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Accurate solar radiation forecasting is essential for optimizing the design, operation, and management of solar power systems. It ensures reliable energy trading, efficient maintenance scheduling and stable power generation. Deep learning (DL) models, known for capturing complex nonlinear relationships, have demonstrated potential in solar irradiance prediction. However, limitations such as data variability and privacy concerns persist. To overcome these challenges, this study introduces a novel hybrid DL model that combines self-attention, bidirectional long short-term memory, and bidirectional gated recurrent unit architectures. The model is trained using features selected through recursive feature elimination and ridge regression, ensuring the most relevant data is utilized for prediction. Using ground-measured global horizontal irradiance dataset, the proposed SA-Bi-LSTM-Bi-GRU model achieves exceptional forecasting performance. It significantly reduces MAE by 64.40–90.34 W/m2, RMSE by 16.15–57.30% W/m2, MAPE by 41.38–69.78%, NMRSE by 8.29–21.65% W/m2 compared to considered standalone and hybrid models, while achieving an R2 of 0.98. These improvements underscore the model's ability to deliver precise and reliable forecasts, enhancing grid stability and energy efficiency. By integrating advanced architectures and achieving substantial performance gains, this model offers a groundbreaking solution for sustainable solar energy management. [ABSTRACT FROM AUTHOR]

Ridge regression is an effective tool to handle multicollinearity in regressions. It is also an essential type of shrinkage and regularization methods and is widely used in big data and distributed data applications. The divide and conquer trick, which combines the estimator in each subset with equal weight, is commonly applied in distributed data. To overcome multicollinearity and improve estimation accuracy in the presence of distributed data, we propose a Mallows-type model averaging method for ridge regressions, which combines estimators from all subsets. Our method is proved to be asymptotically optimal allowing the number of subsets and the dimension of variables to be divergent. The consistency of the resultant weight estimators tending to the theoretically optimal weights is also derived. Furthermore, the asymptotic normality of the model averaging estimator is demonstrated. Our simulation study and real data analysis show that the proposed model averaging method often performs better than commonly used model selection and model averaging methods in distributed data cases. [ABSTRACT FROM AUTHOR]

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Reliable inflation forecasts are essential for both business operations and macroeconomic policy making. This study explores the potential of using machine learning (ML) techniques to improve the accuracy of human forecasts of inflation. Specifically, we develop and examine ML-centered forecast adjustment procedures where advanced ML techniques are employed to predict and thus mitigate the errors of human forecasts, akin to how an AI-powered spell and grammar checker helps to prevent mistakes in human writing. Our empirical exercises demonstrate the benefits of several popular ML techniques, such as the elastic net, LASSO, and ridge regressions, and provide evidence of their ability to improve both our own benchmark inflation forecasts and those reported by the frequent participants in the US Survey of Professional Forecasters. The forecast adjustment procedures proposed in this paper are conceptually appealing, widely applicable, and empirically effective in reducing forecast bias and improving forecast accuracy. [ABSTRACT FROM AUTHOR]

In the current article, we develop the Aslam-Ahmad estimator i.e. the modified Liu-ridge-type estimator as proposed by Aslam and Ahmad [Aslam M, Ahmad S. The modified Liu-ridge-type estimator: a new class of biased estimators to address multicollinearity. Commun Statist Simul Comput. 2022] for the Cox proportional hazards regression model. It is common to use the maximum partial likelihood approach for the parameter estimation of the Cox proportional hazards model. However, when there is considerable multicollinearity among the regressors, this estimator is unable to give stable estimates for the unknown regression coefficients. To vanquish the problem, the literature suggests many alternative biased estimators. Following this, we propose to tailor the Aslam-Ahmad estimator for the Cox proportional hazards model and define its properties. The performance of the proposed estimator with the available estimators has been evaluated by the Monte Carlo simulations using the scalar mean squared error criterion. A numerical example has also been provided for the illustration. [ABSTRACT FROM AUTHOR]

Cities are of wide concern to scholars due to their major share of global carbon emissions. Energy-related carbon emissions differ significantly among cities, especially megacities, due to regional heterogeneity in socioeconomic conditions. To analyze the differences in influencing factors on carbon emissions within megacities, and further target emission reductions, measures were developed. Beijing was selected to investigate influencing factor differences in the core zones, developing zones and ecological zones using the STIRPAT model on the county level. The results show the following: (1) Regional heterogeneity existed in Beijing in carbon emissions changes from 2010 to 2022. The carbon emissions of the core zones grew steadily and were demonstrated as a major part of Beijing. (2) There were variations in the influencing factors of emissions. Population size was the major driving factor of emissions in the core zones, while emissions in the developing zones and ecological zones were driven primarily by GDP per capita. Notably, urbanization promoted the increase in carbon emissions in the developing zones, but had a negative influence on emissions in the ecological zones. The energy intensity was the primary negative force of carbon emissions in the three zones. (3) The differences in population, economic scale, industrial structure and technological level lead to the heterogeneity of carbon emissions in Beijing. The three zones should formulate targeted emission reduction measures based on the primary factors of carbon emissions and their functional positioning. [ABSTRACT FROM AUTHOR]

This study aimed to build a valid model to predict maximal weightlifting competition performance using ordinary least squares linear regression (OLR) and penalized (Ridge) linear regression (penLR) in 29 elite male weightlifters. One repetition maximum (1RM) or 3RM test results of assistant exercises were used as predictors. Maximal performance data of competition and assistant exercises were collected during a macrocycle in preparation for a competition. One repetition maximum snatch pull, 3RM back squat, 1RM overhead press, and body mass were used to predict the 1RM snatch; and 1RM clean pull, 3RM front squat, 1RM overhead press, and body mass were used to predict the 1RM clean and jerk. Model validation was performed using cross-validation (CV) and external validation (EV; random unknown dataset) for the coefficient of determination and root mean square error (RMSE). Results revealed that penLR models present more plausible output in the relative importance of highly correlated predictors. Of note, the 1RM snatch pull is the most relevant predictor for the 1RM snatch, whereas the 1RM clean pull and 3RM front squat are the most relevant predictors for the 1RM clean and jerk. Validation-based absolute predictive error (RMSE) ranged between ≈ 3-9 kg for the 1RM snatch and ≈ 3-7 kg for the 1RM clean and jerk, depending on the model (OLR vs. penLR) and validation procedure (CV vs. EV). In conclusion, penLR models should be used over OLR models to analyze highly correlated predictors because of more plausible model coefficients and smaller predictive errors. [ABSTRACT FROM AUTHOR]

Recent studies focus on modelling count data, which often shows overdispersion or underdispersion. The Conway–Maxwell–Poisson (COMP) regression model effectively handles such dispersion. However, multicollinearity can negatively impact the maximum likelihood estimator (MLE) by increasing variance. To address this, biased estimators like the ridge estimator have been suggested to mitigate multicollinearity effects. This research proposes a new COMP hybrid estimator to further address multicollinearity issues. Theoretical comparisons between the COMP hybrid estimator, MLE, and other COMP estimators reveal that the hybrid estimator reduces mean squared error. Monte Carlo simulations and practical applications confirm that these proposed estimators outperform both MLE and existing COMP estimators. [ABSTRACT FROM AUTHOR]

Ridge regression was originally proposed as an alternative to ordinary least-squares regression to address multicollinearity in linear regression and was later extended to logistic and Cox regressions. The ridge estimator is interpreted as the Bayesian posterior mean or median in the Bayesian framework when the regression coefficients have multivariate normal priors. We previously proposed using vine copula-based joint priors on regression coefficients in linear and Cox regressions, including an interaction that promotes the use of ridge regression because the interaction term can result in multicollinearity. We showed that vine copula-based priors improve the estimation accuracy over the multivariate normal prior, and they would be a promising approach in other regression types, such as logistic regression. In this study, we focus on a case involving two covariates and their interaction terms, and propose a vine copula-based prior for Bayesian ridge estimators under a logistic model. Simulation and data analysis results show that Archimedean (Clayton and Gumbel) copula priors are superior to other priors (the Gaussian copula and trivariate normal priors) in the presence of multicollinearity. [ABSTRACT FROM AUTHOR]

This study investigates high-performance models for predicting the Weapon Engagement Zone (WEZ) in beyond-visual-range (BVR) air combat scenarios. Accurate WEZ predictions are crucial for decision-making in air combat, and high-performance solutions are essential for developing and deploying autonomous systems. To optimize the model training process, we introduce novel feature engineering and data augmentation strategies, achieving a 70% improvement in the Mean Absolute Error (MAE) of WEZ predictions. A comparison of various regression methods highlights the potential of polynomial-based alternatives when fully utilized. In our evaluations, Polynomial Regression (PR) with higher interaction degrees outperforms more complex machine learning models in prediction accuracy and computational efficiency. For instance, Lasso regression, a PR method with regularization, achieves results that are 33% better and 2.1 times faster than the best artificial neural network-based solution. Our results challenge common assumptions in the literature about the complexity and feasibility of higher-order PR solutions, suggesting that they can be a compelling alternative for various challenges across domains. This study also provides a new open dataset to facilitate further research and advancements in this field.

Climate transition zones are ecologically sensitive regions that respond to changes in complex natural conditions. Analyzing the spatiotemporal evolution trends and impact factors of landscape ecological risk is crucial for maintaining regional ecosystem security. However, research predominantly focused on the past analytical paradigm, which often needed more strategic predictions for future scenarios tailored to diverse developmental requirements. This study analyzed land use changes in the Huai River Basin during 2000, 2010, and 2020 and used the Future Land Use Simulation model to conduct a multi-scenario simulation for 2030. Subsequently, this study assessed the landscape ecological risk from 2000 to 2030 and analyzed the influencing mechanisms using the ridge regression model. The results showed that: (1) The primary transitions were concentrated between cropland and construction land. By 2030, the area of construction land was projected to continue to expand, with the greatest increase of 2906 km2 anticipated in the natural development scenario. (2) The overall spatial pattern of landscape ecological risk showed a "high in the east and low in the west" distribution, with the lowest risk areas predominating (accounting for over 43%). Over the past 20 years, the risk initially increased and then decreased, and by 2030, the risk was expected to decline further. (3) The risk exhibited significant positive spatial autocorrelation. By 2030, the constraint of spatial location on risk distribution would decrease. Local spatial clustering was mainly characterized by "Low-Low" regions (accounting for 20%). (4) Vegetation cover consistently correlated negatively with ecological risk and was the most influential factor, with relative contribution rates all exceeding 21%. The findings have provided a scientific reference for the ecological and environmental management of areas with intense human activity under complex climatic conditions. [ABSTRACT FROM AUTHOR]

Classification as an essential part of Machine Learning and Data Mining has significant roles in engineering, medicine, agriculture, military, etc. With the evolution of data collection tools and the unceasing efforts of researchers, new datasets with huge dimensions are obtained so that each data sample has multiple labels. This kind of classification is called Multi-Class Classification (MLC) and demands new techniques to predict the set of labels for a data instance. To date, a variety of methods have been proposed to solve MLC problems. However, new high-dimensional datasets with challenging patterns are being developed, making it necessary for new research to be conducted to develop more efficient methods. This paper presents a novel framework named QLHA to solve MLC problems more efficiently. In the QLHA, the Principal Label Space Transformation (PLST) and Ridge Regression (RR) are recruited to predict the labels of data. Next, an effective objective function is introduced. Also, a hybrid metaheuristic algorithm called QGTOJS is provided to optimize objective value and enhance the predicted labels by selecting the most relevant features. In the QGTOJS, the Gorilla Troops Optimization (GTO) and Jellyfish Search algorithm (JS) are binarized and hybridized through a modified variant of the Q-learning algorithm. Besides, an adjusted Hill Climbing strategy is adopted to balance the exploration and exploitation and improve local optima departure. Likewise, a local search mechanism is provided to enhance searchability as much as possible. Eventually, the QLHA is applied to ten multi-label datasets and the obtained results are compared with heuristic and metaheuristic-based MLC methods numerically and visually. The experimental results disclosed the effectiveness of the contributions and superiority of the QLHA over competitors. [ABSTRACT FROM AUTHOR]

The popularity of genomic selection as an efficient and cost-effective approach to estimate breeding values continues to increase, due in part to the significant saving in genotyping. Ridge regression is one of the most popular methods used for genomic prediction; however, its efficiency (in terms of prediction performance) depends on the appropriate tunning of the penalization parameter. In this paper we propose a novel, more efficient method to select the optimal penalization parameter for Ridge regression. We compared the proposed method with the conventional method to select the penalization parameter in 14 real data sets and we found that in 13 of these, the proposed method outperformed the conventional method and across data sets the gains in prediction accuracy in terms of Pearson's correlation was of 56.15%, with not-gains observed in terms of normalized mean square error. Finally, our results show evidence of the potential of the proposed method, and we encourage its adoption to improve the selection of candidate lines in the context of plant breeding. [ABSTRACT FROM AUTHOR]

Poisson and negative binomial regressions are popular methods for modelling the relationship between a count variable and explanatory variables. In the presence of multicollinearity, ridge regression is an alternative to the maximum likelihood estimation for regression coefficients. Furthermore, ridge estimators are interpreted as the Bayesian posterior mean (or mode) when the regression coefficients follow a multivariate normal prior. However, using the multivariate normal prior may not effectively estimate regression coefficients, especially in the presence of interaction terms. This study proposes vine copula-based priors for Bayesian ridge estimators in Poisson and negative binomial regression models. The simulations and data analysis results indicate that for the Poisson model with equidispersion and the negative binomial model with overdispersion, the Clayton and Gumbel copula priors of the Archimedean family achieve superior performance than the multivariate normal prior and Gaussian copula prior. [ABSTRACT FROM AUTHOR]

In this article, we present the Liu estimator for the Cox proportional hazards (PH) model. The maximum partial likelihood estimator (MPLE) is commonly used for estimation of the coefficients of the Cox PH model. The MPLE performs well if the covariates are uncorrelated. However, in many situations, covariates become seriously correlated, and then the MPLE is inept to produce stable estimates for the unknown coefficients. To overcome this situation, the literature suggests using the ridge estimator as an alternative to the MPLE for the Cox PH model in the presence of multicollinearity. In the present article, we extend the Liu estimator, a popular superseder of the ridge estimator, for the Cox PH model and discuss its properties. The performance of the proposed estimator has been compared with the available estimators using the scalar mean squared error criterion through the Monte Carlo simulations. A numerical example has also been provided for the illustration. [ABSTRACT FROM AUTHOR]

The primary objective of the paper is to predict the price of the "Toyota Cars" based on the requirement of the customer. The primary need is to identify the minimum price of the "Toyota Cars" based on the 37 key attributes/factors. The levels of each factor in the data set varies from 2 to 312, moreover the data sets used for the analysis, contain both feature as well as numerical based data. The data sets are taken from the "KAGGLE" an open-source real time data provider. The total number of data available for the analysis is 1442, since the data are large and the factors are more with different levels, the prediction done using the Machine Learning (ML) approach. Ridge regression, a supervised machine learning algorithm is used to predict the sales price of the "Toyota Cars", thereby identifying the minimum price based on different combinations of attributes. The prime novelty lies on the usage of the Ridge regression, a linear regression algorithm for the prediction process. The ridge regression model was developed by means of the Python programming and Google Colab, an open-source online compiler for the ML was used for the code compilation process. Based on the result, the minimum price was found to be 6520.8 dollars, with the basic customer requirements such as Model: Corolla 1.6-16v, Type: Sedan, 4 Doors, CNG, 69hp, 1854 cc, with air conditioner, centre lock and power steering. [ABSTRACT FROM AUTHOR]

Vegetation phenology is highly sensitive to climate change, and an examination of vegetation phenology across diverse climatic conditions is crucial for identifying key factors influencing vegetation dynamics. However, there is a significant lack of macroscopic research and quantitative assessments on the response of vegetation phenology to meteorological factors in large-scale zones. This study employed Whittaker filtering and dynamic thresholding to extract phenological parameters of vegetation in China. Trend analysis was used to investigate the spatiotemporal changes in vegetation phenology from 1982 to 2022, while partial correlation and ridge regression analysis were conducted to quantify the response of vegetation in different zones to meteorological factors. The findings of this study demonstrate that over the past four decades, the start of the growing season (SOS) of vegetation in China has progressively advanced annually, whereas the end of the growing season (EOS) has progressively delayed annually, leading to an annual increase in the length of the growing season (LOS). Notably, these changes exhibit significant spatial variations. The response of vegetation phenology to temperature and precipitation is relatively complex and is closely related to local climatic conditions, humidity, vegetation type, etc. Different zones and diverse vegetation types have very different sensitivities to the same meteorological factor, sometimes even demonstrating contrasting responses. Consequently, this study is expected to clarify the response relationship between different vegetation ecosystems and meteorological factors in large-scale areas. [ABSTRACT FROM AUTHOR]

The global shift towards electric vehicles (EVs) underscores the critical need for reliable battery performance and safety. Lithium-ion batteries, particularly Li-NMC (lithium nickel manganese cobalt oxide), are widely adopted for their balanced functional and performance characteristics. However, the advancement of batteries with higher nickel content and reduced manganese and cobalt introduces challenges, including increased susceptibility to thermal runaway and degradation, especially under abusive conditions like over-discharge. This study addresses significant research gaps by developing a machine learning (ML) algorithm for the early detection and predictive maintenance of over-discharged Li-NMC batteries. Current methods often fail to identify and mitigate the effects of continuous cycling, which can release harmful free radicals such as singlet oxygen (1 O 2 ) and superoxide ( O 2 - ) that accelerate degradation. Our ML approach utilizes supervised learning, feature engineering, and model optimization, leveraging key input features such as voltage, time, and cycle count which are derived from extensive battery life testing. To validate our model, we conducted scanning electron microscopy energy-dispersive spectroscopy (SEM–EDS), galvanostatic charge–discharge (GCD) tests, and rate capability tests. The proposed ridge regression model achieved a mean absolute error (MAE) of 0.11422%, a mean squared error (MSE) of 0.02313%, and an R-squared (R2) value of 0.99, outperforming other models such as Decision Trees (DT), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Gradient Boosting (GB), and Lasso Regression. Our model addresses key shortcomings of existing methods, particularly in predicting degradation in precycled batteries subjected to fault induction. The insights gained contribute to a robust control strategy for EV battery management, enabling proactive maintenance, timely battery replacement, and enhanced system reliability and safety, effectively addressing the long-term challenges in battery health management. [ABSTRACT FROM AUTHOR]

Data analysis is very important in many fields of science. The most preferred methods in data analysis is linear regression due to its simplicity to interpret and ease of application. One of the assumptions accepted while obtaining linear regression is that there is no correlation between the independent variables in the model which refers to absence of multicollinearity. As a result of multicollinearity, the variance of the parameter estimates will be high and this reduces the accuracy and reliability of the linear models. Shrinkage methods aim to handle the multicollinearity problem by minimizing the variance of the estimators in linear model. Ridge Regression, Lasso, and Elastic-Net methods are applied to different simulated data sets with different characteristics and also real world data sets. Based on performance results, the methods are compared according to multi-criteria decision making method named TOPSIS, and the order of preference is determined for each data set. [ABSTRACT FROM AUTHOR]

Air pollution is defined as an adverse event that negatively affects ecosystems and standard conditions necessary for human survival and progress, manifested by certain substances in the atmosphere exceeding specific concentration levels. The control of air pollution is a significant strategic task related to the national economies and the well-being of the people. In the face of increasingly severe environmental pollution problems, accurately predicting air pollution indicators becomes crucial. Among the popular air pollution prediction methods, the K-nearest neighbors (KNN) appears to be one of most promising approaches. In this paper, we develop a novel KNN rule that combines the ridge estimators called KNN-ridge regression (KNN-RR). The proposed KNN-RR is motivated by the sensitivity problem that multi-collinearity exists in the current KNN regression, aiming to enhance the prediction performance. Our theoretical result shows that under some mild assumptions, there exists a penalty parameter such that the mean square prediction error of ridge regression is smaller than that of ordinary least square regression. We examine the empirical performances of KNN-RR and other methods on real-world datasets, such as the AQI and PM2.5 prediction, and the results indicate that our method has some advantages in improving prediction accuracy. To a certain extent, this paper paves a new way to improve some supervised machine learning methods. [ABSTRACT FROM AUTHOR]

This study aims to model the reduction in the Tinnitus Functional Index (TFI) utilizing supervised machine learning algorithms, focusing primarily on Ordinary Least Squares (OLS), K-Nearest Neighbor (KNN), Ridge, and Lasso regressions. Our analysis highlighted Group, ISI, and SWLS as significant predictors of TFI reduction, identified through the best subset selection and confirmed by both forward and backward selection criteria in the OLS regression. Notably, the shrinkage methods, Ridge and Lasso regressions, demonstrated superior performance compared to OLS and KNN, with the Ridge regression presenting the smallest test mean square error (MSE) of 318.30. This finding establishes the Ridge regression as the best model for analyzing our Tinnitus dataset relative to the other methods, which exhibited test MSEs of 319.28 (Lasso), 330.76 (OLS), and 584.92 (KNN), respectively. This research highlights the potential of supervised machine learning algorithms in advancing personalized Tinnitus treatment, reflecting broader trends in the field as evidenced by studies in the literature. By leveraging these algorithms, we can enhance treatment precision and outcomes, contributing significantly to improved quality of life for individuals with Tinnitus. Future research should explore the integration of multimodal data and longitudinal applications of these algorithms to further refine predictive capabilities and treatment effectiveness. [ABSTRACT FROM AUTHOR]

In contrast to multi-class classification, in multi-label classification (MLC), each data sample is a member of several classes. In this regard, the classifiers should discover more complex patterns within the data; accordingly, the complexity grows remarkably. Recently, scholars used the binary relevance, the label powerset, and the ensemble method (EM) techniques to extend state-of-the-art classifiers or develop new ones to solve real-world MLC problems more optimally. The current paper recruits the ridge regression (RR) and a decision tree (DT) and presents a new MLC model named PHMLC. In the PHMLC, the Laplacian Eigenmaps Nonlinear Dimensionality Reduction Algorithm is employed to omit the redundant and irrelevant features. Next, the data is encoded by the principal label space transformation (PLST) and singular value decomposition. Then, the RR is recalled to predict the first set of labels through the encoded data. Besides, the Binary Relevance technique is applied to the original DT, and an MLC model called BR-DT is introduced. The BR-DT is responsible for predicting the second set of labels. Eventually, the greedy selection mechanism is utilized to obtain final labels based on the predicted labels by the PLST-based RR and the BR-DT models. To demonstrate the PHMLC's effectiveness, the PHMLC model is applied to nine real-life multi-label datasets, and the results are compared with the BR-SVM, CPLST, BR-Ridge, BR-DR, PLST, and BR-DT models regarding the AP, EM, HS, Macro, and Micro F1 metrics. [ABSTRACT FROM AUTHOR]

Deep learning models, such as recurrent neural network (RNN) models, are suitable for modeling and forecasting non-stationary time series but are not interpretable. A prediction model with interpretability and high accuracy can improve decision makers' trust in the model and provide a basis for decision making. This paper proposes a double decomposition strategy based on wavelet decomposition (WD) and empirical mode decomposition (EMD). We construct a prediction model of high-order fuzzy cognitive maps (HFCM), called the WE-HFCM model, which considers interpretability and strong reasoning ability. Specifically, we use the WD and EDM algorithms to decompose the time sequence signal and realize the depth extraction of the signal's high-frequency, low-frequency, time-domain, and frequency domain features. Then, the ridge regression algorithm is used to learn the HFCM weight vector to achieve modeling prediction. Finally, we apply the proposed WE-HFCM model to stationary and non-stationary datasets in simulation experiments. We compare the predicted results with the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models.For stationary time series, the prediction accuracy of the WE-HFCM model is about 45% higher than that of the ARIMA, about 35% higher than that of the SARIMA model, and about 16% higher than that of the LSTM model. For non-stationary time series, the prediction accuracy of the WE-HFCM model is 69% higher than that of the ARIMA and SARIMA models. [ABSTRACT FROM AUTHOR]

The advantages and disadvantages of application of Padé approximants to regression analysis with two independent covariates are discussed. The main difficulty of using Padé function is nonlinearity of data fitting. Possible approaches to overcoming the problem are discussed. New formulation of residuals is suggested in the method of least squares. It leads to a system of linear equations in case of rational functions. The possibility of using ridge regularization technique to avoid overfitting is demonstrated in this approach. To illustrate the efficiency of the suggested method, several practical cases from physics and reliability theory are considered. [ABSTRACT FROM AUTHOR]

Soccer is evolving into a science rather than just a sport, driven by intense competition between professional teams. This transformation requires efforts beyond physical training, including strategic planning, data analysis, and advanced metrics. Coaches and teams increasingly use sophisticated methods and data-driven insights to enhance decision-making. Analyzing team performance is crucial to prepare players and coaches, enabling targeted training and strategic adjustments. Expected goals (xG) analysis plays a key role in assessing team and individual player performance, providing nuanced insights into on-field actions and opportunities. This approach allows coaches to optimize tactics and lineup choices beyond traditional scorelines. However, relying solely on xG might not provide a full picture of player performance, as a higher xG does not always translate into more goals due to the intricacies and variabilities of in-game situations. This paper seeks to refine performance assessments by incorporating predictions for both expected goals (xG) and actual goals (aG). Using this new model, we consider a wider variety of factors to provide a more comprehensive evaluation of players and teams. Another major focus of our study is to present a method for selecting and categorizing players based on their predicted xG and aG performance. Additionally, this paper discusses expected goals and actual goals for each individual game; consequently, we use expected goals per game (xGg) and actual goals per game (aGg) to reflect them. Moreover, we employ regression machine learning models, particularly ridge regression, which demonstrates strong performance in forecasting xGg and aGg, outperforming other models in our comparative assessment. Ridge regression's ability to handle overlapping and correlated variables makes it an ideal choice for our analysis. This approach improves prediction accuracy and provides actionable insights for coaches and analysts to optimize team performance. By using constructed features from various methods in the dataset, we improve our model's performance by as much as 12%. These features offer a more detailed understanding of player performance in specific leagues and roles, improving the model's accuracy from 83% to nearly 95%, as indicated by the R-squared metric. Furthermore, our research introduces a player selection methodology based on their predicted xG and aG, as determined by our proposed model. According to our model's classification, we categorize top players into two groups: efficient scorers and consistent performers. These precise forecasts can guide strategic decisions, player selection, and training approaches, ultimately enhancing team performance and success. [ABSTRACT FROM AUTHOR]

Ridge regression is a variant of linear regression that aims to circumvent the issue of collinearity among predictors. The ridge parameter k has an important role in the bias-variance tradeoff. In this article, we introduce a new approach to select the ridge parameter to deal with the multicollinearity problem with different behavior of the error term. The proposed ridge estimator is a function of the number of predictors and the standard error of the regression model. An extensive simulation study is conducted to assess the performance of the estimators for the linear regression model with different error terms, which include normally distributed, non-normal and heteroscedastic or autocorrelated errors. Based upon the criterion of mean square error (MSE), it is found that the new proposed estimator outperforms OLS, commonly used and closely related estimators. Further, the application of the proposed estimator is provided on the COVID-19 data of India. [ABSTRACT FROM AUTHOR]

Enhancing marine carbon sequestration through nearshore aquaculture is a novel scientific approach to addressing global climate change and facilitating low-carbon development. Scientifically estimating the quantity and price of China's marine fisheries carbon sinks provides a crucial foundation for promoting marine carbon trading. In this article, firstly, the long-term carbon storage capacity of China's marine carbon sequestration fishery available from 1979 to 2022 for carbon trading is calculated. And then a transcendental logarithmic production function model incorporating ridge regression analysis, and an accounting equation for estimating the shadow price of China's marine fisheries carbon sequestration are established. Simultaneously, the distortion level of China's marine fisheries carbon sequestration prices from 2015 to 2022 is measured, and the reasons and economic effects of the distortion in prices are analyzed. The research results show that: 1) The capacity of a net carbon sequestration in China's marine carbon sequestration fishery for carbon trading, ranged from 78,869.01 tons in 1979 to 1,232,762.27 tons in 2022, with an average annual capacity of 592,472.07 tons and an average annual growth rate of 7.48%; 2) The price of China's marine fisheries carbon sinks increased from 39.46 CNY in 1979 to 375.96 CNY in 2022, with an average annual growth rate of 6.00%. The average annual price was 167.87 CNY; 3) There were varying degrees of distortion in China's marine fisheries carbon sequestration prices from 2015 to 2022, which decreased annually with the construction of China's own carbon trading market and the practice of trading. To realize the value of marine fisheries carbon sequestration, it is necessary to actively promote the development of voluntary emission reduction markets, develop carbon trading futures markets, and strengthen the dynamic monitoring system for resources. [ABSTRACT FROM AUTHOR]