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Building Statistical Models in Python

You're reading from   Building Statistical Models in Python Develop useful models for regression, classification, time series, and survival analysis

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Product type Paperback
Published in Aug 2023
Publisher Packt
ISBN-13 9781804614280
Length 420 pages
Edition 1st Edition
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Concepts
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Authors (3):
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Huy Hoang Nguyen Huy Hoang Nguyen
Author Profile Icon Huy Hoang Nguyen
Huy Hoang Nguyen
Paul N Adams Paul N Adams
Author Profile Icon Paul N Adams
Paul N Adams
Stuart J Miller Stuart J Miller
Author Profile Icon Stuart J Miller
Stuart J Miller
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Toc

Table of Contents (22) Chapters Close

Preface 1. Part 1:Introduction to Statistics
2. Chapter 1: Sampling and Generalization FREE CHAPTER 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

Survival Models

In Chapter 13, Time-to-Event Variables, we introduced the topics of survival analysis, censoring, and time-to-event (TTE) variables. In this chapter, we will provide an in-depth overview and walkthrough of the implementation of these techniques with respect to three primary model frameworks:

  • Kaplan-Meier model
  • Exponential model
  • Cox Proportional Hazards model

We will discuss how each approach provides probabilistic insight into the survival and hazard risk of study subjects using univariate Kaplan-Meier and exponential approaches as well as the multivariate Cox Proportional Hazards regression model. We’ll walk through examples using real data and discuss the results so that the reader understands how to assess performance and translate test output into useful information. Finally, we will show how to use the trained models to provide forecast probabilities for unseen data.

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