Comparing Robust Regression Estimators to Detect Data Clusters: A Case Study

2014 ◽  
pp. 125-138
Author(s):  
Alessandra Durio ◽  
Ennio Isaia
Keyword(s):  
Robust Regression ◽  
Regression Estimators ◽  
Data Clusters

A geostatistical approach to winter road surface condition estimation using mobile RWIS data

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Robust Regression ◽  
Surface Condition ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
Modelling Framework ◽  
Geostatistical Approach ◽  
Winter Road ◽  
Systematic Framework

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.

scholarly journals PEMODELAN REGRESI RIDGE ROBUST-MM DALAM PENANGANAN MULTIKOLINIERITAS DAN PENCILAN (Studi Kasus : Faktor-Faktor yang Mempengaruhi AKB di Jawa Tengah Tahun 2017)

2019 ◽  
Vol 8 (1) ◽  
pp. 24-34
Author(s):  
Eka Destiyani ◽  
Rita Rahmawati ◽  
Suparti Suparti
Keyword(s):  
Least Squares ◽  
Robust Regression ◽  
Ordinary Least Squares ◽  
Regression Method ◽  
Predictor Variables ◽  
Healthy Life ◽  
Significance Level ◽  
Central Java ◽  
Regression Parameters

The Ordinary Least Squares (OLS) is one of the most commonly used method to estimate linear regression parameters. If multicollinearity is exist within predictor variables especially coupled with the outliers, then regression analysis with OLS is no longer used. One method that can be used to solve a multicollinearity and outliers problems is Ridge Robust-MM Regression. Ridge Robust-MM  Regression is a modification of the Ridge Regression method based on the MM-estimator of Robust Regression. The case study in this research is AKB in Central Java 2017 influenced by population dencity, the precentage of households behaving in a clean and healthy life, the number of low-weighted baby born, the number of babies who are given exclusive breastfeeding, the number of babies that receiving a neonatal visit once, and the number of babies who get health services. The result of estimation using OLS show that there is violation of multicollinearity and also the presence of outliers. Applied ridge robust-MM regression to case study proves ridge robust regression can improve parameter estimation. Based on t test at 5% significance level most of predictor variables have significant effect to variable AKB. The influence value of predictor variables to AKB is 47.68% and MSE value is 0.01538.Keywords:  Ordinary  Least  Squares  (OLS),  Multicollinearity,  Outliers,  RidgeRegression, Robust Regression, AKB.

An analysis of constrained robust regression estimators

1984 ◽  
Vol 31 (2) ◽  
pp. 283-296 ◽  
Author(s):  
Ronald G. Askin ◽  
Douglas C. Montgomery
Keyword(s):  
Robust Regression ◽  
Regression Estimators

Outlier detection by means of robust regression estimators for use in engineering science

2009 ◽  
Vol 10 (6) ◽  
pp. 909-921 ◽  
Author(s):  
Serif Hekimoglu ◽  
R. Cuneyt Erenoglu ◽  
Jan Kalina
Keyword(s):  
Outlier Detection ◽  
Robust Regression ◽  
Engineering Science ◽  
Regression Estimators

scholarly journals Minimum Covariance Determinant-Based Quantile Robust Regression-Type Estimators for Mean Parameter

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Usman Shahzad ◽  
Nadia H. Al-Noor ◽  
Noureen Afshan ◽  
David Anekeya Alilah ◽  
Muhammad Hanif ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Robust Regression ◽  
Real Life ◽  
Minimum Covariance Determinant ◽  
Mean Squared Errors ◽  
Regression Estimators ◽  
Chi Squared ◽  
Class Of Estimators ◽  
The Mean ◽  
Ratio Estimators

Robust regression tools are commonly used to develop regression-type ratio estimators with traditional measures of location whenever data are contaminated with outliers. Recently, the researchers extended this idea and developed regression-type ratio estimators through robust minimum covariance determinant (MCD) estimation. In this study, the quantile regression with MCD-based measures of location is utilized and a class of quantile regression-type mean estimators is proposed. The mean squared errors (MSEs) of the proposed estimators are also obtained. The proposed estimators are compared with the reviewed class of estimators through a simulation study. We also incorporated two real-life applications. To assess the presence of outliers in these real-life applications, the Dixon chi-squared test is used. It is found that the quantile regression estimators are performing better as compared to some existing estimators.

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