C. Mitchell Dayton

3.0k total citations
66 papers, 2.2k citations indexed

About

C. Mitchell Dayton is a scholar working on Statistics and Probability, Management Science and Operations Research and Artificial Intelligence. According to data from OpenAlex, C. Mitchell Dayton has authored 66 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Statistics and Probability, 11 papers in Management Science and Operations Research and 9 papers in Artificial Intelligence. Recurrent topics in C. Mitchell Dayton's work include Advanced Statistical Methods and Models (20 papers), Statistical Methods and Bayesian Inference (13 papers) and Statistical Methods in Clinical Trials (7 papers). C. Mitchell Dayton is often cited by papers focused on Advanced Statistical Methods and Models (20 papers), Statistical Methods and Bayesian Inference (13 papers) and Statistical Methods in Clinical Trials (7 papers). C. Mitchell Dayton collaborates with scholars based in United States, Egypt and Slovakia. C. Mitchell Dayton's co-authors include George B. Macready, Ting Hsiang Lin, N. J. Scheers, Helen C. Gift, Kathryn A. Atchison, Olivia N. Saracho, William D. Schafer, Blossom H. Patterson, Barry I. Graubard and James S. Kemp and has published in prestigious journals such as Journal of the American Statistical Association, PLoS ONE and Cancer.

In The Last Decade

C. Mitchell Dayton

60 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
C. Mitchell Dayton United States 22 657 389 388 237 225 66 2.2k
Rolf Langeheine Germany 15 386 0.6× 264 0.7× 211 0.5× 182 0.8× 120 0.5× 37 1.4k
Yutaka Kano Japan 18 567 0.9× 363 0.9× 224 0.6× 134 0.6× 63 0.3× 54 2.1k
Ivo W. Molenaar Netherlands 20 599 0.9× 1.0k 2.7× 179 0.5× 194 0.8× 195 0.9× 40 2.7k
Anne Boomsma Netherlands 20 537 0.8× 643 1.7× 143 0.4× 270 1.1× 170 0.8× 42 2.9k
Jürgen Rost Germany 19 430 0.7× 569 1.5× 149 0.4× 618 2.6× 286 1.3× 48 2.1k
Irini Moustaki United Kingdom 23 877 1.3× 562 1.4× 443 1.1× 205 0.9× 83 0.4× 67 2.9k
Maurice M. Tatsuoka United States 18 334 0.5× 381 1.0× 261 0.7× 389 1.6× 331 1.5× 48 3.2k
Kathleen M. Sheehan United States 22 248 0.4× 472 1.2× 398 1.0× 419 1.8× 342 1.5× 78 1.6k
Frank Rijmen United States 23 330 0.5× 461 1.2× 161 0.4× 133 0.6× 94 0.4× 67 1.9k
Fumiko Samejima United States 15 482 0.7× 1.1k 2.9× 127 0.3× 253 1.1× 198 0.9× 29 2.3k

Countries citing papers authored by C. Mitchell Dayton

Since Specialization
Citations

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

Fields of papers citing papers by C. Mitchell Dayton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by C. Mitchell Dayton. 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 C. Mitchell Dayton. The network helps show where C. Mitchell Dayton may publish in the future.

Co-authorship network of co-authors of C. Mitchell Dayton

This figure shows the co-authorship network connecting the top 25 collaborators of C. Mitchell Dayton. A scholar is included among the top collaborators of C. Mitchell Dayton 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 C. Mitchell Dayton. C. Mitchell Dayton 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.
Scheers, N. J., et al.. (2019). Reports of Injury Risks and Reasons for Choice of Sleep Environments for Infants and Toddlers. Maternal and Child Health Journal. 23(12). 1613–1620.
2.
Dayton, C. Mitchell. (2017). A reinterpretation and extension of McNemar’s test. Journal of Modern Applied Statistical Methods. 16(1). 20–33. 3 indexed citations
3.
Dayton, C. Mitchell, et al.. (2011). Factors Influencing the Mixture Index of Model Fit in Contingency Tables Showing Independence. Journal of Modern Applied Statistical Methods. 10(1). 176–191. 2 indexed citations
4.
Chen, Jinsong, et al.. (2010). Assessing Classification Bias in Latent Class Analysis: Comparing Resubstitution and Leave-One-Out Methods. Journal of Modern Applied Statistical Methods. 9(1). 52–63.
5.
Dayton, C. Mitchell, et al.. (2010). <em>JMASM30</em> PI-LCA: A SAS Program Computing the Two-point Mixture Index of Fit for Two-class LCA Models with Dichotomous Variables (SAS). Journal of Modern Applied Statistical Methods. 9(1). 314–331. 1 indexed citations
6.
Dayton, C. Mitchell, et al.. (2005). Sample Size Selection for Pair-Wise Comparisons Using Information Criteria. Journal of Modern Applied Statistical Methods. 4(2). 601–608. 2 indexed citations
7.
Dayton, C. Mitchell. (2003). Model Comparisons Using Information Measures. Journal of Modern Applied Statistical Methods. 2(2). 281–292. 46 indexed citations
8.
Gagné, Phill & C. Mitchell Dayton. (2002). Best Regression Model Using Information Criteria. Journal of Modern Applied Statistical Methods. 1(2). 479–488. 39 indexed citations
9.
Ely, Scott, Amy Chadburn, C. Mitchell Dayton, Ethel Cesarman, & Daniel M. Knowles. (2000). Telomerase activity in B-cell non-Hodgkin lymphoma. Cancer. 89(2). 445–452. 26 indexed citations
10.
Scheers, N. J., C. Mitchell Dayton, & James S. Kemp. (1998). Sudden Infant Death With External Airways Covered. Archives of Pediatrics and Adolescent Medicine. 152(6). 540–7. 64 indexed citations
11.
Gift, Helen C., Kathryn A. Atchison, & C. Mitchell Dayton. (1997). Conceptualizing oral health and oral health-related quality of life. Social Science & Medicine. 44(5). 601–608. 126 indexed citations
12.
Macready, George B. & C. Mitchell Dayton. (1992). The Application of Latent Class Models in Adaptive Testing. Psychometrika. 57(1). 71–88. 11 indexed citations
13.
Dayton, C. Mitchell. (1991). Educational Applications of Latent Class Analysis.. Measurement and Evaluation in Counseling and Development. 24(3). 5 indexed citations
14.
Dayton, C. Mitchell & George B. Macready. (1988). Concomitant-Variable Latent-Class Models. Journal of the American Statistical Association. 83(401). 173–173. 60 indexed citations
15.
Scheers, N. J. & C. Mitchell Dayton. (1986). RRCOV: Computer Program for Covariate Randomized Response Models. The American Statistician. 40(3). 229–229. 2 indexed citations
16.
Dayton, C. Mitchell & George B. Macready. (1983). Latent structure analysis of repeated classifications with dichotomous data. British Journal of Mathematical and Statistical Psychology. 36(2). 189–201. 8 indexed citations
17.
Eliot, John, et al.. (1978). Further Study of the X-Linked Recessive Gene Hypothesis for Inheritance of Spatial Abilities. Perceptual and Motor Skills. 47(3_suppl). 1023–1029. 6 indexed citations
18.
Eliot, John & C. Mitchell Dayton. (1976). Factors Affecting Accuracy of Perception on a Task Requiring the Ability to Identify Viewpoints. The Journal of Genetic Psychology. 128(2). 201–214. 7 indexed citations
19.
Dayton, C. Mitchell, et al.. (1968). THE PREDICTION OF HIGH SCHOOL ACADEMIC PERFORMANCE USING VOCATIONAL INVENTORY SCORES1. Journal of Educational Measurement. 5(2). 129–139.
20.
Dayton, C. Mitchell, et al.. (1966). RELATIONSHIP BETWEEN HOLLAND VOCATIONAL INVENTORY SCORES AND PERFORMANCE MEASURES OF HIGH SCHOOL STUDENTS.. PLoS ONE. 9(1). e83197–e83197.

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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