dqr: Distributed Quantile Regression

by Pilot Sampling and One-Step Updating

Spark implementation

System Requirements

• Spark >= 2.3.1

• Python >= 3.7.0

  • pyarrow >= 0.15.0 Please read this Compatibility issue with Spark 2.3.x or 2.4.x
  • statsmodels >= 0.12.0

• See setup.py for detailed requirements.

Run the code on a Spark platform

• Zip the code into a portable package (a zipped file dqr.zip will be placed into the projects folder)

make zip

• Run the project on a Spark platform

PYSPARK_PYTHON=/usr/local/bin/python3.7 \
        spark-submit --py-files projects/dqr.zip \
        projects/dqr_spark.py

Build a Python module

You could also build the code into standard Python module and deploy to Spark clusters.

python setup.py bdist

Conceptual demo in R

• Contributed by @edwardguo61

• The required R version: 3.5.1

• Files:

  • dqr/Restimator.R: one-shot estimation and one-step estimation for distributed quantile regression
  • dqr/R/simulator.R: simulation functions to generate random/non-random data
  • dqr/R/uilts.R: other functions used
  • projects/dqr_demo.R: generate data, conduct estimation and generate plot. Please run dqr_demo.R to see how to use the functions.

References

• Rui Pan, Tunan Ren, Baishan Guo, Feng Li, Guodong Li and Hansheng Wang (2021). A Note on Distributed Quantile Regression by Pilot Sampling and One-Step Updating, Journal of Business and Economic Statistics. (in press).