Articles | Open Access | https://doi.org/10.37547/history-crjh-05-01-04

BEYOND BOUNDARIES: FORTIFYING DATA SECURITY WITH CUTTING-EDGE CENSORED DATA MODELING AND ANTI-REGRESSION ADVANCEMENTS

Annisa Nugraha , Department of Mathematics, Faculty of Mathematics and Natural Sciences, Andalas University, Indonesia

Abstract

In an era where data security is paramount, this study introduces a groundbreaking approach to fortify information integrity through advanced techniques in censored data modeling and anti-regression innovation. We delve into the intricacies of safeguarding sensitive insights, pushing the boundaries of conventional methodologies. The framework presented in this research not only enhances predictive accuracy but also ensures robust protection against potential threats, thus redefining the landscape of data security.

Keywords

Data Security, Censored Data Modeling, Anti-Regression

References

ANDERSEN, P. K., BORGAN, Ø., GILL, R. D., & KEIDING, N. (1993). STATISTICAL MODELS BASED ON COUNTING PROCESSES. SPRINGER.

CHEN, M. H., & IBRAHIM, J. G. (1999). BAYESIAN SURVIVAL ANALYSIS. JOHN WILEY & SONS.

KLEIN, J. P., & MOESCHBERGER, M. L. (2003). SURVIVAL ANALYSIS: TECHNIQUES FOR CENSORED AND TRUNCATED DATA. SPRINGER SCIENCE & BUSINESS MEDIA.

LAWLESS, J. F. (2003). STATISTICAL MODELS AND METHODS FOR LIFETIME DATA. JOHN WILEY & SONS.

LEE, E. T., & WANG, J. W. (2003). STATISTICAL METHODS FOR SURVIVAL DATA ANALYSIS. JOHN WILEY & SONS.

NELSON, W. (1995). ACCELERATED LIFE TESTING: STEP-STRESS MODELS AND DATA ANALYSIS. JOHN WILEY & SONS.

PAN, W. (2002). AKAIKE'S INFORMATION CRITERION IN GENERALIZED ESTIMATING EQUATIONS. BIOMETRICS, 58(1), 200-204.

THERNEAU, T. M., & GRAMBSCH, P. M. (2000). MODELING SURVIVAL DATA: EXTENDING THE COX MODEL. SPRINGER SCIENCE & BUSINESS MEDIA.

Article Statistics

Downloads

Download data is not yet available.

Copyright License

Download Citations

How to Cite

Annisa Nugraha. (2024). BEYOND BOUNDARIES: FORTIFYING DATA SECURITY WITH CUTTING-EDGE CENSORED DATA MODELING AND ANTI-REGRESSION ADVANCEMENTS. Current Research Journal of History, 5(01), 15–18. https://doi.org/10.37547/history-crjh-05-01-04