Contents of Volume 109

View front and back matter from the print issue

Editorial

Taras Bodnar and Ostap Okhrin.
Theor. Probability and Math. Statist. 109 (2023), 1-2
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Distributional hyperspace-convergence of Argmin-sets in convex $M$-estimation

Dietmar Ferger.
Theor. Probability and Math. Statist. 109 (2023), 3-35
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A note on the prediction error of principal component regression in high dimensions

Laura Hucker and Martin Wahl.
Theor. Probability and Math. Statist. 109 (2023), 37-53
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Matrix variate generalized asymmetric Laplace distributions

Tomasz J. Kozubowski, Stepan Mazur and Krzysztof Podgórski.
Theor. Probability and Math. Statist. 109 (2023), 55-80
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Graphical posterior predictive classification: Bayesian model averaging with particle Gibbs

Tatjana Pavlenko and Felix L. Rios.
Theor. Probability and Math. Statist. 109 (2023), 81-99
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Reverse stress testing in skew-elliptical models

Jonathan von Schroeder, Thorsten Dickhaus and Taras Bodnar.
Theor. Probability and Math. Statist. 109 (2023), 101-127
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Test for mean matrix in GMANOVA model under heteroscedasticity and non-normality for high-dimensional data

Takayuki Yamada, Tetsuto Himeno, Annika Tillander and Tatjana Pavlenko.
Theor. Probability and Math. Statist. 109 (2023), 129-158
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Stochastic differential equations with discontinuous diffusion coefficients

Soledad Torres and Lauri Viitasaari.
Theor. Probability and Math. Statist. 109 (2023), 159-175
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