scispace - formally typeset
Open AccessBook

An introduction to the bootstrap

Reads0
Chats0
TLDR
This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
Abstract
This article presents bootstrap methods for estimation, using simple arguments. Minitab macros for implementing these methods are given.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Regression Shrinkage and Selection via the Lasso

TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Journal ArticleDOI

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

TL;DR: An overview of simple and multiple mediation is provided and three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model are explored.
Book

Data Mining: Concepts and Techniques

TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Book

Data Mining: Practical Machine Learning Tools and Techniques

TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
References
More filters
Journal ArticleDOI

Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy

TL;DR: The bootstrap is extended to other measures of statistical accuracy such as bias and prediction error, and to complicated data structures such as time series, censored data, and regression models.
Book

Mathematical Statistics and Data Analysis

TL;DR: In this article, the authors present a model for estimating parameters and fitting of probability distributions from the normal distribution. But the model is not suitable for the analysis of categorical data.
Book

Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses

TL;DR: This book provides a step-by-step manual on the application of permutation tests in biology, medicine, science, and engineering and shows how the problems of missing and censored data, nonresponders, after thefact covariates, and outliers may be handled.
Book

A Handbook of Small Data Sets

TL;DR: In this paper, the authors present a data set for statistics lecturers who want ready-made data sets complete with notes for teaching, which is of interest to statistic lecturers.