Nonlinear Regression Approach Research Articles

A Nonlinear Quantile Regression for Accelerated Destructive Degradation Testing Data

Traditional regression approaches to accelerated destructive degradation test (ADDT) data have modeled the mean curve as representative. However, maximum likelihood estimates (MLEs) of the mean model are likely to be biased when the data are non-Gaussian or highly skewed. The median model can be an alternative for skewed degradation data. In this work, we introduce a nonlinear quantile regression (QR) approach for estimating quantile curves of ADDT data. We propose an iterative QR algorithm that uses the generalized expectation-maximization (GEM) framework to estimate the parameters of the nonlinear QR ADDT model, based on the asymmetric Laplace distribution to accommodate non-Gaussian and skewed errors. Using the asymptotic properties of the QR parameter estimates, we estimate variance-covariance matrix for the τ th QR parameters using order statistics and bootstrap methods. We propose a new prediction method of the quantile of the failure-time distribution in the normal use condition. Confidence intervals for the quantiles of the failure-time distribution are constructed using the parametric bootstrap method. The proposed model is illustrated using an industrial application and compared with the existing model. Various quantile curve estimates derived using the QR ADDT model provide a more flexible modeling framework than the traditional mean ADDT modeling approach.

Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large‐scale variables

AbstractThis paper aims to study the volatility forecasting of Chinese crude oil futures from the large‐scale variables perspective by considering both the information on international futures markets volatility and technical indicators of Chinese crude oil futures. We employ the dual feature processing method (LASSO‐PCA) by integrating least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) to extract important factors of the large‐scale exogenous variables. Besides the traditional ordinary least squares (OLS) estimation, the nonlinear support vector regression (SVR) approach is employed to integrate with the LASSO‐PCA method. The empirical results show that both the OLS and SVR combined with LASSO‐PCA can improve the forecasting accuracy, especially SVR‐LASSO‐PCA owns the best forecasting performance. The analysis of the selected variables finds international futures volatility is chosen more frequently. These results are further validated through a series of robust experiments. Finally, the time difference between China and the United States is also considered in order to obtain more reasonable out‐of‐sample forecasting.

Nonlinear Regression Approach as a Correction Factor of Measurements of Low-Cost Electrochemical Air Quality Sensors

Air quality directly affects the health of humans. The health implications of poor air quality are recognized by professionals and the public alike and these concerns have driven both the proliferation of formal sensor networks, but also low-cost sensors which can be used in the home. The advancement of technology in recent years has also led to the rapid development of low-cost sensors. Given that citizens are concerned about the air quality of the environment in which they live, they are turning to the supply of low-cost sensors, as they are affordable. The question of the reliability of measurements from low-cost sensors remains an area of research. In this research work, the optimization of ozone (O3) and nitrogen dioxide (NO2) measurements of low-cost electrochemical air quality sensors is investigated by applying nonlinear regression, using a second-order polynomial equation as a correction factor. The proposed correction method is implementable in IoT devices, as it does not require high computational resources. The results show that the measurements are susceptible to correction, with the effect that the corrected values are close to the actual values obtained by the reference instruments of the Department of Environmental Pollution Control Project of Athens (PERPA), a service of the Greek Ministry of the Environment and Energy.

Machine Learning Predictive Modeling of Agricultural Sustainability Indicators

<p>Modern-day researchers are provided with data abundance that has its drawback: increased analysis complexity. Approaching this issue through traditional data analysis techniques provides only partial solutions to the complex situation. This research offers analytical and predictive models based on machine‐learning algorithms (linear regression, random forest, and generalized additive model) that can be used to assess and improve the Common Agricultural Policy (CAP) impact over agricultural sustainability in European Union (EU) countries, providing the identification of proper instruments that can be adopted by EU policymakers and CAP Council in financial management of the policy. The chosen methodology elaborates custom‐developed models based on a dataset containing 22 relevant indicators, considering three main dimensions contributing to the EU sustainable agriculture development goals in the CAP context: social, environment, and economic. The results showed that sustainable agriculture parameters influenced by the relevant indicators could be modeled with both linear and non-linear regression approaches by utilization of real-time data using machine learning. The predictive analytic models provide satisfactory performance and could be adopted by researchers and practitioners as policy impact monitoring and controlling tools, not only the EU but also for other countries that have or plan to adopt similar agricultural policies.</p><p><strong>Keywords: </strong>Agricultural policies, common agricultural policy, machine learning, rural development, sustainable agriculture</p>

Comparing the impact of atezolizumab plus bevacizumab and lenvatinib on the liver function in hepatocellular carcinoma patients: A mixed-effects regression model approach.

This retrospective study compared the impact of atezolizumab plus bevacizumab (Atez/Bev) and lenvatinib (LEN) on the liver function in patients with hepatocellular carcinoma. We included 526 patients who received Atez/Bev and 731 who received LEN March 2018 and July 2022 in this study. We conducted a 1:1 propensity-score-matched analysis and identified 324 patients in each group for inclusion in the present analysis. Nonlinear mixed-effects regression models were employed, allowing for the evaluation and inclusion of cases where treatment was interrupted due to disease progression, adverse events, or loss to follow-up. These models were used to compare the ALBI score between the Atez/Bev and LEN groups. Following propensity score matching, the mean ALBI scores in the Atez/Bev and LEN groups were -2.41 ± 0.40 and -2.44 ± 0.42 at baseline, and -2.17 ± 0.56 and -2.19 ± 0.58 at 12 weeks, respectively. Although the ALBI score significantly worsened during treatment in both groups (p < 0.001), there was no significant difference in the rate of ALBI score deterioration between the groups (p = 0.06). Subgroup analyses showed that LEN-treated patients with BCLC advanced stage (p = 0.02) and those who initially received the full dose (p < 0.001) had a significantly greater worsening of ALBI score compared to Atez/Bev. Using a nonlinear mixed-effects regression approach, which allowed for the inclusion of cases with treatment interruption, we found no significant difference in the trend of liver function deterioration between the Atez/Bev and LEN groups. Caution should be exercised for LEN-treated patients with BCLC advanced stage or those receiving the full dose of LEN.

Visible and near-infrared spectroscopic determination of sugarcane chlorophyll content using a modified wavelength selection method for multivariate calibration

Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220–1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.

Spline functions and nonlinear models for modeling the growth of sheep in Northeast Brazil

Modeling animal growth is essential to manage quality meat production systems at a low cost. This study aimed to compare the linear-linear, linear-plateau, and linear-linear-linear spline functions with the nonlinear models Brody, Richards, and von Bertalanffy to model the growth of crossbred sheep in the Northeast of Brazil. Weighing data from the genetic groups of meat sheep Dorper x Morada Nova, Dorper x Rabo Largo, and Dorper x Santa Inês were used. Knots in the spline fitting were considered unknown and estimated under the segmented nonlinear regression approach. Overall, splines were more accurate than traditional nonlinear models in describing the growth of sheep of all genetic groups. The linear-plateau spline was superior in predicting weight as a function of the age of sheep. The linear-plateau function also resulted in estimates of parameters (growth rates, time for animals to reach adult weight, and adult weight) with easier biological interpretations, which can be used in practice for decision-making for herd management.

Do industrial robots reduce carbon intensity? The role of natural resource rents and corruption control.

Although the research on the impact of robotics on carbon emissions is increasing, there are still relatively few studies on the impact of robots on carbon intensity from the perspective of natural resources and corruption. In order to fill in the research gaps, panel data from 66 countries between 1993 and 2018 are collected, and linear and nonlinear panel regression approaches are developed. Natural resource rent and corruption control are used as threshold variables, robot penetrationis used as explanatory variables, and carbon emission intensity is the explained variable. The results of the linear model show that robot penetration is negatively correlated with carbon emission intensity, which means that robot penetration reduces carbon emission intensity. The results of the nonlinear model show that when natural resource rents and corruption control are used as thresholds, the relationship between robot penetration and carbon emission intensity presents a U shape and an inverted U shape, respectively. Specifically,the threshold for natural resource rents is 4.7%. When the natural resource rent is lower than this threshold, the robot penetration rate reduces the carbon emission intensity, but when the natural resource rent is higher than this threshold, the robot penetration rate increases the carbon emission intensity. The threshold value of corruption control is -0.4349. When the corruption control is lower than this threshold, the robot penetration rate increases the carbon emission intensity. If the corruption control is higher than this threshold, the robot reduces the carbon emission intensity. Finally, policy recommendations for better use of robotics to reduce carbon emission intensity are put forward from the perspective of natural resource rent and corruption control.

The nonlinear impact of technologies import on industrial employment: A panel threshold regression approach

This study aims to obtain empirical evidence of the existence of a nonlinear effect of imported technology on industrial employment, which has been largely overlooked in the literature. To achieve this objective, this article employed panel data models using non-linear panel threshold regression (PTR) approaches for a sample of developed and developing countries over the period of technological advancement from 2000 to 2019. Several conclusions were drawn from this study. Firstly, the use of a regime-switching test based on the Hansen approach confirmed the existence of a nonlinear relationship between technology imports and industrial employment. Secondly, the analysis demonstrated that the threshold and its impact vary depending on the level of economic development. In addition, the results showed a weak positive impact for both developed and developing countries. However, this positive effect was more pronounced in developed countries, mainly due to the relative efficiency of their compensatory mechanisms. This efficiency is dependent on the institutional and economic factors of the countries. Therefore, special attention should be given to these factors when formulating economic policies and innovation strategies.

The influence of drain pipe location and diameter on seepage through an earth dam

Earth dams are built for various reasons these including storage of water, regulation of water flow, prevention of flooding, and the generation of hydroelectric power. The drain pipes within an earth dam, are considered one of the more important control devices used to reduce the effects of problems caused by seepage. As such, the role of drain pipes against seepage problems in an earth dam, has been studied numerically in this investigation. The effects of drain pipe location and their diameters (50, 75, 100, and 150 mm), were examined using different hydraulic heads. The analysis focuses on the characteristics of relative seepage flow, exit gradient, and factor of safety regarding sliding. A range of models of typical earth dams, with and without drain pipes, have been simulated using Geo-Studio software in SEEP/W and SLOPE/W applications, examining more than 200 different cases. By using the data computed from numerical models, new formulas for predicting the seepage parameters and factors of safety were developed in this study by using the multiple non-linear regression (NLR) approaches. The findings show that the presence of a drain pipe reduces seepage flow and the exit gradient, at the same time increasing the factor of safety. Regarding pipe diameter, the rate of seepage and exit gradient decreased while the factor of safety increased as the diameter of the pipe increased. The factor of safety increased by between 11 and 25% when the drain pipe was placed in an optimal position, allowing for a reduction in seepage flow and exit gradient.

An Efficient Experimental Model to Estimate the Performance of the Raise Borer Drilling Machine Using Linear and Nonlinear Regression Approaches in the Azad Dam in Iran

An Efficient Experimental Model to Estimate the Performance of the Raise Borer Drilling Machine Using Linear and Nonlinear Regression Approaches in the Azad Dam in Iran

Rheological Study of Formate (Nacl) and Ethylene Glycol Based Drilling Fluid Using MATLAB

Non-Newtonian models are used in describing the rheology of drilling fluids. Models, such as Power Law, Bingham Plastic, and the Hershel-Buckley model are used to predict drilling fluid behaviour. Selection of the best rheological model that accurately represents the shear stress-shear rate data is optimal in determining fluid use and predicting realistic rheological behaviour. This research is aimed at studying the best rheology model to fit collected data from sodium chloride (NaCl) and ethylene glycol (EG) based drilling fluid. The assessment utilized a total of ten sets of experimental viscometry data from laboratory-formulated drilling fluid samples A to J with the varying volume of formate sodium chloride (NaCl) and ethylene glycol (EG). Using Equations relating shear rates to shear stress, MATLAB non-linear regression approach was used to determine the best of the three rheological models to fit the experimental. The Root square (R2), Adjusted Root square (Adj R2), and Root Mean Square Error (RMSE) were used in determining the goodness of fit and degree of deviation of the experimental data. data. High concentrations of both NaCl and ethylene glycols in the formulation provide a better shear rate shear stress fit. Although the ethylene glycol presence may have account for the improved fit, as Sample E, F and G despite containing high volume of the formate NaCl, the data where not effectively fitted by the models. From the results, the power law model was the least suitable with an R2 value of 0.881-0.956, Adj R2 83-96%, and a poor RSME in the range 2-9. The Herschel-Bulkley model showed an improved fit for the fluids with an R2 0.897-0.973, Adj R287- 98%, and a better RSME &lt;3. this model also shows a good shear thinning behaviour from its three parameter consistency (n) index values. The Bingham plastic model best fits the rheology of the drilling fluid data R2 0.93-0.99, Adj R2 93-98%, and RSME of &lt;2.

National Bombay Stock Trade Market Expectation Utilizing KNN Calculation

Predicting stock prices is a fascinating and difficult area of study. The economies of developed nations are compared to their power economies. As a result of their easy profits and low risk rate of return, stock markets are currently regarded as an illustrious trading field. For researchers in data mining and business, the stock market is thought to be a good place to work because of its numerous and constantly changing information sources. In order to assist investors, management, decision-makers, and users in making correct and informed investment decisions, we used the k-nearest neighbour algorithm and a non-linear regression approach in this paper to predict stock prices for a sample of six major companies listed on the Jordanian stock exchange. The results indicate that the KNN algorithm is reliable with a low error ratio; Consequently, the outcomes were reasonable and rational. Additionally, the prediction results were nearly identical to the actual stock prices, depending on the data on the stock price.

Biodiesel composition based machine learning approaches to predict engine fuel properties

Recently data-driven machine learning approaches received considerable attention in several applications, including developing models to predict engine fuel properties of biodiesel. Multilinear regression (MLR) is the most straightforward method among the available approaches in the literature to predict biodiesel properties. However, a nonlinear correlation between biodiesel composition and properties cannot be modeled using the MLR approach, resulting in poor predictability. Artificial neural network (ANN), the most explored nonlinear regression approach to predict biodiesel properties, is prone to poor reproducibility. Support Vector Machine (SVM) regression is a machine learning approach that can be applied to develop biodiesel property prediction models whose favorable features include nonlinear data modeling. Models are developed to correlate biodiesel composition with calorific value, viscosity, and cetane number in the present work using ANN and SVM regression. Seventy biodiesels with varying compositions are used for calibrating the models and 30 other biodiesels for validation. Both the nonlinear regression approaches perform well in predicting the biodiesel properties, among which SVM results in better prediction than ANN. SVM resulted in models with a MAPE of 0.26%, 1.07%, and 1.69%, respectively, for calorific value, viscosity and cetane number of biodiesels which are considerably lower than those predicted using the literature models.

Still no Dunning-Kruger effect: A reply to Hiller

The primary criticism raised by Hiller (2023) pertained to the transformation of the self-assessed intelligence (SAI) scores applied by Gignac and Zajenkowski (2020), an investigation that suggested the Dunning-Kruger effect (DKE) may be primarily a statistical artefact. Hiller recommended an alternative transformation. Our re-analysis of Gignac and Zajenkowski's data with Hiller's recommended transformation failed to find evidence in favour of the DK hypothesis. Hiller also recommended SAI measurement with a percentile-based approach. Based on a review of the literature, one investigation has employed a percentile-based approach to SAI measurement, and it largely failed to support the DK hypothesis, when analysed with the non-linear regression approach recommended by Gignac and Zajenkowski. We conclude by encouraging researchers to continue to derive novel approaches to testing the DK hypothesis, especially approaches that overcome known methodological challenges.

Linear and Nonlinear Land Use Regression Approach for Modelling PM2.5 Concentration in Ulaanbaatar, Mongolia during Peak Hours

In recent decades, air pollution in Ulaanbaatar has become a challenge regarding the health of the citizens of Ulaanbaatar, due to coal combustion in the ger area. Households burn fuel for cooking and to warm their houses in the morning and evening. This creates a difference between daytime and nighttime air pollution levels. The accurate mapping of air pollution and assessment of exposure to air pollution have thus become important study objects for researchers. The city center is where most air quality monitoring stations are located, but they are unable to monitor every residential region, particularly the ger area, which is where most particulate matter pollution originates. Due to this circumstance, it is difficult to construct an LUR model for the entire capital city’s residential region. This study aims to map peak PM2.5 dispersion during the day using the Linear and Nonlinear Land Use Regression (LUR) model (Multi-Linear Regression Model (MLRM) and Generalized Additive Model (GAM)) for Ulaanbaatar, with monitoring station measurements and mobile device (DUST TRUK II) measurements. LUR models are frequently used to map small-scale spatial variations in element levels for various types of air pollution, based on measurements and geographical predictors. PM2.5 measurement data were collected and analyzed in the R statistical software and ArcGIS. The results showed the dispersion map MLRM R2 = 0.84, adjusted R2 = 0.83, RMSE = 53.25 µg/m3 and GAM R2 = 0.89, and adjusted R2 = 0.87, RMSE = 44 µg/m3. In order to validate the models, the LOOCV technique was run on both the MLRM and GAM. Their performance was also high, with LOOCV R2 = 0.83, RMSE = 55.6 µg/m3, MAE = 38.7 µg/m3, and GAM LOOCV R2 = 0.77, RMSE = 65.5 µg/m3, MAE = 47.7 µg/m3. From these results, the LUR model’s performance is high, especially the GAM model, which works better than MRLM.

Meteorological hazard risk assessment of offshore wind power grids based on CVAR method

Wind energy is a clean, renewable and green energy. Compared to land-based wind power, offshore wind power does not occupy land resources. Meanwhile offshore wind resources is much richer and its wind direction is more stable. Meteorological hazards have a major impact on offshore wind facilities. Typhoons, lightning, rainstorms and other meteorological hazards that occur during operation can not only cause fatal damage to offshore wind facilities, but often also trigger a chain reaction of marine hazards, making them more difficult to maintain. In this paper, we analyze the impact of various types of meteorological hazards on offshore wind facilities with examples and select the most important ones. A comprehensive risk assessment model CVAR for offshore wind projects is developed in combination with other types of risk elements to predict the damaging of offshore wind facilities so that managers can respond in a timely manner. In addition, A nonlinear stepwise regression (NLSR) approach is then used to forecast the meteorological elements in offshore grid facilities.

How Does the Fed Make Decisions: A Machine Learning Augmented Taylor Rule

The Federal funds rate is a cornerstone of asset pricing that has a significant impact on asset valuation and portfolio performance. However, estimating it reliably can be a challenging issue given that the FOMC makes monetary policy decisions based on complex economic conditions. The authors leveraged existing literatures’ findings on factors and combined those major factor categories into the new model, which includes inflation, labor markets, financial condition, and proxy of global market, and the authors selected the optimal data series to optimize the effectiveness of detecting Fed decisions through a classification factor selection process. Also, the authors improved the regression from fixed coefficients to gradient boosting nonlinear regression approach to reflect the dynamic interconnections among all the factors and their lags through different periods. Upon conducting out-of-sample forecasting, with these selected factors and machine learning gradient boosting regression, the out-of-sample RMSE improved by 77% from traditional Taylor rule model, which offered an alternative robust solution for forecasting the Federal fund rates that can be further applied to asset pricing and investing.

Development of Prediction Models for the Torsion Capacity of Reinforced Concrete Beams Using M5P and Nonlinear Regression Models

Torsional strength is related with one of the most critical failure types for the design and assessment of reinforced concrete (RC) members due to the complexity of the associated stress state and low ductility. Previous studies have shown that reliable methods to predict the torsional strength of RC beams are still needed, namely for over-reinforced and high-strength RC beams. This research aims to offer a novel set of models to predict the torsional strength of RC beams with a wide range of design attributes and geometries by using advanced M5P tree and nonlinear regression models. For this, a broad database with 202 experimental tests is used to generate highly reliable and resilient models. To build the models, three independent variables related with the properties of the RC beams are considered: concrete cross-section area (area enclosed within the outer perimeter of the cross-section), concrete compressive strength, and torsional reinforcement factor (which accounts for the type—longitudinal or transverse—amount, and yielding strength of the torsional reinforcement). In contrast to multiple nonlinear regression approaches, the findings show that the M5P tree approach has the best estimation in terms of both accuracy and safety. Furthermore, M5P model predictions are far more accurate and safer than the most prevalent design equations. Finally, sensitivity and parametric studies are used to confirm the robustness of the presented models.

Support vector machine (SVM) approach to develop the discharge prediction model for triangular labyrinth weir

Abstract Most of the studies on labyrinth weir were carried out in the laboratory, and regression models have been developed for discharge coefficient in terms of pertinent independent parameters. It is difficult to obtain an exact analytical solution to the head discharge relationship due to the existence of 3D flow. Consequently, various forms of soft computing techniques are used as an appropriate alternative to achieve greater accuracy in developing a discharge prediction model. In the present study, support vector regression (SVR) has, therefore, been implemented to develop a discharge coefficient prediction model for a triangular labyrinth (TL) weir using a sizeable amount of laboratory data available in the literature. An attempt has also been made to obtain a simple discharge coefficient equation using the same data based on the non-linear regression (NLR) approach for field application. A comparative study has been carried out to assess the accuracy of the discharge coefficient models obtained in the present study and those reported in the literature. Sensitivity analysis has been made to study the influence of individual parameters on the discharge coefficient. The accuracy of different discharge coefficient prediction models was also tested for the data of prototype labyrinth weir and appropriate models were recommended for the field application.