Assignment Objectives: Reinforce the likelihood concepts and MLE. Understand the concepts of confidence intervals. Master the process of finding likelihood ratio confidence interval of unknown parameter

Midterm Exam Objectives Understand the definition and relationship between PDFs and CDFs, including their non-parametric estimators: the empirical distribution function and kernel density estimation (KDE). Estimate sampling distributions using simulation-based methods, specifically the bootstrap. Derive point estimates of parameters using the method of moments and maximum likelihood estimation (MLE). Describe the asymptotic (normal) and bootstrap sampling distributions of maximum likelihood estimators. Apply all the above inferential procedures in a programming environment to perform numerical data analysis.

Assignment Objectives Comprehend the likelihood function and its properties. Master the maximum likelihood estimation framework and required components. Understand the plug-in principle underlying MLE. Implement maximum likelihood estimation procedures in R.

Assignment Objectives Master the fundamental concepts of point estimation and performance metrics Understand the theoretical foundation of the method of moments estimator (MME) Implement MME in R, incorporating numerical approximation methods

Assignment 1: Estimating CDF and PDF