Bootstrap methods and their application ebook

Bootstrap's existence let it quickly become famous as a front-end framework that offered a wide set of tools from page grid up to components that render a web page in the best possible way for any device.

This book will be a tutorial covering various examples as well as step-by-step methodology to create interesting web applications using Bootstrap and to understand the front-end framework to its core.

We begin with an introduction to the Bootstrap framework and setting up an environment to build a simple web page. We then cover the grid system, basic Bootstrap components, HTML elements, and customization components for responsive and mobile first development.

This is presented by creating a beautiful Landing page sample. You will also learn how to create a web application like Twitter by using the full set of components offered in the framework.

Finally, you will learn to create a dashboard web app, using Bootstrap to its finest potential including component customizations, event handling, and external library integration.

All these examples are explained step-by-step and in depth, while covering the versions 3 and the most recent version 4 of Bootstrap. So, you will be in the state of the art for front-end development. By the end of this book, you will be familiar with the development of a plugin for the framework and Bootstrap's world which is popular for fast paced front-end web development, used in countless projects all over the world, and now yours. Style and approach This is a step-by-step guide that closely focuses on developing web applications in most objective way solving real-life problems with Bootstrap.

It covers two sample web applications, which are explained in detail. Author : John E. Rank-based and resampling techniques are heavily represented, but robust techniques are considered as well.

These techniques include one-sample testing and estimation, multi-sample testing and estimation, and regression. Attention is payed to the intellectual development of the field, with a thorough review of bibliographical references.

Computational tools, in R and SAS, are developed and illustrated via examples. Exercises designed to reinforce examples are included. Simulations are conducted under different scenarios by varying error covariance structures and sample sizes. We generate data with four covariance structures and three sample size settings. Moreover, to show the effectiveness of the proposed method over the other methods, we compare the results of the proposed method with existing bootstrap methods: parametric, residual, REB, and wild.

By considering the above scenarios, we carry out a series of simulations under five different objectives. In the first two objectives, we observe the bootstrap distributions of model coefficients and set out the number of bootstrap replications as suitable for the upcoming simulations.

In the third and fourth objectives, we study the parameter estimation performance and assess parameter estimation accuracy. Finally, we compute the empirical coverage probability of the parameters. The simulation results with the heteroscedastic errors demonstrate that the accuracy of the estimation process of the proposed bootstrap outperforms the wild bootstrap under the small sample settings.

In contrast, the estimation performance, accuracy, and coverage of both methods become similar in the large sample setting. Moreover, the proposed and existing bootstrap methods have similar performance under homoscedasticity.

Furthermore, two applications with real-life examples are illustrated to evaluate the effectiveness of the proposed bootstrap method.

Analysing climate data is an exciting challenge, which is increased by non-normal distributional shape, serial dependence, uneven spacing and timescale uncertainties. It shows the bootstrap to perform reliably in the most important statistical estimation techniques: regression, spectral analysis, extreme values and correlation.

This book is written for climatologists and applied statisticians. It explains step by step the bootstrap algorithms including novel adaptions and methods for confidence interval construction.

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