Survival analysis generally deals with analysis of data arising from clinical trials. Censoring, truncation, and missing data create analytical challenges and the statistical methods and inference require novel and different approaches for analysis. Statistical properties, essentially asymptotic ones, of the estimators and tests are aptly handled in the counting process framework which is drawn from the larger arm of stochastic calculus. With explosion of data generation during the past two decades, survival data has also enlarged assuming a gigantic size. Most statistical methods developed before the millennium were based on a linear approach even in the face of complex nature of survival data. Nonparametric nonlinear methods are best envisaged in the Machine Learning school. This book attempts to cover all these aspects in a concise way. Survival Analysis offers an integrated blend of statistical methods and machine learning useful in analysis of survival data. The purpose of the offering is to give an exposure to the machine learning trends for lifetime data analysis. Features: <ul> <li>Classical survival analysis techniques for estimating statistical functional and hypotheses testing</li> <li>Regression methods covering the popular Cox relative risk regression model, Aalen's additive hazards model, etc.</li> <li>Information criteria to facilitate model selection including Akaike, Bayes, and Focused</li> <li>Penalized methods</li> <li>Survival trees and ensemble techniques of bagging, boosting, and random survival forests</li> <li>A brief exposure of neural networks for survival data</li> <li>R program illustration throughout the book</li>

Survival Analysis

Computers & Technology

Tattar, Prabhanjan Narayanachar

Vaman, H J

Computer Science

Chapman and Hall/CRC

Software Implementation Illustrated with R and PythonAbout This Book<ul>
<li>Learn the nature of data through software which takes the preliminary concepts right away using R and Python.</li>
<li>Understand data modeling and visualization to perform efficient statistical analysis with this guide.</li>
<li>Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.</li>
</ul>Who This Book Is ForIf you want to have a brief understanding of the nature of data and perform advanced statistical analysis using both R and Python, then this book is what you need. No prior knowledge is required. Aspiring data scientist, R users trying to learn Python and vice versaWhat You Will Learn<ul>
<li>Learn the nature of data through software with preliminary concepts right away in R</li>
<li>Read data from various sources and export the R output to other software</li>
<li>Perform effective data visualization with the nature of variables and rich alternative options</li>
<li>Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference</li>
<li>Learn statistical inference through simulation combining the classical inference and modern computational power</li>
<li>Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics</li>
<li>Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearity</li>
</ul>In DetailStatistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions.
This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world.
You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python.
The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics.
By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.Style and approachDeveloping better and smarter ways to analyze data. Making better decisions/future predictions. Learn how to explore, visualize and perform statistical analysis. Better and efficient statistical and computational methods. Perform practical examples to master your learning

Statistical Application Development with R and Python

Databases & Big Data

Packt Publishing

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Books by Tattar, Prabhanjan Narayanachar