Ridge Regression: A tool to forecast wheat area and production

Jamal, Nasir; Rind, Muhammad Qasim

doi:10.18187/pjsor.v3i2.67

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…This will be inconsistent with the theoretical definition of VIF (see, e.g. Jamal & Rind, 2007;Irfan et al, 2013;and Salmerón et al, 2013). (b) To consider that the problem of collinearity has been overcome when it has not.…”

Section: Introductionmentioning

confidence: 94%

Standardization of Variables and Collinearity Diagnostic in Ridge Regression

García

Gómez

García

et al. 2015

Int Statistical Rev

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Ridge estimation (RE) is an alternative method to ordinary least squares when there exists a collinearity problem in a linear regression model. The variance inflator factor (VIF) is applied to test if the problem exists in the original model and is also necessary after applying the ridge estimate to check if the chosen value for parameter k has mitigated the collinearity problem. This paper shows that the application of the original data when working with the ridge estimate leads to non-monotone VIF values. García et al. (2014) showed some problems with the traditional VIF used in RE.We propose an augmented VIF, VIF R .j; k/, associated with RE, which is obtained by standardizing the data before augmenting the model. The VIF R .j; k/ will coincide with the VIF associated with the ordinary least squares estimator when k D 0. The augmented VIF has the very desirable properties of being continuous, monotone in the ridge parameter and higher than one.

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Section: Introductionmentioning

confidence: 94%

Standardization of Variables and Collinearity Diagnostic in Ridge Regression

García

Gómez

García

et al. 2015

Int Statistical Rev

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“…The RR shrinks model weights to the smallest possible size via L2 regularization, to limit the model and avoid overfitting. The grid search method was applied to select the optimal α, which controls how much to regularize a model [35].…”

Section: Ridge Regressionmentioning

confidence: 99%

Determination of Internal Quality Indices in Oriental Melon Using Snapshot-Type Hyperspectral Image and Machine Learning Model

et al. 2022

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In this study, we aimed to develop a prediction model of the solid solutions concentration (SSC) and moisture content (MC) in oriental melon with snapshot-type hyperspectral imagery (Visible (VIS): 460–600 nm, 16 bands; Red-Near infrared (Red-NIR): 600–860 nm, 15 bands) using a machine learning model. The oriental melons were cultivated in a hydroponic greenhouse, Republic of Korea, and a total of 91 oriental melons that were harvested from March to April of 2022 were used as samples. The SSC and MC of the oriental melons were measured using destructive methods after taking hyperspectral imagery of the oriental melons. The reflectance spectrum obtained from the hyperspectral imagery was processed by the standard normal variate (SNV) method. Variable importance in projection (VIP) scores were used to select the bands related to SSC and MC. As a result, ten (609, 736, 561, 849, 818, 489, 754, 526, 683, and 597 nm) and six (609, 736, 561, 818, 849, and 489 nm) bands were selected for the SSC and MC, respectively. Four machine learning models, support vector regression (SVR), ridge regression (RR), K-nearest neighbors regression (K-NNR), and random forest regression (RFR), were used to develop models to predict SSC and MC, and their performances were compared. The SVR showed the best performance for predicting both the SSC and MC of the oriental melons. The SVR model achieved a relatively high accuracy with R2 values of 0.86 and 0.74 and RMSE values of 1.06 and 1.05 for SSC and MC, respectively. However, it will be necessary to carry out more experiments under various conditions, such as differing maturities of fruits and varying light sources and environments, to achieve more comprehensive predictions and apply them to monitoring robots in the future. Nevertheless, it is considered that the snapshot-type hyperspectral imagery aided by SVR would be a useful tool to predict the SSC and MC of oriental melon. In addition, if the maturity classification model for the oriental melon can be applied to fields, it could lead to less labor and result in high-quality oriental melon production.

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“…Marquardt and Snee (1975), [30] 45.751, 0.826, 0.694, 0.510, 0.309 0, 0.1, 0.2, 0.4, 0.8 Price (1977), [37] 0.134 0.2 Williams et al (1979), [40] 0.9 0.1 Timmermans (1981), [39] 0.736 0.5 Bruce and Hann(1981), [2] VIF< 1 k > 0.09 Anderson (1981), [1] 0.75 1 Garg (1984), [10] 0.98, 0.94, 0.91, 0.88, 0.80 0.04, 0.05, 0.05, 0.07, 0.07 Jamal and Rind(2007), [20] 0.946 0.16 Mardikyan and Cetin (2008), [27] 0.993 0.021 Marinoiu (2009), [28] 0.656 0.124 McDonald (2010), [32] 0.823 0.1 Das and Chatterjee (2011), [3] 0.85 0.115 Ijomah and Nwakuya (2011), [18] VIF< 1 k > 0.055 Irfan et al ( 2013), [19] 0.994 0.02 Table 2. Explanation about the existence of a matrix X k from which we have obtained…”

Section: Authors Vifs Kmentioning

confidence: 99%