Search icon
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Building Statistical Models in Python

You're reading from  Building Statistical Models in Python

Product type Book
Published in Aug 2023
Publisher Packt
ISBN-13 9781804614280
Pages 420 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
Huy Hoang Nguyen Huy Hoang Nguyen
Profile icon Huy Hoang Nguyen
Paul N Adams Paul N Adams
Profile icon Paul N Adams
Stuart J Miller Stuart J Miller
Profile icon Stuart J Miller
View More author details

Table of Contents (22) Chapters

Preface 1. Part 1:Introduction to Statistics
2. Chapter 1: Sampling and Generalization 3. Chapter 2: Distributions of Data 4. Chapter 3: Hypothesis Testing 5. Chapter 4: Parametric Tests 6. Chapter 5: Non-Parametric Tests 7. Part 2:Regression Models
8. Chapter 6: Simple Linear Regression 9. Chapter 7: Multiple Linear Regression 10. Part 3:Classification Models
11. Chapter 8: Discrete Models 12. Chapter 9: Discriminant Analysis 13. Part 4:Time Series Models
14. Chapter 10: Introduction to Time Series 15. Chapter 11: ARIMA Models 16. Chapter 12: Multivariate Time Series 17. Part 5:Survival Analysis
18. Chapter 13: Time-to-Event Variables – An Introduction 19. Chapter 14: Survival Models 20. Index 21. Other Books You May Enjoy

Cox Proportional Hazards regression model

Survival analysis, also called TTE analysis, as we discussed in Chapter 13, Time-to-Event Variables, is an analytical approach that uses probability to estimate the time remaining before an event occurs based on previous observations. We have seen how this can be helpful when including appropriate covariates in applications such as estimating life expectancy, mechanical failure, and customer churn, which can help with prioritizing needs and to more efficiently allocate resources. As we discussed in depth in Chapter 13, censoring is an aspect making survival analysis unique from other statistical questions that can be solved using techniques such as regression. Consequently—and because dropping an observation due to censoring will almost certainly mislead our model and provide results we cannot trust—we insert what is known as an event status indicator to help account for whether an event will occur or fail to occur prior to estimating...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}