Repo where different methods for price regression are used (supervised machine learning)
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Updated
Jun 4, 2020 - Jupyter Notebook
Repo where different methods for price regression are used (supervised machine learning)
The purpose of my application was to solve a problem many businesses (small businesses in particular) face. They do not know how much to produce, where to price, how much to spend on advertising and many other questions. Eden’s purpose was to answer these questions for them easily and with no technical acumen required by the user. Eden would mod…
Diagnostic tools for regression modeling. Julia-equivalent for diagnoser (https://github.com/robertschnitman/diagnoser).
Testing different models for the linear regression model with one estimator and heteroskedacity in data
Here I have checked and removed for heteroskedasticity .
Script used for my undergraduate thesis
OLS regression with possibility of controlling for fixed effects and robust standard errors
Econometrics_regression analysis using R language
Basic methodologies of Empirical Research applied on various case studies (R language)
Full Log-Likelihood Heteroskedastic Regression with Deep Neural Networks and Tensorflow
Diagnostic tools for regression modeling.
Linear Multilinear and Logistic Regression in Machine Learning
Supplementary materials for the manuscript "Latent-class trajectory modeling with a heterogeneous mean-variance relation" by N. G. P. Den Teuling, F. Ungolo, S.C. Pauws, and E.R. van den Heuvel
Impact of macroecomonic variables on S&P 500
R package to perform regression-based Brown-Forsythe test
An R package for time series modelling with mixture autoregressive and related models.
As part of this project, we have used Regression Analysis on top of a panel data on Guns in USA to determine the "Effect of Shall-Carry Law on Violence Rate and Incarceration Rate in United States".
R Code for Bayesian Inference for Structural Vector Autoregressions Identified with Markov-Switching Heteroskedasticity
GWAS of trait variance (C++)
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