Better Solutions Faster: Soft Evolution of Robust Regression Models InParetogeneticprogramming

2007 ◽  
pp. 13-32 ◽  
Author(s):  
Ekaterina Vladislavleva ◽  
Guido Smits ◽  
Mark Kotanchek
Keyword(s):  
Regression Models ◽  
Robust Regression

scholarly journals Robust Regression Models for Load Forecasting

2019 ◽  
Vol 10 (5) ◽  
pp. 5397-5404 ◽  
Author(s):  
Jian Luo ◽  
Tao Hong ◽  
Shu-Cherng Fang
Keyword(s):  
Regression Models ◽  
Robust Regression ◽  
Load Forecasting

Fear in Bureaucracy: Comparing Public and Private Sector Workers’ Expectations of Punishment

2018 ◽  
Vol 52 (2) ◽  
pp. 233-264 ◽  
Author(s):  
Jiwon Jung ◽  
Barry Bozeman ◽  
Monica Gaughan
Keyword(s):  
Regression Models ◽  
Robust Regression ◽  
Risk Propensity ◽  
Government Employees ◽  
Public And Private ◽  
Different Types ◽  
Major Predictor ◽  
New Ideas ◽  
Public And Private Sector ◽  
Organizational Rules

When employees fear punishment for taking initiative, organizations are likely to be less effective and, equally important, such fear extracts a human toll, often contributing to a variety of manifestations of unhappiness including diminished health. We focus on two different types of fears of punishment, fear of being punished for presenting new ideas and for bending organizational rules. Employing Mechanical Turk crowdsourcing data from 1,189 participants in the 2015 survey of National Administrative Studies Project Citizen, we test hypotheses about possible differences in fear of punishment according to sector (government vs. business), general risk propensity, views about coworkers, job clarity, gender, and whether respondents are members of an underrepresented racial or ethnic minority. Using nested robust regression models, we find that the two different types of fear of punishment are predicted by different variables. Sector has no bearing on fear of punishment for presenting new ideas but is a major predictor of differences in fear of bending the rules, with government employees being more fearful. While gender has no significant effects, being a racial minority is closely related to fear of presenting new ideas. Having a negative view of one’s fellow workers, particularly one’s supervisor, is associated with greater fear of punishment from both rule bending and presenting new ideas. Those with a clear organization mission and job clarity are less likely to be afraid of punishment for proposing innovative ideas but not necessarily for bending rules. We suggest that the results have implications for managerial practice and human resource reform.

scholarly journals Analysis of Efficiency of Least Trimmed Square and Least Median Square Methods in The Estimation of Robust Regression Parameters

2020 ◽  
Vol 4 (1) ◽  
pp. 21
Author(s):  
Hamdan Abdi ◽  
Sajaratud Dur ◽  
Rina Widyasar ◽  
Ismail Husein
Keyword(s):  
Observational Data ◽  
Palm Oil ◽  
Regression Models ◽  
Robust Regression ◽  
Least Squares Method ◽  
Regression Method ◽  
Estimation Methods ◽  
Land Area ◽  
Regression Methods ◽  
Regression Parameters

<span lang="EN">Robust regression is a regression method used when the remainder's distribution is not reasonable, or there is an outreach to observational data that affects the model. One method for estimating regression parameters is the Least Squares Method (MKT). The method is easily affected by the presence of outliers. Therefore we need an alternative method that is robust to the presence of outliers, namely robust regression. Methods for estimating robust regression parameters include Least Trimmed Square (LTS) and Least Median Square (LMS). These methods are estimators with high breakdown points for outlier observational data and have more efficient algorithms than other estimation methods. This study aims to compare the regression models formed from the LTS and LMS methods, determine the efficiency of the model formed, and determine the factors that influence the production of community oil palm in Langkat District in 2018. The results showed that in testing, the estimated model of the regression parameters showed the same results. Compared to the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity influence the production of palm oil smallholders in Langkat District in 2018. as well as the comparison of the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity are factors that influence the production of palm oil smallholders in Langkat District in 2018. as well as the comparison of the efficiency estimator and the error square value, it was concluded that the LTS method was more efficient. Variable land area and productivity are factors that influence the production of palm oil smallholders in Langkat District in 2018</span>

Fuzzy Robust Regression Model by Possibility Maximization

2011 ◽  
Vol 15 (4) ◽  
pp. 479-484 ◽  
Author(s):  
Yoshiyuki Yabuuchi ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Regression Model ◽  
Regression Models ◽  
Robust Regression ◽  
Fuzzy Regression ◽  
Target System ◽  
Economic Systems ◽  
Switching Regression ◽  
Clustering Model ◽  
Fuzzy Regression Model ◽  
Mixed Samples

Since management and economic systems are complex, it is hard to handle data obtained in management and economic areas. Hitherto, in the fields, much research has focused on the structure and analysis of such data. H. Tanaka et al. proposed a fuzzy regression model to illustrate the potential possibilities inherent in the target system. J. C. Bezdek proposed a switching regression model based on a fuzzy clustering model to separate mixed samples coming from plural latent systems and apply regression models to the groups of samples coming from each system. It is hard to illustrate a rough and moderate possibility of the target system. In this paper, to deal with the possibility of a social system, we propose a new fuzzy robust regression model.

scholarly journals Regression models tolerant to massively missing data: a case study in solar-radiation nowcasting

2014 ◽  
Vol 7 (12) ◽  
pp. 4387-4399 ◽  
Author(s):  
I. Žliobaitė ◽  
J. Hollmén ◽  
H. Junninen
Keyword(s):  
Missing Data ◽  
Solar Radiation ◽  
Regression Models ◽  
Robust Regression ◽  
Minimum Energy ◽  
Data Streaming ◽  
Linear Regression Models ◽  
Automatic Data ◽  
Minimum Energy Consumption

Abstract. Statistical models for environmental monitoring strongly rely on automatic data acquisition systems that use various physical sensors. Often, sensor readings are missing for extended periods of time, while model outputs need to be continuously available in real time. With a case study in solar-radiation nowcasting, we investigate how to deal with massively missing data (around 50% of the time some data are unavailable) in such situations. Our goal is to analyze characteristics of missing data and recommend a strategy for deploying regression models which would be robust to missing data in situations where data are massively missing. We are after one model that performs well at all times, with and without data gaps. Due to the need to provide instantaneous outputs with minimum energy consumption for computing in the data streaming setting, we dismiss computationally demanding data imputation methods and resort to a mean replacement, accompanied with a robust regression model. We use an established strategy for assessing different regression models and for determining how many missing sensor readings can be tolerated before model outputs become obsolete. We experimentally analyze the accuracies and robustness to missing data of seven linear regression models. We recommend using the regularized PCA regression with our established guideline in training regression models, which themselves are robust to missing data.

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