Diogo Ribeiro·2d agoSurvival Analysis for Data ScientistsPart 4 of 6: Parametric Models, Accelerated Failure Time and the Value of Customers Beyond the Data. Six models fitted to a year of churn…
Diogo Ribeiro·4d agoSurvival Analysis for Data ScientistsPart 3 of 6: The Cox Model, Checking Proportional Hazards and Pricing Customer Segments, How the partial likelihood estimates the effect of…
Diogo Ribeiro·6d agoHow Long New Hires Stay, by Age at Hire: Survival From the Start Date in Four Decades of U.S.Survival curves, restricted mean tenure, censoring bounds and a calibrated career model, built from the NLSY79 job-duration tables…
Anushree Das·6d agoContinuous-Time Next-Best-Product Recommendation: Tabular Baselines, Survival Analysis, and RMTPP…Why predicting what a customer buys is only half the problem, and what happens when you benchmark ElasticNet, LightGBM, Cause-Specific Cox…
Diogo Ribeiro·Oct 26Survival Analysis for Data ScientistsPart 2 of 6: Hazards, the Nelson-Aalen Estimator and the Log-Rank Test. How to read churn month by month, why a hazard ratio can drift when…A response icon1A response icon1
Usha Govindarajulu·Oct 26Statistical issues in survival analysis (Shared frailty # & size clusters)October 21, 2026
Lifeflexi·Oct 26Flexible Parametric Survival Models: Royston-ParmarFlexible parametric survival models, especially the Royston-Parmar model, use restricted cubic splines to model the baseline survival…
Diogo Ribeiro·Oct 19Survival Analysis for Data ScientistsPart 1 of 6: Time to Event, Censoring and the Kaplan-Meier Estimator. Why a customer table that is still being written gives the wrong…
Leandre Sabourin·Oct 14The Cox Proportional Hazards Model: Combining Survival Analysis and Regression in One FrameworkIn my previous article, we learned that the Kaplan–Meier estimator is a useful tool for understanding how survival changes over time. It…A response icon3A response icon3
Erick Kiprotich Yegon·Oct 12When a Null Treatment Looks ProtectiveOct 12, 2026 · @Erick kiprotich YEGON
