Comparison of multi-response prediction methods

Raju Rimal,Trygve Almøy,Solve Sæbø

doi:10.1016/j.chemolab.2019.05.004

Raju Rimal, Trygve Almøy + Show 1 more

Open Access

https://doi.org/10.1016/j.chemolab.2019.05.004

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Abstract

While data science is battling to extract information from the enormous explosion of data, many estimators and algorithms are being developed for better prediction. Researchers and data scientists often introduce new methods and evaluate them based on various aspects of data. However, studies on the impact of/on a model with multiple response variables are limited. This study compares some newly-developed (envelope) and well-established (PLS, PCR) prediction methods based on real data and simulated data specifically designed by varying properties such as multicollinearity, the correlation between multiple responses and position of relevant principal components of predictors. This study aims to give some insight into these methods and help the researcher to understand and use them in further studies.

Highlights

The prediction has been an essential component of modern data science, whether in the discipline of statistical analysis or machine learning
The main aim of this paper is to present a comprehensive comparison of contemporary prediction methods such as simultaneous envelope estimation (Senv) [8] and envelope estimation in predictor space (Xenv) [7] with customary prediction methods such as Principal Component Regression (PCR), Partial Least Squares Regression (PLS) using simulated dataset with controlled properties
A concept of envelope introduced by Cook et al [6] based on the reduction in the regression model was implemented for the development of different estimators

Summary

Introduction

The prediction has been an essential component of modern data science, whether in the discipline of statistical analysis or machine learning. Modern technology has facilitated a massive explosion of data such data often contain irrelevant information that makes prediction difficult. Researchers are devising new methods and algorithms in order to extract information to create robust predictive models. Such models mostly contain predictor variables that are directly or indirectly correlated with other predictor variables. Studies often consist of many response variables correlated with each other. These interlinked relationships influence any study, whether it is predictive modelling or inference

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