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Statistical Application Development with R and Python

You're reading from   Statistical Application Development with R and Python Develop applications using data processing, statistical models, and CART

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Product type Paperback
Published in Aug 2017
Publisher
ISBN-13 9781788621199
Length 432 pages
Edition 2nd Edition
Languages
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Author (1):
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Prabhanjan Narayanachar Tattar Prabhanjan Narayanachar Tattar
Author Profile Icon Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar
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Toc

Table of Contents (12) Chapters Close

Preface 1. Data Characteristics FREE CHAPTER 2. Import/Export Data 3. Data Visualization 4. Exploratory Analysis 5. Statistical Inference 6. Linear Regression Analysis 7. Logistic Regression Model 8. Regression Models with Regularization 9. Classification and Regression Trees 10. CART and Beyond Index

Understanding bagging

Bagging is an abbreviation for bootstrap aggregation. The important underlying concept here is the bootstrap, which was invented by the eminent scientist Bradley Efron. We will first digress here slightly from the CART technique and consider a very brief illustration of the bootstrap technique.

The bootstrap

Consider a random sample The bootstrap of size n from The bootstrap. Let The bootstrap be an estimator of The bootstrap. To begin with, we first draw a random sample of size n from The bootstrap with a replacement; that is, we obtain a random sample The bootstrap, where some of the observations from the original sample may have repetitions and some may not be present at all. There is no one-to-one correspondence between The bootstrap and The bootstrap. Using The bootstrap, we compute The bootstrap. Repeat this exercise several times, say B. The inference for The bootstrap is carried out by using the sampling distribution of the bootstrap samples The bootstrap, …, The bootstrap.

Let us illustrate the concept of the bootstrap with the famous aspirin example; see Chapter 8 of Tattar, et. al. (2013). A surprising double-blind...

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