Survival Analysis Essentials
Master Probability, Algorithms, and Time-to-Event Data in Computer Science
Most statistical methods assume you have complete information about your outcomes. But in reality, you often don't. Equipment fails before you finish observing it. Customers leave before you can measure their lifetime value. Patients recover or are lost to follow-up. This incomplete data—called censoring—breaks traditional statistical approaches and demands a specialized framework: survival analysis. This book teaches you survival analysis from the ground up, connecting probability theory to practical algorithms you can implement. You'll learn why standard regression fails with time-to-event data, how the Kaplan-Meier estimator handles censoring elegantly, and when to use Cox models versus parametric approaches. Whether you're analyzing medical outcomes, predicting customer churn, or assessing system reliability, you'll understand both the mathematical foundations and the computational considerations that make survival analysis work in practice. Clear explanations, worked examples, and algorithmic insights prepare you to apply these powerful techniques to your own data challenges.
Est. Length
191 pages (8 chapters)
Language
English
Publication date
Feb 26, 2026
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