Regression

Modeling correlated count data using bivariate Poisson regression with EM algorithm. Handle overdispersion and correlation in count models.

Flexible non-linear regression using cubic spline basis functions. Bayesian approach with PyMC for smooth curve fitting and uncertainty quantification.

Robust regression using Huber loss function. Handle outliers and improve model stability with robust statistical methods.

Quantile regression for uncertainty estimation beyond the mean. PyTorch implementation for predicting conditional quantiles.

Part 2: Applying EM algorithm to censored linear regression. Handle missing data and truncated observations in statistical modeling.