Monte Carlo Statistical Methods (Springer Texts in Statistics)

716 indexed citations

Abstract

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This paper, published in 2005, received 716 indexed citations. Written by Christian P. Robert and George Casella covering the research area of . It is primarily cited by scholars working on Artificial Intelligence (336 citations), Statistics and Probability (185 citations) and Statistics, Probability and Uncertainty (76 citations). Published in Springer eBooks.

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Countries where authors are citing Monte Carlo Statistical Methods (Springer Texts in Statistics)

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Citations

This map shows the geographic impact of Monte Carlo Statistical Methods (Springer Texts in Statistics). 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 Monte Carlo Statistical Methods (Springer Texts in Statistics) with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Monte Carlo Statistical Methods (Springer Texts in Statistics) more than expected).

Fields of papers citing Monte Carlo Statistical Methods (Springer Texts in Statistics)

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Monte Carlo Statistical Methods (Springer Texts in Statistics). Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Monte Carlo Statistical Methods (Springer Texts in Statistics).

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.

This paper is also available at doi.org/w8364031.

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