ManhHB94/varlmhet

VARLMHET: Stata module to calculate heterokedasticity tests for VAR, VEC models

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blasif/j.environ.2024

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

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ManhHB94/xttest4

XTTEST4: Stata module to calculate heterokedasticity tests for fixed effects models

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blei-lab/treeffuser

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

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Akashash01/Akash_Linear-regression

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

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youssef-laouina/Predicting-Apartments-Prices-in-Buenos-Aires

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

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aryanrzn/Heteroscedastic-Weighted-Least-Squares

Language: R - Size: 3.91 KB - Last synced at: about 1 year ago - Pushed at: about 3 years ago - Stars: 0 - Forks: 0

kaypro283/SmoothTrend

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.

Language: Python - Size: 245 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

vita-epfl/TIC-TAC

[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.

Language: Python - Size: 38.5 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 4 - Forks: 1

shwetapardhi/Assignment-05-Multiple-Linear-Regression-2

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

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ChihLi/HetCalibrate

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.

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Teliteu/SPURRvsHALL

application of tests to verify model specifications and presence of heteroscedasticity in the forest inventory sample

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RezaDastranj/LME-ASDRs

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

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aaron1rcl/heteroscedastic_data

Various models and techniques to show how to handle heteroscedastic data

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RezaDastranj/GAMM-ASDRs

Generalized Additive Forecasting Mortality

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jcorrean/GAMLSS4PsycData

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

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venkatesh-eranti/Housing_case-study

A real estate company that has a dataset containing the prices of properties in the Delhi region. It wishes to use the data to optimise the sale prices of the properties based on important factors such as area, bedrooms, parking, etc

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vaitybharati/P26.-Supervised-ML---Multiple-Linear-Regression---Cars-dataset

Supervised-ML---Multiple-Linear-Regression---Cars-dataset. Model MPG of a car based on other variables. EDA, Correlation Analysis, Model Building, Model Testing, Model Validation Techniques, Collinearity Problem Check, Residual Analysis, Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Leverage value, Improving the Model, Model - Re-build, Re-check and Re-improve - 2, Model - Re-build, Re-check and Re-improve - 3, Final Model, Model Predictions.

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vaitybharati/Assignment-05-Multiple-Linear-Regression-2

Assignment-05-Multiple-Linear-Regression-2. 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 the past few years State -- states from which data is collected Profit -- profit of each state in the past few years.

Language: Jupyter Notebook - Size: 669 KB - Last synced at: almost 2 years ago - Pushed at: about 4 years ago - Stars: 2 - Forks: 9

vaitybharati/P27.-Supervised-ML---Multiple-Linear-Regression---Toyoto-Cars

Supervised-ML---Multiple-Linear-Regression---Toyota-Cars. EDA, Correlation Analysis, Model Building, Model Testing, Model Validation Techniques, Collinearity Problem Check, Residual Analysis, Model Deletion Diagnostics (checking Outliers or Influencers) Two Techniques : 1. Cook's Distance & 2. Leverage value, Improving the Model, Model - Re-build, Re-check and Re-improve - 2, Model - Re-build, Re-check and Re-improve - 3, Final Model, Model Predictions.

Language: Jupyter Notebook - Size: 3.54 MB - Last synced at: almost 2 years ago - Pushed at: about 4 years ago - Stars: 2 - Forks: 0

olesyamba/ICvsML

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

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ChihLi/HetCalibrate-Reproducibility

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) (https://epubs.siam.org/doi/10.1137/21M1417946).

Language: R - Size: 560 KB - Last synced at: about 2 years ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

WuCandice/-Statistical-Analysis-of-Economic-Variables-and-the-Mortgage-Rate-in-the-United-States

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

Language: R - Size: 3.98 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0

tnathu-ai/time_series_analysis_in_R

time series analysis in R use cases

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mamomen1996/Python_CS_01

Traditional Regression problem project in Python

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samuelzammit/EstCosmoPar-GPR-TPR

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

Language: Python - Size: 67.4 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

MRCIEU/varGWAS

GWAS of trait variance (C++)

Language: R - Size: 9.47 MB - Last synced at: over 2 years ago - Pushed at: almost 3 years ago - Stars: 2 - Forks: 0

uweremer/regression_diagnostics

Skript zur Videoreihe Regressionsdiagnostik in R

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MRCIEU/varGWASR

R package to perform regression-based Brown-Forsythe test

Language: R - Size: 107 KB - Last synced at: over 2 years ago - Pushed at: almost 3 years ago - Stars: 1 - Forks: 0

pvh95/ols_bootstrap

OLS Bootstrap on Cross-Sectional Data

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PatilSukanya/Assignment-05.-Multiple-Linear-regression-Q2

Used libraries and functions as follows:

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Ismail-therap/OLS-Regression-Analysis

Ordinary least square (OLS) regression analysis carried out in this project. The selected dependent variables are some public health indicators like anxiety, diabetes. We tried to find the independent variables which are responsible for this health hazard.

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AndrMenezes/nlm2017

Course of Nonlinear Regression at UEM in 2017

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ireneliu521/BOPS-Strategy-Analysis_Project_R

Evaluate the Buy Online Pick-up in Store (BOPS) strategy with a real-world dataset

Size: 10.1 MB - Last synced at: 3 months ago - Pushed at: over 7 years ago - Stars: 0 - Forks: 0