Joseph B. Lang

1.4k total citations
38 papers, 967 citations indexed

About

Joseph B. Lang is a scholar working on Statistics and Probability, Sociology and Political Science and Artificial Intelligence. According to data from OpenAlex, Joseph B. Lang has authored 38 papers receiving a total of 967 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Statistics and Probability, 8 papers in Sociology and Political Science and 6 papers in Artificial Intelligence. Recurrent topics in Joseph B. Lang's work include Statistical Methods and Bayesian Inference (22 papers), Advanced Statistical Methods and Models (9 papers) and Statistical Methods in Clinical Trials (8 papers). Joseph B. Lang is often cited by papers focused on Statistical Methods and Bayesian Inference (22 papers), Advanced Statistical Methods and Models (9 papers) and Statistical Methods in Clinical Trials (8 papers). Joseph B. Lang collaborates with scholars based in United States, Italy and Brazil. Joseph B. Lang's co-authors include Alan Agresti, Karen Heimer, Thomas D. Stucky, Bryan Byrne, Marvin Harris, John W. McDonald, Peter Smith, Scott R. Eliason, Timothy J. Barrett and Maria Iannario and has published in prestigious journals such as Journal of the American Statistical Association, Biometrika and Social Forces.

In The Last Decade

Joseph B. Lang

37 papers receiving 895 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Joseph B. Lang United States 17 505 223 162 115 87 38 967
Katherine K. Wallman United States 8 427 0.8× 84 0.4× 101 0.6× 98 0.9× 51 0.6× 17 882
Robert H. Somers United States 12 160 0.3× 148 0.7× 79 0.5× 103 0.9× 67 0.8× 31 913
Stephen E. Fienberg United States 7 177 0.4× 73 0.3× 125 0.8× 49 0.4× 70 0.8× 8 713
Antonio Forcina Italy 15 338 0.7× 71 0.3× 164 1.0× 90 0.8× 106 1.2× 43 661
Jeroen Pannekoek Netherlands 10 203 0.4× 130 0.6× 228 1.4× 87 0.8× 54 0.6× 25 772
Tue Tjur Denmark 9 178 0.4× 61 0.3× 60 0.4× 123 1.1× 60 0.7× 16 928
James C. Lingoes United States 14 119 0.2× 179 0.8× 83 0.5× 72 0.6× 52 0.6× 25 1.1k
Wenxin Jiang United States 14 314 0.6× 91 0.4× 222 1.4× 27 0.2× 81 0.9× 66 862
Robert S. Barcikowski United States 11 204 0.4× 101 0.5× 22 0.1× 108 0.9× 47 0.5× 47 910
David Andrews United States 9 222 0.4× 69 0.3× 62 0.4× 28 0.2× 68 0.8× 46 699

Countries citing papers authored by Joseph B. Lang

Since Specialization
Citations

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

Fields of papers citing papers by Joseph B. Lang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joseph B. Lang. 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 Joseph B. Lang. The network helps show where Joseph B. Lang may publish in the future.

Co-authorship network of co-authors of Joseph B. Lang

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

All Works

20 of 20 papers shown
1.
Heimer, Karen, et al.. (2022). Race and Men’s Imprisonment in the United States: Religious Conservatism, Political Conservatism and Racial Threat. The British Journal of Criminology. 62(5). 1233–1251. 2 indexed citations
2.
Lang, Joseph B., et al.. (2018). Analysis of medicare spending per beneficiary (MSPB). 2018. 1171–1179. 1 indexed citations
3.
Barrett, Timothy J., et al.. (2016). Non-Destructive Analysis of 14th–19th Century European Handmade Papers. Restaurator International Journal for the Preservation of Library and Archival Material. 37(2). 20 indexed citations
4.
Iannario, Maria & Joseph B. Lang. (2016). Testing conditional independence in sets ofI×Jtables by means of moment and correlation score tests with application to HPV vaccine. Statistics in Medicine. 35(25). 4573–4587. 4 indexed citations
5.
Lang, Joseph B.. (2016). Mean-Minimum Exact Confidence Intervals. The American Statistician. 71(4). 354–368. 3 indexed citations
6.
Lang, Joseph B. & Maria Iannario. (2013). Improved tests of independence in singly-ordered two-way contingency tables. Computational Statistics & Data Analysis. 68. 339–351. 7 indexed citations
7.
Lang, Joseph B.. (2008). Score and profile likelihood confidence intervals for contingency table parameters. Statistics in Medicine. 27(28). 5975–5990. 14 indexed citations
8.
Lang, Joseph B.. (2003). An Introduction to Generalized Linear Models. Journal of the American Statistical Association. 98(464). 1086–1087. 96 indexed citations
9.
Dykstra, Richard L., et al.. (2002). Order restricted inference for hypotheses concerning qualitative dispersion. Journal of Statistical Planning and Inference. 107(1-2). 249–265. 4 indexed citations
10.
Lang, Joseph B. & Thor Aspelund. (2001). Binormal association-marginal models for empirically evaluating and comparing diagnostics. Statistical Modelling. 1(1). 49–64. 1 indexed citations
11.
Lang, Joseph B., John W. McDonald, & Peter Smith. (1999). Association-Marginal Modeling of Multivariate Categorical Responses: A Maximum Likelihood Approach. Journal of the American Statistical Association. 94(448). 1161–1171. 27 indexed citations
12.
Lang, Joseph B., John W. McDonald, & Peter Smith. (1999). Association-Marginal Modeling of Multivariate Categorical Responses: A Maximum Likelihood Approach. Journal of the American Statistical Association. 94(448). 1161–1161. 9 indexed citations
13.
Lang, Joseph B.. (1996). On the Partitioning of Goodness-of-Fit Statistics for Multivariate Categorical Response Models. Journal of the American Statistical Association. 91(435). 1017–1023. 23 indexed citations
14.
Lang, Joseph B.. (1996). On the Comparison of Multinomial and Poisson Log-Linear Models. Journal of the Royal Statistical Society Series B (Statistical Methodology). 58(1). 253–266. 37 indexed citations
15.
Byrne, Bryan, et al.. (1995). What's in a Name? The Consequences of Violating Brazilian Emic Color-Race Categories in Estimates of Social Well-Being. Journal of Anthropological Research. 51(4). 389–397. 18 indexed citations
16.
Harris, Marvin, et al.. (1995). A Reply to Telles. Social Forces. 73(4). 1613–1613. 1 indexed citations
17.
Lang, Joseph B. & Alan Agresti. (1994). Simultaneously Modeling Joint and Marginal Distributions of Multivariate Categorical Responses. Journal of the American Statistical Association. 89(426). 625–625. 36 indexed citations
18.
Lang, Joseph B. & Alan Agresti. (1994). Simultaneously Modeling Joint and Marginal Distributions of Multivariate Categorical Responses. Journal of the American Statistical Association. 89(426). 625–632. 177 indexed citations
19.
Agresti, Alan, Joseph B. Lang, & Cyrus R. Mehta. (1993). Some empirical comparisons of exact, modified exact, and higher-order asymptotic tests of independence for ordered categorical variables. Communications in Statistics - Simulation and Computation. 22(1). 1–18. 3 indexed citations
20.
Harris, Marvin, et al.. (1993). Who are the Whites?: Imposed Census Categories and the Racial Demography of Brazil. Social Forces. 72(2). 451–451. 58 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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