nonlinear-models

Python package for Granger causality test with nonlinear forecasting methods.

Regularized Levenberg-Marquardt algorithm for nonlinear regression on small size datasets

R and C++ codes that can be used to replicate the empirical results obtained in the paper "Time-varying state correlations in state space models and their estimation via indirect inference" by Caterina Schiavoni, Siem Jan Koopman, Franz Palm, Stephan Smeekes and Jan van den Brakel.

Implementation of different Lorenz models (Matlab and Python)

Edison App: NONMEM Estimation using R

Projects of Nonlinear Systems and Control Exam (A.Y. 2022/2023) from Prof. Ticozzi

R codes to implement two examples for the mode and importance sampling estimation methods.

A presentation about {pmrm} at the 2026 Midwest Biopharmaceutical Statistics Workshop

Regression analysis is a statistical analysis technique that can be used to model relationships between a dependent and one or more independent ones. The implementation of a regression is used to describe relationships quantitatively or to predict values of the dependent variables. Here you may find the model to predict house prices.

A variation of the Binomial Model to create series with scaling and multifractal properties.

Data and R code accompanying article 10.3354/meps14261 in Marine Ecology Progress Series.

some matlab work and plotting stuff

Source code developed for the experiments in the master's dissertation entitled "Robust nonlinear beta regression"

Code and tutorials for implementing the GlObal And Local Score

Estimation of the Structural Similarity Index (SSIM) using nonlinear regression

Some drafts of miscellaneous codes.

Statistical Machine Learning, Spring 2022

Provided valuable insights into the predictive performance of different modeling methodologies for housing price prediction in Boston. It suggests that a combination of linear and non-linear models can be effective and lays the foundation for further research and practical applications in this domain.