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This Special Issue, titled "Mathematical theory of statistical inference, stochastic processes, and their applications" aims to disseminate high-quality research articles that address the domain of statistical inference applied to discrete or continuous time stochastic processes. The focus of this issue encompasses a diverse range of topics, including diffusion-type processes, point processes, random fields, Markov processes, and various models pertaining to time series analysis. Additionally, this call for papers extends its purview to encompass empirical processes and U-processes, statistical learning theory, functional data analysis, strong and weak approximations, and information theory, in conjunction with the aforementioned subjects.

A distinguishing feature of statistical inference for stochastic processes is its capacity to engender fruitful collaborations with other subfields within the realms of mathematics, statistics, and computer science. This interdisciplinary interplay has facilitated the development of robust tools and novel perspectives. Consequently, the submission of contributions proposing potential applications of the evolving theory in engineering and related domains is strongly encouraged. Notably, this Special Issue places a significant emphasis on advancing methodology and fostering theoretical outcomes, reflecting a dedication to pushing the boundaries of knowledge in the field.

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