\[ \newcommand{\bSigma}{\boldsymbol{\Sigma}} \newcommand{\bOmega}{\boldsymbol{\Omega}} \newcommand{\bTheta}{\boldsymbol{\Theta}} \newcommand{\bPi}{\boldsymbol{\Pi}} \newcommand{\bbeta}{\boldsymbol{\beta}} \newcommand{\balpha}{\boldsymbol{\alpha}} \newcommand{\brho}{\boldsymbol{\rho}} \newcommand{\beps}{\boldsymbol{\epsilon}} \newcommand{\blambda}{\boldsymbol{\lambda}} \newcommand{\bgamma}{\boldsymbol{\gamma}} \newcommand{\btheta}{\boldsymbol{\theta}} \newcommand{\bmu}{\boldsymbol{\mu}} \newcommand{\bpi}{\boldsymbol{\pi}} \newcommand{\bphi}{\boldsymbol{\phi}} \newcommand{\bPhi}{\boldsymbol{\Phi}} \newcommand{\boldeta}{\boldsymbol{\eta}} \newcommand{\bx}{\boldsymbol{x}} \newcommand{\bD}{\boldsymbol{D}} \newcommand{\bV}{\boldsymbol{V}} \newcommand{\bv}{\boldsymbol{v}} \newcommand{\bY}{\boldsymbol{Y}} \newcommand{\bA}{\boldsymbol{A}} \newcommand{\bB}{\boldsymbol{B}} \newcommand{\bR}{\boldsymbol{R}} \newcommand{\bM}{\boldsymbol{M}} \newcommand{\bI}{\boldsymbol{I}} \newcommand{\bC}{\boldsymbol{C}} \newcommand{\bW}{\boldsymbol{W}} \newcommand{\bw}{\boldsymbol{w}} \newcommand{\bd}{\boldsymbol{d}} \newcommand{\bT}{\boldsymbol{T}} \newcommand{\bt}{\boldsymbol{t}} \newcommand{\bZ}{\boldsymbol{Z}} \newcommand{\bX}{\boldsymbol{X}} \newcommand{\bz}{\boldsymbol{z}} \newcommand{\by}{\boldsymbol{y}} \newcommand{\br}{\boldsymbol{r}} \newcommand{\bp}{\boldsymbol{p}} \newcommand{\bb}{\boldsymbol{b}} \newcommand{\bZero}{\boldsymbol{0}} \newcommand{\bOne}{\boldsymbol{1}} \]
Modern inference methods for non-probability samples with R
Welcome!
Welcome to the book “Modern inference methods for non-probability samples in R”. In this comprehensive guide, we will delve into the details of statistical inference, focusing specifically on the unique challenges and techniques associated with non-probability samples.
Throughout this course, you will embark on a journey that combines both theoretical considerations and practical, hands-on applications Whether you’re an aspiring statistician, a data scientist, or a researcher in any field, this material will equip you with the knowledge and tools needed to navigate the intricacies of non-probability samples and extract meaningful insights from your data.
Here’s a glimpse of what you can expect:
Understanding Non-probability Samples: Gain a deep understanding of what non-probability samples are, their characteristics, and the contexts in which they are commonly used.
Challenges and Biases: Explore the inherent challenges and biases associated with non-probability samples, including selection bias, coverage bias, and non-response bias.
Statistical Inference Techniques: Discover specialized statistical methods designed to address the unique characteristics of non-probability samples, including propensity score weighting and imputation techniques.
Hands-on R Programming: An introduction to the R package
nonprobsvyfor inference with non-probability samples.
Have a great read!