A Distribution-Free Theory of Nonparametric Regression

966 indexed citations

Abstract

loading...

About

This paper, published in 2002, received 966 indexed citations. Written by László Györfi, Michael Köhler, Adam Krzyżak and Harro Walk covering the research area of Statistics and Probability. It is primarily cited by scholars working on Statistics and Probability (425 citations), Artificial Intelligence (419 citations) and Control and Systems Engineering (158 citations). Published in Springer series in statistics.

In The Last Decade

doi.org/10.1007/b97848 →

Countries where authors are citing A Distribution-Free Theory of Nonparametric Regression

Specialization
Citations

This map shows the geographic impact of A Distribution-Free Theory of Nonparametric Regression. 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 A Distribution-Free Theory of Nonparametric Regression with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A Distribution-Free Theory of Nonparametric Regression more than expected).

Fields of papers citing A Distribution-Free Theory of Nonparametric Regression

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of A Distribution-Free Theory of Nonparametric Regression. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A Distribution-Free Theory of Nonparametric Regression.

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/10.1007/b97848.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026