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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

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...

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