This article explores the use of stationary distributions in time series models to define thresholds in zero-inflated data, improving classification accuracy.
This article delves into the fundamentals of Markov Chain Monte Carlo (MCMC), its applications, and its significance in solving complex, high-dimensional probability distributions.
A guide to solving DSGE models numerically, focusing on perturbation techniques and finite difference methods used in economic modeling.
Explore the different types of observational errors, their causes, and their impact on accuracy and precision in various fields, such as data science and engineering.
The Mann-Whitney U test and independent t-test are used for comparing two independent groups, but the choice between them depends on data distribution. Learn when to use each and explore real-world applications.
Understand Cochran’s Q test, a non-parametric test for comparing proportions across related groups, and its applications in binary data and its connection to McNemar’s test.
Learn the fundamentals of ARIMA modeling for time series analysis. This guide covers the AR, I, and MA components, model identification, validation, and its comparison with other models.
Discover the foundations of Ordinary Least Squares (OLS) regression, its key properties such as consistency, efficiency, and maximum likelihood estimation, and its applications in linear modeling.
Learn what the False Positive Rate (FPR) is, how it impacts machine learning models, and when to use it for better evaluation.
Learn about the Shapiro-Wilk and Anderson-Darling tests for normality, their differences, and how they guide decisions between parametric and non-parametric statistical methods.