5 Statistical Methods That Dominate Their Industries (and Why)
Discover which statistical methods dominate healthcare, finance, tech, retail, and sports analytics, and why these industry-method pairings became indispensable standards.
survival-analysis
High-Dimensional Survival Analysis: Techniques for Big Data in Time-to-Event Modeling
In this article, we'll move past the textbook Cox model and look at how regularized models, tree-based methods, and deep learning can help survival analysis scale to meet real-world complexity.
Survival Analysis: Techniques for Analyzing Time-to-Event Data
Survival analysis, primarily used for modeling time-to-event data, provides an arsenal of statistical tools to estimate the expected time until one or more events of interest, such as death, failure, or attrition.
Concise Guide to Survival Analysis
So, if you’ve ever asked, “How long until X happens?” and wanted to back that up with solid data, you’re in the right place.
Learning curve: The Kaplan-Meier estimator | The Actuary
The Kaplan-Meier survival curve estimator is one of the most cited ideas in science
Olivier Grisel - Predictive survival analysis with scikit-learn, scikit-survival and lifelines
A Practical Guide to Survival Analysis
Survival analysis consists of statistical methods that help us understand and predict how long it takes for an event to occur.
Better A/B testing with survival analysis
Pic by author - using DALL-E 3 When running experiments don’t forget to bring your survival kit I’ve already made the case in several blog posts (part 1, part 2, part 3) that using survival analysis can improve churn prediction. In this blog post I’ll ...
Survival Analysis with R
With roots dating back to at least 1662 when John Graunt, a London merchant, published an extensive set of inferences based on mortality records, survival analysis is one of the oldest subfields of Statistics [1]. Basic life-table methods, including techniques for dealing with…
The Complete Introduction to Survival Analysis in Python | by Marco Peixeir
Understand survival analysis, its use in the industry, and how to apply it in Python
What Is Survival Analysis? Examples by Hand and in R
Learn more about survival analysis (also called time-to-event analysis) and how it is used, and how to apply it by hand and in R
Survival Analysis: Optimize the Partial Likelihood of the Cox Model
Finding the coefficients that maximize the log-partial likelihood in Python
Survival Analysis in R (in under 10-minutes)
Making a survival analysis can be a challenge even for experienced R users, but the good news is I’ll help you make beautiful, publication-quality survival plots in under 10-minutes. Here’s what WE are going to do: Make your first survival model an...
How to Evaluate Survival Analysis Models
Introduction to the most popular performance evaluation metrics for survival analysis along with practical Python examples
Survival Analysis: A Brief Introduction
An initial look into the method best suited for examining time-to-event data
Survival Analysis in Python: A Quick Guide to The Weibull Analysis
A Quick Guide to The Weibull Analysis
Introduction to Survival Analysis
In Cows using R
Hands-on Survival Analysis with Python
What companies can learn from employee turnover data
A Complete Guide To Survival Analysis In Python, part 3 - KDnuggets
Concluding this three-part series covering a step-by-step review of statistical survival analysis, we look at a detailed example implementing the Kaplan-Meier fitter based on different groups, a Log-Rank test, and Cox Regression, all with examples and shared code.
Introduction to Survival Analysis: the Kaplan-Meier estimator
Learn one of the most popular techniques used for survival analysis and how to implement it in Python!
Practical Survival Analysis — Concepts, techniques, models
With worked out examples using Python and Lifelines