Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Estimation of Finite Mixture Distributions Through Bayesian Sampling
1994620 citationsJean Diebolt, Christian P. RobertJournal of the Royal Statistical Society Series B (Statistical Methodology)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Jean Diebolt's research. 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 Jean Diebolt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jean Diebolt more than expected).
This network shows the impact of papers produced by Jean Diebolt. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jean Diebolt. The network helps show where Jean Diebolt may publish in the future.
Co-authorship network of co-authors of Jean Diebolt
This figure shows the co-authorship network connecting the top 25 collaborators of Jean Diebolt.
A scholar is included among the top collaborators of Jean Diebolt based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Jean Diebolt. Jean Diebolt is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Girard, Stéphane & Jean Diebolt. (1998). On the Convergence of the ET Method for Extreme Upper Quantile Estimation. HAL (Le Centre pour la Communication Scientifique Directe).1 indexed citations
Diebolt, Jean & Edward H. Ip. (1996). Stochastic EM: method and application.82 indexed citations
16.
Diebolt, Jean & Christian P. Robert. (1994). Estimation of Finite Mixture Distributions Through Bayesian Sampling. Journal of the Royal Statistical Society Series B (Statistical Methodology). 56(2). 363–375.620 indexed citations breakdown →
Celeux, Gilles & Jean Diebolt. (1989). Une version de type recuit simule de l'algorithme EM. HAL (Le Centre pour la Communication Scientifique Directe). 310(3). 119–124.11 indexed citations
19.
Celeux, Gilles & Jean Diebolt. (1987). The EM and the SEM algorithms for mixtures : statistical and numerical aspects. OpenGrey (Institut de l'Information Scientifique et Technique). 32. 135–151.12 indexed citations
20.
Celeux, Gilles & Jean Diebolt. (1986). L'algorithme SEM: un algorithme d'apprentissage probabiliste: pour la reconnaissance de mélange de densités. French digital mathematics library (Numdam). 34(2). 35–52.34 indexed citations
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.