Inference for Non-Gaussian Distributions in Linear Models

Non-Gaussian features in data can be seen either as a curse or a blessing, depending on one's perspective. For those who value the elegance of the Gaussian paradigm and its well-established theoretical results, the disruptive effects of non-Gaussianity can be a source of frustration, challenging long-standing frameworks. Yet for others, these same departures from normality offer opportunities: by accounting for non-Gaussian structures, one can develop more efficient inference procedures and more accurate predictive methods, moving beyond the familiar—if somewhat predictable—Gaussian path. This session explores the world beyond the Gaussian paradigm by presenting new theoretical foundations and innovative inferential methodologies designed to address non-Gaussian data.

Organized by:

Krzysztof Podgórski (Poland)