S. Kotz

11.0k total citations · 3 hit papers
8 papers, 7.2k citations indexed

About

S. Kotz is a scholar working on Artificial Intelligence, Statistics and Probability and Management Science and Operations Research. According to data from OpenAlex, S. Kotz has authored 8 papers receiving a total of 7.2k indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 3 papers in Statistics and Probability and 2 papers in Management Science and Operations Research. Recurrent topics in S. Kotz's work include Statistical Distribution Estimation and Applications (3 papers), Bayesian Methods and Mixture Models (3 papers) and Probability and Risk Models (2 papers). S. Kotz is often cited by papers focused on Statistical Distribution Estimation and Applications (3 papers), Bayesian Methods and Mixture Models (3 papers) and Probability and Risk Models (2 papers). S. Kotz collaborates with scholars based in United States, Australia and Türkiye. S. Kotz's co-authors include Norman L. Johnson, N. Balakrishnan, Yasuhiro Omori, S. Kocherlakota, Norman L. Johnson, N. Balakrishnan, Adrienne W. Kemp, Nicholas J. Ashbolt, Lucy Marshall and James A. Baldwin and has published in prestigious journals such as Journal of the American Statistical Association, Biometrics and Journal of Computational and Applied Mathematics.

In The Last Decade

S. Kotz

8 papers receiving 6.7k citations

Hit Papers

align trajectories

What are hit papers?

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.

Continuous Univariate Distributions.

1995 5.9k citations Yasuhiro Omori, Norman L. Johnson et al. Journal of the American Statistical Association profile →
Continuous Univariate Distributions.Vol. 1

1995 625 citations S. Kocherlakota, Norman L. Johnson et al. Biometrics profile →
Univariate Discrete Distributions.

1993 538 citations S. Kocherlakota, Norman L. Johnson et al. Biometrics profile →

Peers

S. Kotz

SP

SPU

AI

MSOR

FINAN

Michael A. Stephens Canada

N. Balakrishnan Canada

Yasuhiro Omori Japan

Ali S. Hadi United States

Barry C. Arnold United States

Murray Rosenblatt United States

Adelchi Azzalini Italy

M. C. Jones United Kingdom

C. Radhakrishna Rao United States

Albert W. Marshall United States

Michael A. Stephens Canada

S. Kotz

2.8k ×0.9

SP

1.0k ×0.6

SPU

1.2k ×1.1

AI

740 ×0.8

MSOR

842 ×1.0

FINAN

Citations per year, relative to S. Kotz S. Kotz (= 1×) peers Michael A. Stephens

Countries citing papers authored by S. Kotz

Since Specialization

Citations

This map shows the geographic impact of S. Kotz'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 S. Kotz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites S. Kotz more than expected).

Fields of papers citing papers by S. Kotz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by S. Kotz. 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 S. Kotz. The network helps show where S. Kotz may publish in the future.

Co-authorship network of co-authors of S. Kotz

This figure shows the co-authorship network connecting the top 25 collaborators of S. Kotz. A scholar is included among the top collaborators of S. Kotz 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 S. Kotz. S. Kotz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Kotz, S., et al.. (2007). Test of independence for generalized Farlie–Gumbel–Morgenstern distributions. Journal of Computational and Applied Mathematics. 212(1). 102–111. 7 indexed citations

2.

Roser, David J., et al.. (2002). Automated event sampling for microbiological and related analytes in remote sites: a comprehensive system. Water Science & Technology Water Supply. 2(3). 123–130. 14 indexed citations

3.

Kocherlakota, S., Norman L. Johnson, S. Kotz, & N. Balakrishnan. (1995). Continuous Univariate Distributions.Vol. 1. Biometrics. 51(4). 1582–1582. 625 indexed citations breakdown →

4.

Omori, Yasuhiro, Norman L. Johnson, S. Kotz, & N. Balakrishnan. (1995). Continuous Univariate Distributions.. Journal of the American Statistical Association. 90(432). 1490–1490. 5918 indexed citations breakdown →

5.

Altham, P. M. E., Norman L. Johnson, S. Kotz, & Adrienne W. Kemp. (1994). Univariate Discrete Distributions.. Journal of the American Statistical Association. 89(426). 718–718. 5 indexed citations

6.

Kocherlakota, S., Norman L. Johnson, S. Kotz, & Adrienne W. Kemp. (1993). Univariate Discrete Distributions.. Biometrics. 49(4). 1282–1282. 538 indexed citations breakdown →

7.

Johnson, Norman L. & S. Kotz. (1977). On some generalized farlie-gumbel-morgenstern distributions-II regression, correlation and further generalizations. Communication in Statistics- Theory and Methods. 6(6). 485–496. 95 indexed citations

8.

Buckland, W. R., Norman L. Johnson, & S. Kotz. (1971). Discrete Distributions.. Journal of the Royal Statistical Society Series D (The Statistician). 20(3). 75–75. 3 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.

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