Statistical Inference for Stochastic Processes

metrics 2024

Innovating insights into stochastic processes.

Introduction

Statistical Inference for Stochastic Processes is a premier academic journal published by SPRINGER, dedicated to advancing the field of statistical methods in stochastic processes. With an ISSN of 1387-0874 and an E-ISSN of 1572-9311, this journal provides a platform for rigorous research and innovative methodologies from 2005 through to 2024. It is positioned in the Q3 category for Statistics and Probability, ranking #194 out of 278 within the Scopus Mathematics domain, reflecting its significance among academic peers despite its relatively junior status in citation metrics. As a resource for researchers, professionals, and students alike, this journal aims to publish high-quality, peer-reviewed articles that contribute to the understanding and application of stochastic processes, making it an essential part of the statistical sciences landscape. While not offering open access, subscribers and institutions will find a wealth of knowledge and insights that are pivotal for both theoretical and practical advancements in statistics.

Metrics 2024

SCIMAGO Journal Rank0.36
Journal Impact Factor0.70
Journal Impact Factor (5 years)0.80
H-Index22
Journal IF Without Self0.70
Eigen Factor0.00
Normal Eigen Factor0.12
Influence0.51
Immediacy Index0.10
Cited Half Life10.10
Citing Half Life17.60
JCI0.42
Total Documents339
WOS Total Citations300
SCIMAGO Total Citations654
SCIMAGO SELF Citations49
Scopus Journal Rank0.36
Cites / Document (2 Years)0.51
Cites / Document (3 Years)0.58
Cites / Document (4 Years)0.69

Metrics History

Rank 2024

Scopus

Statistics and Probability in Mathematics
Rank #194/278
Percentile 30.22
Quartile Q3

IF (Web Of Science)

STATISTICS & PROBABILITY
Rank 126/168
Percentile 25.30
Quartile Q3

JCI (Web Of Science)

STATISTICS & PROBABILITY
Rank 111/168
Percentile 33.93
Quartile Q3

Quartile History

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