Tensorflow based training, inference and feature engineering pipelines used in OSIC Kaggle Competition
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Updated
Oct 7, 2020 - Jupyter Notebook
Tensorflow based training, inference and feature engineering pipelines used in OSIC Kaggle Competition
R/jags model code for hierarchical Bayesian quantile regression
Do files employed in my research
In this repository, software applications in simulation and visualization for various applications are presented with interesting examples.
test the phenomenon of PEAD in China
We carried out thematic clustering and differential prediction of number of retweets with Gradient Boosting and Quantile regression. We also performed text embedding with Bidirectional Encoder Representations (BERT, Google) for deep prediction with TensorFlow
Open-source implementation of ADMM algorithms for penalized quantile regression in Gu, et al. 2018 Technometrics
데이콘 태양광 발전량 예측 AI 경진대회
Quantile predictions for the Belgian day-ahead electricity market.
Notes and laboratories from a graduate course in Business Analytics.
Detailed implementation of various regression analysis models and concepts on real dataset.
This repository contains the quantitative analysis based on quantile regression models that examine the interaction between inflation and public debt
Kaggle competition "Net-Load Forecasting During the "Sobriety" Period"
D-Vine GAM Copula based Quantile Regression
R Package: Adaptively weighted group lasso for semiparametic quantile regression models
Scripts to create diagnostic plots of Stata sqreg fits
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