Prabhanjan Narayanachar Tattar – författare

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:Classical survival analysis techniques for estimating statistical functional and hypotheses testingRegression methods covering the popular Cox relative risk regression model, Aalen’s additive hazards model, etc.Information criteria to facilitate model selection including Akaike, Bayes, and FocusedPenalized methodsSurvival trees and ensemble techniques of bagging, boosting, and random survival forestsA brief exposure of neural networks for survival dataR program illustration throughout the book

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:

Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model, Aalen’s additive hazards model, etc. Information criteria to facilitate model selection including Akaike, Bayes, and Focused Penalized methods Survival trees and ensemble techniques of bagging, boosting, and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book

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:

Classical survival analysis techniques for estimating statistical functional and hypotheses testing Regression methods covering the popular Cox relative risk regression model, Aalen’s additive hazards model, etc. Information criteria to facilitate model selection including Akaike, Bayes, and Focused Penalized methods Survival trees and ensemble techniques of bagging, boosting, and random survival forests A brief exposure of neural networks for survival data R program illustration throughout the book

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniquesKey Features[] Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages[] Understand how to apply useful data analysis techniques in R for real-world applications[] An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysisBook DescriptionThis cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.What you will learn[] Get to know the functional characteristics of R language[] Extract, transform, and load data from heterogeneous sources[] Understand how easily R can confront probability and statistics problems[] Get simple R instructions to quickly organize and manipulate large datasets[] Create professional data visualizations and interactive reports[] Predict user purchase behavior by adopting a classification approach[] Implement data mining techniques to discover items that are frequently purchased together[] Group similar text documents by using various clustering methodsWho this book is forThis book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.

Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

Key Features

[] Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages[] Understand how to apply useful data analysis techniques in R for real-world applications[] An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis

Book Description

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently.The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration.In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction.By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.

What you will learn

[] Get to know the functional characteristics of R language[] Extract, transform, and load data from heterogeneous sources[] Understand how easily R can confront probability and statistics problems[] Get simple R instructions to quickly organize and manipulate large datasets[] Create professional data visualizations and interactive reports[] Predict user purchase behavior by adopting a classification approach[] Implement data mining techniques to discover items that are frequently purchased together[] Group similar text documents by using various clustering methods

Who this book is for

This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.

Software Implementation Illustrated with R and PythonAbout This Book• Learn the nature of data through software which takes the preliminary concepts right away using R and Python.• Understand data modeling and visualization to perform efficient statistical analysis with this guide.• Get well versed with techniques such as regression, clustering, classification, support vector machines and much more to learn the fundamentals of modern statistics.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• Learn the nature of data through software with preliminary concepts right away in R• Read data from various sources and export the R output to other software• Perform effective data visualization with the nature of variables and rich alternative options• Do exploratory data analysis for useful first sight understanding building up to the right attitude towards effective inference• Learn statistical inference through simulation combining the classical inference and modern computational power• Delve deep into regression models such as linear and logistic for continuous and discrete regressands for forming the fundamentals of modern statistics• Introduce yourself to CART – a machine learning tool which is very useful when the data has an intrinsic nonlinearityIn 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

Explore powerful R packages to create predictive models using ensemble methodsKey FeaturesImplement machine learning algorithms to build ensemble-efficient modelsExplore powerful R packages to create predictive models using ensemble methodsLearn to build ensemble models on large datasets using a practical approachBook DescriptionEnsemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy.Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models.By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.What you will learnCarry out an essential review of re-sampling methods, bootstrap, and jackknifeExplore the key ensemble methods: bagging, random forests, and boostingUse multiple algorithms to make strong predictive modelsEnjoy a comprehensive treatment of boosting methodsSupplement methods with statistical tests, such as ROCWalk through data structures in classification, regression, survival, and time series dataUse the supplied R code to implement ensemble methodsLearn stacking method to combine heterogeneous machine learning modelsWho this book is forThis book is for you if you are a data scientist or machine learning developer who wants to implement machine learning techniques by building ensemble models with the power of R. You will learn how to combine different machine learning algorithms to perform efficient data processing. Basic knowledge of machine learning techniques and programming knowledge of R would be an added advantage.