieved through traditional techniques. Decoupling and self-normalization are statistical tools that handle complex problems that are beyond the spheres of classical statistical methods. Self-normalization can be traced back to the seminal work of Gosset [9], which is considered a breakthrough in science. Notably, his Student tstatistic allowed statistical inference about the value of the mean of a (Gaussian) distribution without knowledge of the actual value of the variance, provided one has a random sample from the target population. Remarkably, the self-normalization approach provides techniques to extend the t-statistic to the case of non-Gaussian distributions and nonindependent variables.