Explores a robust location estimator (Tukey's shorth) through simulation, examining its asymptotic normality and efficiency compared to the mean and median.
A detailed proof walkthrough of the De Moivre–Laplace theorem, the earliest version of the central limit theorem for the binomial distribution.
Explores sparse correlation structures in statistical models and the conditions under which the Central Limit Theorem holds for dependent data.
A critique of the Shapiro-Wilk normality test, arguing it's often misused due to the Central Limit Theorem and is rarely the scientifically relevant question.
A technical discussion on asymptotic approximations in stratified sampling when sampling probabilities approach zero, relevant for rare disease studies.
Explores various mathematical proofs for the Central Limit Theorem, comparing approaches like characteristic functions, the Lindeberg trick, entropy, and moments.
