Subset selection in multiple linear regression in the presence of outlier and multicollinearity

Jadhav, N. H.; Kashid, D. N.; Kulkarni, Shreyas

doi:10.1016/j.stamet.2014.02.002

Cited by 28 publications

(9 citation statements)

References 12 publications

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“…Path coefficient analysis gives more information among variables as it partitions correlation coefficients into direct and indirect effect and causes of association [4,5]. This approach is commonly used in crop breeding and has been efficacious in revealing the interrelationships between characters, either yield, grain quality or the effects of interaction of genotype by environment or management of cultivation [6,7,8,9]. The result helps to formulate selection criteria based on the direct and indirect effects.…”

Section: Introductionmentioning

confidence: 99%

Correlation and Path Coefficient Analysis Studies on Grain Yield and Its Contributing Characters in Maize (Zea mays L.)

Taiwo

Nwonuala

Foby

et al. 2020

IJPSS

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In crop breeding, the selection for yield is made complex because of the quantitative and polygenic nature of the character. This study was undertaken to assess character association and show the contribution of various yield contributing characters in some maize varieties through the help of correlation and path coefficient analyses in order to identify appropriate plant characters for selection to improve maize grain yield. Seventeen maize varieties were sown in a randomized complete block design with three replications in a humid environment of Port Harcourt, Nigeria in 2018 under rainfed condition. Data were recorded for days to 50% anthesis, days to 50% silking, anthesis-silking interval, plant height (cm) and ear height (cm), number of plants per plot, number of plants harvested, number of ears harvested, moisture content (%), field weight (kg), and grain yield (t ha-1). Results showed number of plants per plot, number of plants harvested, number of ears harvested and field weight correlated positively and significantly both phenotypically and genotypically with grain yield. Path coefficient analysis at the genotypic level also revealed field weight and days to 50% anthesis as the characters exerting the highest positive direct effect on grain yield. Therefore, maize grain yield could be improved through indirect selection for these characters.

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Section: Introductionmentioning

confidence: 99%

Correlation and Path Coefficient Analysis Studies on Grain Yield and Its Contributing Characters in Maize (Zea mays L.)

Taiwo

Nwonuala

Foby

et al. 2020

IJPSS

View full text Add to dashboard Cite

show abstract

“…To check the correlation among the decomposition components, the variance inflation factor (VIF) test will be used to assess the correlation value between the decomposition components. The decomposition components will be free from multicollinearity when the value of VIF is less than 10 (Jadhav et al, 2014) . The VIF form is presented as follows: )…”

Section: Proposed Elnet-emd Methodsmentioning

confidence: 99%

Elastic Net Regression and Empirical Mode Decomposition for Enhancing the Accuracy of the Model Selection

Al-Jawarneh

Ismail

Awajan

2021

Int J Math, Eng, Manag Sci

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Elastic net (ELNET) regression is a hybrid statistical technique used for regularizing and selecting necessary predictor variables that have a strong effect on the response variable and deal with multicollinearity problem when it exists between the predictor variables. The empirical mode decomposition (EMD) algorithm is used to decompose the nonstationary and nonlinear dataset into a finite set of orthogonal intrinsic mode function components and one residual component. This study mainly aims to apply the proposed ELNET-EMD method to determine the effect of the decomposition components of multivariate time-series predictors on the response variable and tackle the multicollinearity between the decomposition components to enhance the prediction accuracy for building a fitting model. A numerical experiment and a real data application are applied. Results show that the proposed ELNET-EMD method outperforms other existing methods by capable of identifying the decomposition components that have the most significance on the response variable despite the high correlation between the decomposition components and by improving the prediction accuracy.

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“…To overcome the problem of extremely time consuming, Salibian‐Barrera and Van Aelst (2008) propose a fast and robust bootstrap method of robust model selection. Besides, Kashid and Kulkarni (2002), Dorugade and Kashid (2010) and Jadhav et al (2014) propose a general framework of model selection in the presence of outliers and multicollinearity.…”

Section: Introductionmentioning

confidence: 99%

Outlier robust model averaging based on S_p criterion

Guo

2021

Stat

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In this paper, we consider the problem of obtaining appropriate weights for model averaging in the face of model uncertainty and outliers. Most of the current model averaging methods are based on ordinary least squares (OLS) estimators, which are very sensitive to outliers or departures from the normality assumption on the error distribution. To overcome this problem, we propose an outlier robust Sp‐type model averaging (SMA) procedure whenever data contain outliers. The proposed method combines the advantages of model averaging and robust model selection. Therefore, it performs better than other classical model averaging and robust model section methods, especially in the outlier data case, and nearly the same as Mallows's model averaging (MMA) even in the clean data case. Moreover, the proposed criterion is quadratic in the weights, so it is operationally simple to implement. The performance of the proposed method is illustrated through some extensive simulation studies as well as real data examples.

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Subset selection in multiple linear regression in the presence of outlier and multicollinearity

Cited by 28 publications

References 12 publications

Correlation and Path Coefficient Analysis Studies on Grain Yield and Its Contributing Characters in Maize (Zea mays L.)

Correlation and Path Coefficient Analysis Studies on Grain Yield and Its Contributing Characters in Maize (Zea mays L.)

Elastic Net Regression and Empirical Mode Decomposition for Enhancing the Accuracy of the Model Selection

Outlier robust model averaging based on S_p criterion

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Subset selection in multiple linear regression in the presence of outlier and multicollinearity

Cited by 28 publications

References 12 publications

Correlation and Path Coefficient Analysis Studies on Grain Yield and Its Contributing Characters in Maize (Zea mays L.)

Correlation and Path Coefficient Analysis Studies on Grain Yield and Its Contributing Characters in Maize (Zea mays L.)

Elastic Net Regression and Empirical Mode Decomposition for Enhancing the Accuracy of the Model Selection

Outlier robust model averaging based on Sp criterion

Contact Info

Product

Resources

About

Outlier robust model averaging based on S_p criterion