Likelihood Ratio Test

The likelihood ratio test is a statistical method used to compare the likelihood of two competing hypotheses, often involving model selection or hypothesis testing in various fields. Current research focuses on extending its application to complex scenarios, such as high-dimensional data analysis (e.g., using machine learning classifiers and kernel density estimators) and robust hypothesis testing in the presence of adversarial perturbations (e.g., employing generalized likelihood ratio tests). These advancements are improving the accuracy and reliability of statistical inference across diverse domains, including machine learning, genetics, and physics, enabling more powerful and nuanced analyses of complex data.