Model Adequacy Checking for Applying Harmonic Regression to Assessment Quality Control

Qian, Jiahe; Li, Shuhong

doi:10.1002/ets2.12327

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…Then, learning curves, residual plots, and predicted R 2 for each metric model were calculated to clarify whether the model using the proposed method is overfitting. The predicted R 2 serves as a measure to evaluate the predictive performance of the regression model [23]. Its importance lies in identifying potential overfitting problems within the model.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

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Recursive Router Metrics Prediction Using Machine Learning-Based Node Modeling for Network Digital Replica

Hattori,

Korikawa,

Takasaki

et al. 2023

IEEE Access

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Future network infrastructures need to safely and rapidly provide network services in complex conditions that include many devices and multiple access lines, such as 5th-generation (5G) and 6th-generation (6G) mobile systems supported by multiple carriers. Additionally, future telecommunications networks will utilize network disaggregation techniques to take advantage of the highest quality technology from various vendors to meet service requirements. Therefore, it is necessary to enhance verification of combinations of various network equipment and components that constitute network infrastructure. Our motivation is to investigate the potential to enable the verification of network node performance digitally to support future network infrastructures. This study concentrates on improving the accuracy of the metric inference of black-boxed network nodes when only the network node configurations and traffic conditions are available as external conditions. Our main contribution is as follows: We provide a novel method of machine learning based on network node modeling to improve the accuracy of network node metric inference for throughput, packet loss rate, and packet delay by recursively appending inferred other node metrics to the training datasets in accordance with feature importance; we demonstrate the application of the proposed method to 14 baseline machine learning algorithms for evaluating the accuracy of inferred network node metrics; finally, we show improvement in utilization of network resources for accommodating traffic on a fixed network with a traffic policer, whose parameters are set using the proposed method. Additionally, we investigate the impact of appending inferred network node metrics to the training datasets, which is a key feature of the proposed method, on computational time and the possibility of overfitting.INDEX TERMS Recursive router metrics inference, Network node modeling, Network digital replica.In addition, network disaggregation technology [4] is pro-

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Section: Evaluation and Resultsmentioning

confidence: 99%

“…, where ŷ(i) and ȳ are the inferred value in which data point i is not included and mean measured value of router metrics y, respectively [23]. In general, overfitting happens when the model is too complex relative to the amount and noisiness of the training datasets.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

Recursive Router Metrics Prediction Using Machine Learning-Based Node Modeling for Network Digital Replica

Hattori,

Korikawa,

Takasaki

et al. 2023

IEEE Access

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show abstract

“…The differentiation of the statistic at that time was its ability to measure fit based on samples that were not used to form a model [62,63]. The statistic is a cross-validation attempt by a leave-one-out method that subtracts ŷ(i) and leaves the i-th observation out, reducing the sample size to n − 1 [64]. Repeating the subtraction and omission of every data point will lead to the sum of squares of discrepancies [65,66].…”

Section: Predicted Residual Error Sum Of Squares (Press)mentioning

confidence: 99%

Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

Untadi

et al. 2023

Axioms

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Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model’s forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applied to census and fire incident data from the South East Queensland region of Australia. A cross-validation procedure is used to assess the model’s accuracy, and comparative analyses are conducted using other elimination criteria such as p-value, Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and predicted residual error sum of squares (PRESS). The results demonstrate that the RFPE criterion is a more accurate predictive model based on several goodness-of-fit measures. Overall, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modeling of building fires.

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“…The differentiation to the statistic at that time is its ability to measure fit based on samples that are not used to form a model [55,56]. The statistic is a cross-validation attempt by a leave-one-out method that subtracts 𝑦 ̂(𝑖) and leaves the ith observation out, reducing the sample size to n -1 [57]. Repeating the subtraction and omission to every single data point will lead to the sum of squares of discrepancies [58,59].…”

Section: Predicted Residual Error Sum Of Squares (Press)mentioning

confidence: 99%

Modelling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

Untadi¹,

Li²,

Li³

et al. 2023

Preprint

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Building fires are preventable incidents that have proven to be both deadly and costly. Addressing their root causes will lead to safer neighbourhoods for families and businesses to live and operate in. Multiple studies have established the effect of residents’ socioeconomic compositions on an area’s building fire rates; however, the existing model based on the classical stepwise regression procedure has several limitations. This paper aims to construct a more accurate predictive model of building fire rates based on a set of explanatory socioeconomic variables. In building the socioeconomic model, a backward elimination by Robust Final Predictor Error (RFPE) criterion is proposed to enhance the forecasting capability of the model. The proposed method has been implemented on the census data and the fire incident data of the South East Queensland region in Australia. A cross-validation was then conducted to assess the model’s accuracy. In addition, comparative analyses of other elimination criteria, such as p-value, adjusted R-squared, Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC) and predicted residual error sum of squared (PRESS), were conducted. The cross-validation analyses demonstrate that the proposed criterion is a more accurate predictive model based on a couple of goodness-of-fit measures. All in all, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modelling of building fires.

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Model Adequacy Checking for Applying Harmonic Regression to Assessment Quality Control

Cited by 4 publications

References 39 publications

Recursive Router Metrics Prediction Using Machine Learning-Based Node Modeling for Network Digital Replica

Recursive Router Metrics Prediction Using Machine Learning-Based Node Modeling for Network Digital Replica

Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

Modelling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

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