This book on asymptotic and methodological statistics celebrates the distinguished career of Marie Huškov� and her foundational work in modern mathematical statistics. It brings together original research contributions from renowned statisticians, focusing on asymptotic theory, methodological innovations, and applications in statistical inference. The volume highlights cutting-edge developments in change-point analysis, goodness-of-fit testing and nonparametric statistics, reflecting the extensive impact Marie Huškov� has had in shaping the direction of contemporary statistical science. It serves both as a tribute to her career and as a valuable resource for researchers and PhD students.

About the Author

Daniel Hlubinka is an Associate Professor at the Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His research interests include stochastic processes and functional data.

Š�rka Hudecov� is an Associate Professor at the Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. Her research interests include time series analysis and multivariate statistics.

Mat�š Maciak is an Associate Professor at the Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His research interests include change point analysis and non-parametric statistics.

Michal Pešta is an Associate Professor at the Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, Czech Republic. His research interests include resampling methods and statistical inference.

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