BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Survival Analysis with Python

Survival Analysis with Python

Survival Analysis with Python
Stock photo: cover may vary

Survival Analysis with Python Paperback - 2024

by Nag, Avishek

Add to wish list
  • Used
  • Good
  • Paperback
Used - Good

Description

paperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
Ask the seller a question Add to wish list
A$60.23
Free Delivery within USA
Standard delivery: 7 to 14 days
More delivery options
Dropship order
Ships from Bonita (California, United States)

Details

  • Title Survival Analysis with Python
  • Author Nag, Avishek
  • Binding Paperback
  • Condition Used - Good
  • Pages 84
  • Volumes 1
  • Language ENG
  • Publisher Auerbach Publications
  • Publication date 2024-10-08
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # 1032073675.G
  • ISBN 9781032073675 / 1032073675
  • Weight 0.3 lbs (0.14 kg)
  • Dimensions 9 x 6 x 0.19 in (22.86 x 15.24 x 0.48 cm)
  • Category Computers - Languages / Programming
  • Library of Congress Catalogue Number 2021947457
  • Quantity available 1

About Bonita California, United States

Biblio member since 2020

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Bonita

Reader reviews for Survival Analysis with Python

From the publisher

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into

  • Parametric models with coverage of
    • Concept of maximum likelihood estimate (MLE) of a probability distribution parameter
    • MLE of the survival function
    • Common probability distributions and their analysis
    • Analysis of exponential distribution as a survival function
    • Analysis of Weibull distribution as a survival function
    • Derivation of Gumbel distribution as a survival function from Weibull

  • Non-parametric models including
    • Kaplan-Meier (KM) estimator, a derivation of expression using MLE
    • Fitting KM estimator with an example dataset, Python code and plotting curves
    • Greenwood's formula and its derivation

  • Models with covariates explaining
    • The concept of time shift and the accelerated failure time (AFT) model
    • Weibull-AFT model and derivation of parameters by MLE
    • Proportional Hazard (PH) model
    • Cox-PH model and Breslow's method
    • Significance of covariates
    • Selection of covariates

The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

About the author

Avishek Nag has a Masters of Technology Degree in data analytics and machine learning from Birla Institute of Technology and Science, Pilani, India. He has more than 15 years of experience in Software Development and Architecting Systems. He also has professional experience in data science and machine learning, Java, Python, Big Data, including Spark and MongoDB. He has worked at VMWare, Cisco, Mobile Iron, and Computer Science Corporation (now called DXC). He is also the author of the book Pragmatic Machine Learning with Python, which is recommended in the ACM Education Digital Library.

tracking-