Classical Linear Model Research Articles

Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data

BackgroundThe accurate prediction of genomic breeding values is central to genomic selection in both plant and animal breeding studies. Genomic prediction involves the use of thousands of molecular markers spanning the entire genome and therefore requires methods able to efficiently handle high dimensional data. Not surprisingly, machine learning methods are becoming widely advocated for and used in genomic prediction studies. These methods encompass different groups of supervised and unsupervised learning methods. Although several studies have compared the predictive performances of individual methods, studies comparing the predictive performance of different groups of methods are rare. However, such studies are crucial for identifying (i) groups of methods with superior genomic predictive performance and assessing (ii) the merits and demerits of such groups of methods relative to each other and to the established classical methods. Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding dataset and three empirical maize breeding datasets obtained from a commercial breeding program.ResultsOur results show that the relative predictive performance and computational expense of the groups of machine learning methods depend upon both the data and target traits and that for classical regularized methods, increasing model complexity can incur huge computational costs but does not necessarily always improve predictive accuracy. Thus, despite their greater complexity and computational burden, neither the adaptive nor the group regularized methods clearly improved upon the results of their simple regularized counterparts. This rules out selection of one procedure among machine learning methods for routine use in genomic prediction. The results also show that, because of their competitive predictive performance, computational efficiency, simplicity and therefore relatively few tuning parameters, the classical linear mixed model and regularized regression methods are likely to remain strong contenders for genomic prediction.ConclusionsThe dependence of predictive performance and computational burden on target datasets and traits call for increasing investments in enhancing the computational efficiency of machine learning algorithms and computing resources.

Linking green supply chain management practices and environmental performance in the manufacturing industry: a hybrid SEM-ANN approach.

This research determines the influence of green supply chain management practices (GSCM) on environmental performance. It also investigates the moderating role of supply chain environmental cooperation on GSCM practices and environmental performance relationships. A total of 370 employees of several Bangladeshi manufacturing companies were conveniently chosen as respondents. To verify the data validity and reliability and to test the hypotheses, we used SmartPLS. Finally, we employed an artificial neural network (ANN) to examine the relationship. Green design and green manufacturing have significant positive impacts on environmental performance, while green procurement and green distribution do not. Moreover, environmental cooperation moderates the relationships of green design and green distribution with environmental performance. The moderating effect of supply chain environmental cooperation in the relationship between GSCM practices and environmental performance in the manufacturing industry adds knowledge to the existing literature by incorporating a hybrid model combining PLS-SEM and ANN. Our study adds to the current body of knowledge by delving into the literature on GSCM from the perspective of Bangladesh's industrial sector. This study fills a knowledge gap by shedding light on the interactions of GSCM and environmental performance. Indeed, this study represents a step forward from classic linear regression-based models to an ANN-based nonlinear model. It also demonstrates new contributions to the literature on green supply chain management and environmental performance.

Determinants of profitability of insurance companies in Ethiopia: evidence from insurance companies from 2011 to 2020 years

The profitability of insurance companies has been the focus of several studies within the rise of economies across the countries worldwide. This study aimed to identify determinants of profitability of insurance companies in Ethiopian. The study used nine insurance companies selected by purposive sampling technique among the total 17 insurance companies in Ethiopia from period of 2011 to 2020 based on their establishment. Descriptive, causal research design and quantitative research approach were adopted in carrying out this study. Classical linear regression model under estimation of ordinary least square was employed to identify the determinants of profitability of insurance companies in Ethiopia at 5% level of significance. The classical linear regression model revealed that variable age of the company, tangibility of an asset, size of the company, managerial efficiency, leverage ratio, premium growth and GDP have a positive coefficient relationship with return on asset while loss ratio and inflation have a negative coefficient relationship with return on asset. On the other hand, age of the company size, managerial efficiency, leverage ratio, liquidity ration inflation and premium growth have statistically significant at 5% confidence interval level, whereas the other variables such tangibility of asset and GDP have no statistical significance at 5% confidence interval level. The insurance companies’ previous profit, age of the company, company size, managerial efficiency, leverage ratio, liquidity ratio, loss ratio, premium growth and inflation rate variables are significant key drivers of profitability of Ethiopian insurance companies. Giving due attention to the sector in line with key factors affecting the profitability will improve the overall performance of the insurance companies in Ethiopia.

Exploring Heat Demand Forecasting in District Heating Networks Using Random Parameter Linear Regression Model

Abstract Accurate forecasting of heat demand in district heating networks is essential for their efficient and sustainable operation. This paper presents a novel approach using a random parameter linear regression model to forecast heat demand, distinguishing itself from classical linear regression models by its ability to address unobserved heterogeneity among parameters. Through a case study in Estonia and utilizing data from 2018 to 2023 and considering seasonality and consumption patterns, the study investigates determinants of heating demand in district heating networks. Two models were trained for heating and non-heating seasons. Results indicate significant impacts of weather conditions, energy prices, time of day, and network infrastructure on heat supply during the heating season, while only time of day and electricity prices were significant drivers during the non-heating season, with no notable influence of weather conditions. Prediction accuracy was slightly enhanced using the random parameter linear regression model, with a mean absolute percentage error of 9.66 % compared to 9.99 % for the Multi Linear Regression Model on the testing set.

Comparative analysis of the response of the road structure with full/partial interlayer connection to traffic action

This article investigates the response of the road pavement of a road sector analysed to the action of traffic that was determined numerically by using two dedicated software. In this respect, the Romanian program CALDEROM 2000 (PD 177, 2001), respectively the French program for calculating the road pavements ALIZÉ-LCPC were used. Both programs use the Burminster elastic model for the analytical evaluation of stress states and specific deformations in multilayer road pavements. However, unlike the CALDEROM 2000 program, the ALIZÉ-LCPC software allows the user to define the interfaces between layers and switch loads from single axle to multiple loads, being a rational method, based on the determination of the resilient stresses and deformations in road structures by using the classical linear elastic multilayer model. The aim of this study was to determine whether the road pavement with deficiencies of adhesion between layers is able to take over the calculated traffic considered in the project, possibly the proportion of calculation traffic that can be taken over, which would allow an assessment of the capable operation life of the road pavement and, implicitly, of the possibility of its yielding before the end of the perspective period considered.

Online sales prediction approach using methodology of CRISP-DM

This article studies the sales forecasting problem in the field of e-commerce. Based on the CRISP-DM methodology, innovative data mining technology is used to construct a variety of forecasting models, and is compared and optimized. This article improves the quality and quantity of sales forecasts and provides enterprises with more accurate and effective decision support. In terms of modeling optimization in this article, data mining models such as random forest, support vector machine, and neural network are used for comprehensive prediction, and comparative analysis is conducted with the classic multiple linear regression model. Through model evaluation and optimization, this paper achieved better prediction performance and accuracy. This research has certain theoretical significance and practical value, and provides new ideas and methods for the marketing decisions and business development of e-commerce enterprises.

A Quantitative Impact Assessment of English Translation Errors on the Authenticity of News Dissemination

Abstract To improve the accuracy of news communication between readers and audiences, it is necessary to avoid the errors generated in the process of news translation so as to make the English translation of news content expression more in line with the actual situation, thus ensuring the authenticity of news dissemination. In this paper, the MapReduce multiple regression model is constructed by optimizing the classical multiple linear regression model in terms of maximum likelihood estimation and gradient descent algorithm with the aim of reducing the errors in the process of English translation of news. This model is used to analyze the errors generated by the English translation process and finally proposes a method for improving translation accuracy based on the factors causing the errors. After testing, the F-value, P-value, and R² of the degree of fit of the MapReduce model are 54.39, 0.005, and 0.893, respectively. Furthermore, the model passed the significance test. The Pearson coefficients of the 4 types of translation errors in the MapReduce multiple linear regression model were −0.673, −0.622, −0.584, and −0.736, respectively, all of which were significantly negatively correlated with the truthfulness of news dissemination at the 1% level. The model analyzes that the three kinds of translation errors are basically between 2%-4%, 7%-20%, and 2%-4%, and puts forward reasonable opinions on the avoidance of the four kinds of errors, namely, “pragmatic translation, cultural translation, linguistic translation and text-specific translation”, which generate errors in the process of the English translation of news.

Mitigating Multicollinearity in Linear Regression Model with Two Parameter Kibria-Lukman Estimators

This study delves into the challenges faced by the ordinary least square (OLS) estimator, traditionally regarded as the Best Linear Unbiased Estimator in classical linear regression models. Despite its reliability under specific conditions, OLS falters in the face of multicollinearity, a problem frequently encountered in regression analyses. To combat this issue, various ridge regression estimators have been developed, characterized as one-parameter and two-parameter ridge-type estimators. In this context, our research introduces novel two-parameter estimators, building upon a recently developed one-parameter ridge estimator to mitigate the impact of multicollinearity in linear regression models. Theoretical analysis and simulation experiments were conducted to assess the performance of the proposed estimators. Remarkably, our results reveal that, under certain conditions, these new estimators outperform existing estimators, displaying a significantly reduced mean square error. To validate these findings, real-life data was employed, aligning with the outcomes derived from theoretical analysis and simulations.

Factors affecting tourist visits to archaeological sites in Turkey: A spatial regression analysis

The study focuses on the factors affecting visitor numbers to archaeological sites in Turkey. The aim is to investigate the geographical, economic, and demographic factors underlying the visits using statistical methods. The study covers 117 archaeological site visits in 2019. Although existing studies analysed determinants of visits to archaeological sites of different countries, the evidence needs to be explicit. Methodologically, the classical linear regression models are primarily applied in the literature, whereas the incorporation of spatial dependence has largely been ignored. This study contributes to the literature by employing demographic, economic, and climatic factors and spatial relations between the sites. Therefore, spatial autoregressive (SAR) and spatial error models (SEM) are developed in the analyses. According to the results, WHL inscription and distance to the city centre are crucial factors for the visits. In addition, the study emphasizes the significant negative effect of spatial dependence on visitor numbers of archaeological sites near each other.

The Analysis of the Relationship between Corporate Social Responsibility and Financial Performance of Company

Corporate SCorporate Social Responsibility (CSR) has been forming very dynamically and intensively for several decades. The rapid development, as well as the relatively large scope of this concept, which is cross-sectionally related to a number of different social disciplines, so far causes a very significant terminological inconsistency. The paper focuses on the relationship between CSR and the financial performance of companies, or on the positive consequences of applying the concept of CSR in business on the example of a selected company Deutsche Telekom AG, which applies CSR in its business and which achieves positive results not only from this point of view, but also from the point of view of company profitability. When analyzing the company from the point of view of the global market, it quantitatively monitors the impact of the measured indicators on the profitability of total assets (ROA) and on net profit, by testing the basic assumptions made on the classical linear regression model. The main goal of the paper was to find out the impact of CSR on the financial results of the parent company Deutsche Telekom AG for the period 2001-2021 and to find the relationship between CSR and economic benefits.

Fractional Linear Reservoir Model as Elementary Hydrologic Response Function

This paper presents a fractional linear reservoir model as the elementary response function of hydrologic systems corresponding to the classical linear reservoir model and tests its applicability to rainfall–runoff modeling. To this end, we formulate a fractional linear reservoir model in terms of fractional calculus following the same procedure as the classical linear reservoir model and, at the simplest level, compare its performance of rainfall–runoff modeling with the linear and nonlinear reservoir models. The impulse response function of a fractional linear reservoir model, a probability density function (PDF) following the Mittag–Leffler distribution, shows nonlinearity due to its time-variant behavior compared to that of a linear reservoir model. In traditional linear hydrologic system theory, the lag and route version of a fractional linear reservoir model produces the fast-rising and slow-recession of runoff hydrographs, implying the mixed response of linear and nonlinear reservoir models to rainfall. So, a fractional linear reservoir model could be considered a fundamental tool to effectively reflect the nonlinearity of rainfall–runoff phenomena within the framework of the linear hydrologic system theory. In this respect, the fractional order of the storage relationship specifying a fractional linear reservoir model can be viewed as a kind of parameter to quantify the heterogeneity of runoff generation within a river basin.

Analyzing the Importance of International Law in Current International Relations Based on Linear Programming Models

Abstract International law is mainly the law between countries, which is the code of conduct for countries to conduct international interactions, and this paper researches the role of international law in international relations interactions. First of all, this paper combines the fuzzy set on the basis of the classical linear regression model algorithm, establishes the fuzzy linear programming model, and explores the fuzzy correlation analysis. Then, taking EU law as an example, the role of EU law in EU relations is analyzed through the theory of EU law, EU and the role of EU law in the EU embodied. Finally, based on the role of EU law, the importance of international law in international relations is studied through a fuzzy linear programming model, and the role of strengthening international law is considered. The results show that the countries consider the legitimacy of EU law to be above 0.9, and the countries’ participation in EU law also ranges from 0.9 to 1.0, and the countries’ self-identification increases by about 0.1 to 0.2 after the enactment of EU law. The correlation coefficients of international law on the construction of the identities of the 10 countries are all greater than 0.75, and the interests of the countries have increased by about 2.3, and international law promotes the order of international relations.

Can machine learning algorithms deliver superior models for rental guides?

In this paper we discuss the use and potential advantages and disadvantages of machine learning driven models in rental guides. Rental guides are a formal legal instrument in Germany for surveying rents of flats in cities and municipalities, which are today based on regression models or simple contingency tables. We discuss if and how modern and timely methods of machine learning outperform existing and established routines. We make use of data from the Munich rental guide and mainly focus on the predictive power of these models. We discuss the “black-box” character making some of these models difficult to interpret and hence challenging for applications in the rental guide context. Still, it is of interest to see how “black-box” models perform with respect to prediction error. Moreover, we study adversarial effects, i.e. we investigate robustness in the sense how corrupted data influence the performance of the prediction models. With the data at hand we show that models with promising predictive performance suffer from being more vulnerable to corruptions than classic linear models including Ridge or Lasso regularization.

Subgroup analysis for the functional linear model

Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for all subjects. In this paper, the classical model is extended to allow heterogeneous coefficient functions across different subgroups of subjects. The greatest challenge is that the subgroup structure is usually unknown to us. To this end, we develop a penalization-based approach which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and coefficient functions within each subgroup. An effective computational algorithm is derived. We also establish the oracle properties and estimation consistency. Extensive numerical simulations demonstrate its superiority compared to several competing methods. The analysis of an air quality dataset leads to interesting findings and improved predictions.

Livelihood Changes, Spatial Anticontagion Policy Effects, and Structural Resilience of National Food Systems in a Sub-Saharan African Country Context: A Panel Machine Learning Approach

The livelihood changes due to the COVID-19 policies in low-income and transitional economies serve as a lever for gauging the structural resilience of national food systems. Yet, few studies have addressed the cascading effects of the pandemic policies on the livelihood changes of farming system actors or modeled and provided coherent hypotheses about the transitory structural shifts at the micro-level. Other studies on the subject have either captured the early impacts of the pandemic on food systems with limited or no insight into the sub-Saharan African context or have used macro-level data, due to sparsely available micro-level data. These early insights are relevant for the design of early warning systems. However, an ongoing and deeper insight into the effects of pandemic policies is critical, since new and more comprehensive policies are needed to address the economic fallout and the extenuating effects of COVID-19 on food supply chain disruptions. The overriding questions are as follows: what are the effects of the pandemic policies on the livelihoods of food system actors and are there spatial-economic variations in the effects of the pandemic policies on the livelihoods of the farming system actors? Using 2019 and 2020 primary data from 836 farming system actors in Ghana, we offer fresh insights into the transitory micro-level livelihood changes caused by the COVID-19 anticontagion policies. We analyzed the data using the generalized additive, subset regression, classical linear, and logistic regression models in a machine learning framework. We show that the changes in the livelihood outcomes of the food system actors in Ghana coincide with the nature of pandemic mitigation policies adopted in the spatial units. We found that the lockdown policies had a negative and significant effect on the livelihoods of the farming system actors in the lockdown areas. The policies also negatively affected the livelihoods of the farming system actors in distant communities that shared no direct boundary with the lockdown areas. On the contrary, the lockdown policies positively affected the livelihoods of the farming system actors in the directly contiguous communities to the lockdown areas. We also document the shifts in the livelihood outcomes of the farming system actors, such as income, employment, food demand, and food security in the different spatial policy areas.

Lookahead data-gathering strategies for online adaptive model reduction of transport-dominated problems

Online adaptive model reduction efficiently reduces numerical models of transport-dominated problems by updating reduced spaces over time, which leads to nonlinear approximations on latent manifolds that can achieve a faster error decay than classical linear model reduction methods that keep reduced spaces fixed. Critical for online adaptive model reduction is coupling the full and reduced model to judiciously gather data from the full model for adapting the reduced spaces so that accurate approximations of the evolving full-model solution fields can be maintained. In this work, we introduce lookahead data-gathering strategies that predict the next state of the full model for adapting reduced spaces toward dynamics that are likely to be seen in the immediate future. Numerical experiments demonstrate that the proposed lookahead strategies lead to accurate reduced models even for problems where previously introduced data-gathering strategies that look back in time fail to provide predictive models. The proposed lookahead strategies also improve the robustness and stability of online adaptive reduced models.

A Monte Carlo Experiment on the Asymptotic Efficiency of System Estimators under Multicollinearity

Classical linear regression model assumes that there is no multicollinearity among the explanatory variables in a regression model. Contrary to this assumption, where multicollinearity is perfect, the regression coefficients of the explanatory variables are indeterminable and their standard errors are infinite. On the other hand, where multicollinearity is less than perfect, the regression coefficients, although determinable, possess large standard errors. This implies that the coefficients cannot be estimated with great precision. Hence multicollinearity problem is a major problem in econometric analysis. Using Monte Carlo Simulation, we evaluated the asymptotic efficiency of six estimators (OLS, ILS,2SLS, 3SLS, LIML and FIML), under different magnitudes of the unintended linear relationship between the exogenous variables. Using the SSR criteria, we found that OLS followed by ILS turned out the best estimates amongst the six estimators under multicollinearity. We also found that with increasing sample size, there is no remarkable asymptotic effect in the performance of the estimators at the levels of multicollinearity.

Generalized Variable Selection Algorithms for Gaussian Process Models by LASSO-Like Penalty

With the rapid development of modern technology, massive amounts of data with complex pattern are generated. Gaussian process models that can easily fit the nonlinearity in data become more and more popular nowadays. It is often the case that in some data only a few features are important or active. However, unlike classical linear models, it is challenging to identify active variables in Gaussian process models. One of the most commonly used methods for variable selection in Gaussian process models is automatic relevance determination, which is known to be open-ended. There is no rule of thumb to determine the threshold for dropping features, which makes the variable selection in Gaussian process models ambiguous. In this work, we propose two variable selection algorithms for Gaussian process models, which use the artificial nuisance columns as baseline for identifying the active features. Moreover, the proposed methods work for both regression and classification problems. The algorithms are demonstrated using comprehensive simulation experiments and an application to multi-subject electroencephalography data that studies alcoholic levels of experimental subjects. Supplementary materials for this article are available online.

ROBUST EXTREME RETURN LEVEL WITH POWER NORMALIZATION FOR EXTREME EVENTS: APPLICATION OF REAL HYDROLOGY DATA

In statistical studies of rare and catastrophic phenomena the distribution of generalized extreme values under linear normalization is always chosen as the appropriate model. It used to estimate the probabilities of events that have not yet been observed. Recently, the extreme value theory (EVT) received a lot of attention both theoretically and practically using just the classical linear model (L-Model) or linear normalization of the maximum to estimate return level. So, in this paper we propose a new multiplicative model based on the distribution of generalized extreme values under non-linear normalization, whose purpose is to raise the strong and weak points between these two models. Our main goal is to use our multiplicative model (P-model) to calculate the return level, as well as the associated confidence interval. The diagnostic fit, test and statistical inference to compare the two models (linear and non-linear) are studied. Finally, a data analysis and discussion are applied at first on real hydrological data for Morocco and South of Australia, then on water levels of lake Erié in Canada. The results show that our multiplicative (non-linear) model is more adaptive because it takes into account the variation of the return period.

Discrete particle methods for granular bentonite material simulation

Bentonite clays are strategic materials to ensure the sealing capacity of engineered barrier systems (EBS) in deep geological repositories of high-level radioactive waste. Formerly installed in the form of compacted blocks, in recent years, there has been growing interest in EBS design using granular bentonite materials (GBM) due to improved construction processes [1]. GBM range from the powdered and granulated forms, both natural and obtained from crushed blocks, to compressed bentonite pellets, and the mixtures thereof. The effect of thermo-hydro-chemo-mechanical (THCM) conditions on the evolving gap-filling performance following installation has been investigated in full-scale experiments [2] and in laboratory-scale tests [3], with particular focus on bentonite pellet-powder mixtures [4, 5, 6].
 Numerical modelling of coupled THCM expansive soil behaviour is widespread using continuum methods, namely the finite element method [7, 8], applicable up to full-scale systems even though at the expense of resolution. At the laboratory scale, discrete particle-based methods accounting for the granular nature of bentonite materials in the early stages of hydration provide insight into pellet shape and local effects on the mechanical behaviour of dry pellet-powder systems as well as hydro-mechanical effects on the swelling capacity of GBM systems.
 The compressibility of dry pellet packings and pellet-powder mixture samples is modelled by the discrete element method (DEM). This method has been applied to study the swelling behaviour of pellets represented by spherical particles [9]. The DEM is a powerful tool for reproducing complex granular shapes by non-spherical particle surfaces [10] and also with clumps of spheres (Figure 1(a)), allowing to define mixed material and mechanical contact properties within the pellets. The deformation and fissuring of pellets under oedometer compression is tracked using a cohesive particle model [11], including damage for interparticle contacts in tension, which captures deformation associated phenomena such as surface spalling and pellet reorganisation occurring at stress levels below the diametral fracture. In addition, bentonite powder is represented by loose packings of upscaled spheres matching
 the experimental porosity of the fillings, interacting by a classical linear spring-dashpot contact model resulting in the reduced overall compressibility observed in pellet-powder mixtures [5].
 Flow through GBM systems is modelled by the new discrete particle-based XMm method [12]. In the XMm, the material is discretised into a set of bentonite units (BU) or particles acting as representative elementary volumes of a homogeneous continuum inside which the mass balance equations are solved at the megastructural (X), macrostructural (M), and microstructural (m) pore levels (Figure 1(b)). Moreover, the mechanical compatibility of the system is obtained from the coupling between strain increments and centroid displacements in each BU, where the contact stress and overlap between neighbouring particles are updated similarly to the DEM approach. The hydro-mechanical problem is posed as a set of ordinary differential equations (ODE) to be solved for each BU, and thus the efficiency of the XMm is a compromise between the typically reduced computational cost of ODE solvers and the number of BU considered. The XMm has been used to simulate the hydration of a powder sample with low heterogeneity [4], successfully capturing the evolution of the displacement field upon saturation in isochoric conditions. This method is applied to reproduce the confined hydration of a heterogeneous pellet-powder system [6]. Assuming 2D axisymmetry, the XMm allows defining different initial material properties in each BU introducing the heterogeneity of the sample. The evolving swelling pressure is reproduced as well as the hydration velocities at the different structural levels, showing the decreasing heterogeneity of the mixture.