5) Publications

Methodology Papers of PRIMsrc

(open access, by type and chronological order)

1. Dazard J-E. and Rao J.S. Variable Selection Strategies for High-Dimensional Recursive Peeling-Based Survival Bump Hunting Models. [2021a (in prep)].

2. Dazard J-E. and Rao J.S. Group Bump Hunting by Recursive Peeling-Based Methods: Application to Survival/Risk Predictive Models. [2021b (in prep)].

3. Dazard J-E., Choe M., Leblanc M., Rao J.S. Cross-validation and Peeling Strategies for Survival Bump Hunting Survival using Recursive Peeling Methods. Statistical Analysis and Data Mining (2016), 9(1):12-42. The American Statistical Association (ASA) Affiliated Data Science Journal (doi:10.1002/sam.11301). Also available at: The Cornell University Library Archives arXiv v1:v8 (2015-01-16: 2015-11-20).

4. Dazard J-E., Choe M., LeBlanc M., Rao J.S. R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification. Joint Statistical Meetings, Section for Statistical Programmers and Analysts, Seattle, WA. USA. 2015. JSM Proceedings, American Statistical Association (2015), 650-664.

5. Dazard J-E., Choe M., LeBlanc M., Rao J.S. Cross-Validated Survival Bump Hunting using Recursive Peeling Methods. Joint Statistical Meetings, Section for survival methods for risk estimation/prediction, Boston, MA. USA. 2014. JSM Proceedings, American Statistical Association (2014), 3366-3380.

Application Papers using PRIMsrc

1. Dazard J-E., Rao J.S., Markowitz S. Local sparse bump hunting reveals molecular heterogeneity of colon tumors. Statistics in medicine (2012), 31(11-12):1203-1220.

2. Rao J.S., Huilin Y., Dazard J-E. Disparity Subtyping: Bringing Precision Medicine Closer to Disparity Science Cancer Epidemiology Biomarkers & Prevention (2020), 29(6 Suppl):C018.

3. Dazard J-E., Choe M, Pawitan Y., Rao J.S. Identification and Characterization of Informative Prognostic Subgroups by Survival Bump Hunting. [2021c (in prep)].

Related Methodology Papers and Seminal Papers

1. Liu T., Diaz-Pachon D.A., Rao J.S., Dazard J-E. Mode Hunting Using Pettiest Components Analysis. The Cornell University Library Archives arXiv v1 (2021-01-12).

2. Diaz-Pachon D.A., Saenz J.P., Rao J.S., Dazard J-E. Mode Hunting Through Active Information. Applied Stochastic Models in Business and Industry (2019), 35(2):376-393.

3. Diaz-Pachon D.A., Dazard J-E., Rao J.S. Unsupervised Bump Hunting Using Principal Components. In: Ahmed SE, editor. Big and Complex Data Analysis: Methodologies and Applications. Contributions to Statistics, vol. Edited Refereed Volume. Springer International Publishing, Cham Switzerland (2017), 325-345.

4. Diaz-Pachon D.A., Rao J.S., Dazard J-E. On the Explanatory Power of Principal Components. The Cornell University Library Archives arXiv v1 (2014-04-19).

5. Diaz-Pachon D.A., Rao J.S., Dazard J-E. Optimization of the Patient Rule Induction Method (PRIM) under normality. Complex Data Modeling and Computationally Intensive Statistical Methods for Estimation and Prediction; 2013; Milan, Italy: S.Co. Proceedings (2013).

6. Dazard J-E. and Rao J.S. Local Sparse Bump Hunting. J. Comp. Graph. Statistics (2010), 19(4):900-92.

7. Polonik W. and Wang Z. Prim Analysis. J. Multivariate Analysis (2010), 101(3):525–540.

8. Burman P. and Polonik W. Multivariate mode hunting: data analytic tools with measures of significance. J. Multivariate Analysis (2009), 100:1198–1218.

9. Wu L. and Chipman H. Bayesian Model-Assisted PRIM Algorithm. Departments of Statistics and Actuarial Science, University of Waterloo, Technical Report, (2003).

10. Friedman J. and Fisher N. Bump hunting in high-dimensional data. Stat. Computing (1999), 9:123-143.