Lifetime Data Analysis

818 papers and 13.8k indexed citations
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About

The 818 papers published in Lifetime Data Analysis in the last decades have received a total of 13.8k indexed citations. Papers published in Lifetime Data Analysis usually cover Statistics and Probability (704 papers), Artificial Intelligence (131 papers) and Economics and Econometrics (107 papers) specifically the topics of Statistical Methods and Inference (568 papers), Statistical Methods and Bayesian Inference (395 papers) and Statistical Distribution Estimation and Applications (206 papers). The most active scholars publishing in Lifetime Data Analysis are Philip Hougaard, G. À. Whitmore, W. J. Padgett, Jerry Lawless, Martin Crowder, Daniel Commenges, Per Kragh Andersen, Jianwen Cai, Fred Schenkelberg and Ørnulf Borgan.

In The Last Decade

Lifetime Data Analysis

754 papers receiving 12.8k citations

Fields of papers published in Lifetime Data Analysis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Lifetime Data Analysis. 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 Lifetime Data Analysis.

Countries where authors publish in Lifetime Data Analysis

Since Specialization
Citations

This map shows the geographic impact of research published in Lifetime Data Analysis. 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 Lifetime Data Analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lifetime Data Analysis 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.

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