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 (Sweden)Invited Speakers:
- Anastassia Baxevani (Cyprus)
Moving random fields constructing and estimating motion in spatio-temporal random fields - Farrukh Javed (Sweden)
A class of continuous-time Gaussian mean-variance mixture state-space models
Contributed Speakers:
- Stefano Bonnini (Italy)
Advances in nonparametric testing for overdispersed count data models - Daniel Klein (Slovakia)
Distribution-free REMLS estimation of the AR(1) covariance structure - Yuli Liang (China)
Testing patterned covariance matrices under quadratic subspace - Malwina Mrowińska (Poland)
Test of the quadratic subspace under elliptically contoured distribution - Jolanta Pielaszkiewicz (Sweden)
Covariance structure approximation under high-dimensionality with the use of improved Kullback–Leibler divergence