<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Practical Survival Modeling Techniques on Actuarial Ninja</title><link>https://www.actuarialninja.com/tags/practical-survival-modeling-techniques/</link><description>Recent content in Practical Survival Modeling Techniques on Actuarial Ninja</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Thu, 25 Sep 2025 00:49:51 +0000</lastBuildDate><atom:link href="https://www.actuarialninja.com/tags/practical-survival-modeling-techniques/index.xml" rel="self" type="application/rss+xml"/><item><title>**Modeling Survival Distributions: A Practical Guide**</title><link>https://www.actuarialninja.com/tutorials/modeling-survival-distributions-a-practical-guide/</link><pubDate>Thu, 25 Sep 2025 00:49:51 +0000</pubDate><guid>https://www.actuarialninja.com/tutorials/modeling-survival-distributions-a-practical-guide/</guid><description>&lt;p&gt;When it comes to analyzing data that involves time until a specific event occurs, such as how long a customer stays with a company or how long a patient survives after a treatment, survival analysis is the go-to method. This technique is incredibly versatile and can be applied across various fields, from healthcare and finance to social sciences and engineering. In essence, survival analysis helps us understand the probability of survival over time, which is crucial for making informed decisions in both business and clinical settings.&lt;/p&gt;</description></item></channel></rss>