Pseudo-Maximum Likelihood Theory for High-Dimension Rank-One Inference

Explore a 57-minute Fields Institute seminar presentation by Justin Ko from the University of Waterloo, delivered at the Toronto Probability Seminar, focusing on the mathematical foundations and applications of pseudo-maximum likelihood theory in high-dimensional rank-one inference problems. Delve into advanced probability concepts and statistical methodologies that address the challenges of analyzing high-dimensional data structures through a rank-one lens, with emphasis on theoretical frameworks and practical implications in modern statistical analysis.