Dean M. Young

1.2k total citations
104 papers, 881 citations indexed

About

Dean M. Young is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Dean M. Young has authored 104 papers receiving a total of 881 indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Statistics and Probability, 22 papers in Artificial Intelligence and 14 papers in Management Science and Operations Research. Recurrent topics in Dean M. Young's work include Advanced Statistical Methods and Models (41 papers), Statistical Methods and Bayesian Inference (39 papers) and Statistical Methods and Inference (29 papers). Dean M. Young is often cited by papers focused on Advanced Statistical Methods and Models (41 papers), Statistical Methods and Bayesian Inference (39 papers) and Statistical Methods and Inference (29 papers). Dean M. Young collaborates with scholars based in United States, Belgium and Australia. Dean M. Young's co-authors include James D. Stamey, Daniel F. Jennings, John W. Seaman, Robert A. MacCready, Hugo W. Moser, Mary L. Efron, Patrick L. Odell, Šarūnas Raudys, Linda W. Jennings and J. D. Tubbs and has published in prestigious journals such as New England Journal of Medicine, SHILAP Revista de lepidopterología and Entrepreneurship Theory and Practice.

In The Last Decade

Dean M. Young

92 papers receiving 755 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dean M. Young United States 15 307 121 120 113 80 104 881
Esko Valkeila Finland 14 120 0.4× 7 0.1× 54 0.5× 43 0.4× 25 0.3× 42 1.2k
William D. Sudderth United States 23 448 1.5× 8 0.1× 433 3.6× 178 1.6× 8 0.1× 109 1.8k
Abraham Wald United States 14 125 0.4× 3 0.0× 117 1.0× 82 0.7× 25 0.3× 39 868
J. H. B. Kemperman United States 18 273 0.9× 7 0.1× 127 1.1× 161 1.4× 68 0.8× 70 1.1k
Shaul K. Bar‐Lev Israel 14 351 1.1× 2 0.0× 159 1.3× 13 0.1× 9 0.1× 92 728
Lan Wang United States 19 1.0k 3.3× 3 0.0× 337 2.8× 19 0.2× 31 0.4× 60 1.5k
Helmut Finner Germany 17 583 1.9× 5 0.0× 178 1.5× 44 0.4× 6 0.1× 52 1.1k
Yi‐Ching Yao United States 12 622 2.0× 3 0.0× 216 1.8× 54 0.5× 10 0.1× 76 1.3k
Jeffrey J. Hunter New Zealand 16 214 0.7× 75 0.6× 241 2.1× 22 0.3× 56 979
Joshua Chover United States 11 231 0.8× 3 0.0× 89 0.7× 114 1.0× 33 0.4× 27 1.1k

Countries citing papers authored by Dean M. Young

Since Specialization
Citations

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

Fields of papers citing papers by Dean M. Young

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dean M. Young. 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 Dean M. Young. The network helps show where Dean M. Young may publish in the future.

Co-authorship network of co-authors of Dean M. Young

This figure shows the co-authorship network connecting the top 25 collaborators of Dean M. Young. A scholar is included among the top collaborators of Dean M. Young 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 Dean M. Young. Dean M. Young 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.
Rahardja, Dewi & Dean M. Young. (2021). Confidence Intervals for the Risk Ratio Using Double Sampling with Misclassified Binomial Data. Journal of Data Science. 9(4). 529–548. 1 indexed citations
2.
Young, Dean M., et al.. (2020). Two Explicit Characterizations of the General Nonnegative-Definite Covariance Matrix Structure for Equality of BLUEs, WLSEs, and LSEs. International Journal of Statistics and Probability. 9(6). 108–108. 1 indexed citations
3.
Young, Dean M., et al.. (2017). Estimation-Equivalent and Dispersion-Equivalent Error Covariance Matrices for the General Linear Model. American Journal of Mathematical and Management Sciences. 37(1). 51–55. 1 indexed citations
4.
Young, Dean M., et al.. (2010). Interval estimation for misclassification rate parameters in a complementary Poisson model. Journal of Statistical Computation and Simulation. 81(9). 1145–1156. 1 indexed citations
5.
Young, Dean M., et al.. (2008). Likelihood-Based Confidence Intervals for Complementary Poisson Rate Parameters with Misclassified Data. Communication in Statistics- Theory and Methods. 38(2). 159–172. 2 indexed citations
6.
Stamey, James D., Dean M. Young, & John W. Seaman. (2008). Bayesian estimation of a standardized mortality ratio with missing death certificates. 42(1). 47–64. 1 indexed citations
7.
Stamey, James D., et al.. (2007). Bayesian Subset Selection of Binomial Parameters Using Possibly Misclassified Data. Journal of Modern Applied Statistical Methods. 6(2). 551–560. 2 indexed citations
8.
Stamey, James D., John W. Seaman, & Dean M. Young. (2006). Bayesian Inference for a Correlated 2 × 2 Table with a Structural Zero. Biometrical Journal. 48(2). 233–244. 3 indexed citations
9.
Young, Dean M., et al.. (2005). Estimation-Equivalent Covariance Structures for the Least Squares and Minque Estimators of the Linear Model Variance. Communication in Statistics- Theory and Methods. 34(3). 625–629.
10.
Stamey, James D., et al.. (2005). Maximum Likelihood Estimation of Two Inversely Related Poisson Rate Parameters with Misclassified Data. American Journal of Mathematical and Management Sciences. 25(1-2). 65–81. 3 indexed citations
11.
Stamey, James D., et al.. (2004). A note on tests for interaction in quantal response data. Journal of Statistical Computation and Simulation. 74(9). 683–690. 1 indexed citations
12.
Raudys, Šarūnas & Dean M. Young. (2003). Results in statistical discriminant analysis: a review of the former Soviet Union literature. Journal of Multivariate Analysis. 89(1). 1–35. 35 indexed citations
13.
Young, Dean M., et al.. (2001). A comparison of parametric conditional error-rate estimators for the two-group linear discriminant function. Journal of Statistical Computation and Simulation. 69(3). 277–291. 1 indexed citations
14.
Young, Dean M., et al.. (1999). Independence Distribution Preserving Covariance Structures for the Multivariate Linear Model. Journal of Multivariate Analysis. 68(2). 165–175. 14 indexed citations
15.
Boullion, T. L., John W. Seaman, & Dean M. Young. (1992). Moments of Discrete Probability Distributions Derived Using a Differential Operator. The American Statistician. 46(1). 22–24.
16.
Young, Dean M., et al.. (1992). A characterization of the independence - distribution - preserving covariance structure for the multivariate maximum squared - radii statistic. Communication in Statistics- Theory and Methods. 21(6). 1605–1613. 3 indexed citations
17.
Young, Dean M., et al.. (1988). Probability inequalities for continuous unimodal random variables with finite support. Communication in Statistics- Theory and Methods. 17(10). 3505–3519. 1 indexed citations
18.
Young, Dean M., et al.. (1987). A note on the effect of simple equicorrelation in detecting a spurious multivariate observation. Communication in Statistics- Theory and Methods. 16(4). 1027–1036. 5 indexed citations
19.
Young, Dean M., et al.. (1987). Comparisons of several graphical methods for representing multivariate data. Computers & Mathematics with Applications. 13(7). 647–655. 4 indexed citations
20.
Young, Dean M., et al.. (1986). A note on the rubustness of the lilliefors test for univariate normality with respect to equicorrelated data. Communication in Statistics- Theory and Methods. 15(8). 2355–2361. 4 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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