Treeffuser is an easy-to-use package for probabilistic prediction on tabular data with tree-based diffusion models.

Code for reproducing the results of the paper "A class of modular and flexible covariate-based covariance functions for nonstationary spatial modeling"

This is an linear approach machine learning model used to predict the values of variable(dependent) based on other variables(independent).

Machine learning project predicting real estate prices in Buenos Aires, utilizing advanced techniques for outlier detection, heteroskedasticity handling, and model optimization

SmoothTrend is a comprehensive time series analysis tool that utilizes Holt-Winters, Holt, and Simple Exponential Smoothing methods, as well as ARIMA/SARIMA modeling, to perform advanced trend analysis, stationarity testing, residual analysis, and forecasting.

[ICML 2024] Code repository for "TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression". We address the problem of sub-optimal covariance estimation in deep heteroscedastic regression by proposing a new model and metric.

Prepare a prediction model for profit of 50_startups data. Do transformations for getting better predictions of profit and make a table containing R^2 value for each prepared model. R&D Spend -- Research and devolop spend in the past few years Administration -- spend on administration in the past few years Marketing Spend -- spend on Marketing in t

This R package allows calibration parameter estimation for inexact computer models with heteroscedastic errors proposed by Sung, Barber, and Walker (2022) in SIAM/ASA Journal on Uncertainty Quantification.

Age-Gender-Country-Specific Death Rates Modelling and Forecasting: A Linear Mixed-Effects Model

Various models and techniques to show how to handle heteroscedastic data

Generalized Additive Forecasting Mortality

This repo provides supplemental material for the article titled: "Assessing Potential Heteroscedasticity in Psychological Data: A GAMLSS approach"

Usual linear regression or XGBoost? Combo! Or how I was investigating the impact of intellectual capital on NASDAQ-100 capitalization during 2 years.

This project is about to use linear regression to examine the relationship between various economic variables and the mortgage rate in the United States.

time series analysis in R use cases

Estimation of the Hubble constant using Gaussian process regression and viable alternatives

Traditional Regression problem project in Python

OLS Bootstrap on Cross-Sectional Data

Skript zur Videoreihe Regressionsdiagnostik in R

This instruction aims to reproduce the results in the paper “Calibration of inexact computer models with heteroscedastic errors” proposed by Sung, Barber, and Walker (2022).