Statistical Inference for Stochastic Processes

396 papers and 3.7k indexed citations i.

About

The 396 papers published in Statistical Inference for Stochastic Processes in the last decades have received a total of 3.7k indexed citations. Papers published in Statistical Inference for Stochastic Processes usually cover Finance (245 papers), Statistics and Probability (198 papers) and Artificial Intelligence (70 papers) specifically the topics of Stochastic processes and financial applications (172 papers), Financial Risk and Volatility Modeling (165 papers) and Statistical Methods and Inference (155 papers). The most active scholars publishing in Statistical Inference for Stochastic Processes are Nakahiro Yoshida, Marina Kleptsyna, Jean‐François Coeurjolly, Alain Breton, Masayuki Uchida, Yasutaka Shimizu, Yury A. Kutoyants, Alexander G. Tartakovsky, David M. Mason and Igor Cialenco.

In The Last Decade

Fields of papers published in Statistical Inference for Stochastic Processes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Statistical Inference for Stochastic Processes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Statistical Inference for Stochastic Processes.

Countries where authors publish in Statistical Inference for Stochastic Processes

Since Specialization
Citations

This map shows the geographic impact of research published in Statistical Inference for Stochastic Processes. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Statistical Inference for Stochastic Processes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Statistical Inference for Stochastic Processes more than expected).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

Explore journals with similar magnitude of impact

Rankless by CCL
2025