Linear Regression-based Method Research Articles

The Influence of Transfer Pricing, Leverage, Profitability, and Earnings Management on Tax Avoidance Moderated by Institutional Ownership (An Empirical Study of Manufacturing Companies)

This research delves into the multifaceted relationships between Transfer Pricing, Leverage, Profitability, Profit Management, and Tax Avoidance, with the moderating influence of Institutional Ownership in manufacturing companies listed on the Indonesia Stock Exchange (BEI). The study employs a descriptive research design with a quantitative approach, utilizing a multiple linear regression-based method through EViews. The research population comprises manufacturing companies listed on the Indonesia Stock Exchange (BEI) between 2020 and 2021. The purposive sampling method was employed, resulting in a sample of 81 manufacturing companies, totaling 162 observations. Secondary data were collected from the official IDX website www.idx.co.id and www.yahoofinance.com. The findings indicate that Transfer Pricing and Leverage exhibit a significant positive influence on Tax Avoidance in manufacturing companies, while Profitability demonstrates a significant negative effect on Tax Avoidance. Conversely, Profit Management does not exhibit a significant impact on Tax Avoidance. Moreover, Institutional Ownership is identified as a moderator, strengthening the influence of Transfer Pricing and Leverage on Tax Avoidance. However, Institutional Ownership does not reinforce the influence of Profitability and Profit Management on Tax Avoidance. This research contributes valuable insights for policymakers, tax authorities, and corporate practitioners, highlighting the nuanced dynamics of Transfer Pricing, Leverage, Profitability, and Profit Management in shaping tax avoidance strategies, particularly in the context of manufacturing companies in Indonesia.

The Effect of Professional Training, Job Market Considerations, and Work Environment on Accounting Students' Interest in Careers to be Public Accountants

Interest is one of the considerations for students in the career selection process. A person's interest can be seen from several internal and external factors. This study aimed to determine the effect of professional training, labor market considerations, and work environment on the interest of STEI Jakarta Accounting Students to become Public Accountants. The research method is a descriptive quantitative approach, measured using multiple linear regression-based methods processed with SPSS Version 26.00. The population in this study were undergraduate students majoring in Accounting STEI Jakarta class of 2018 semester eight, who had followed the Auditing 1 and Audit 2 courses. Using the Slovin formula, the number of samples was 75 respondents by determining the sample's criteria. The results showed that professional training had no significant effect on accounting students' interest in having a career as a public accountant. In comparison, the consideration of the labor market and work environment significantly affects the interest of accounting students in a career as a public accountant

범주형 전환문항을 포함하는 항목 무응답 대체 방법 비교

In social surveys, unfolding bracket questions are used as a technique to reduce the loss of information due to nonresponse when item nonresponses occur. Item nonresponse, including unfolding bracket questions, has a similar structure to interval censored survival data. However, imputation methods to handle item nonresponses including unfolding bracket questions are limited. In this study, we examine performance of imputation with and without unfolding bracket questions. Also we compare three imputation methods to handle unfolding bracket questions. The First method is a linear regression-based nearest neighbor hotdeck imputation. Secondly, it is possible to consider a uniform distribution that selects a value with the same probability within the section for each individual. The third is an imputation method using the survival function estimated by the nonparametric maximum likelihood estimation method. Hotdeck imputation was considered as an imputation for not using unfolding bracket questions. The performance of these imputation methods was evaluated through simulation and the KLoSA study is used to provide examples of the application of these methods to real data. Imputation methods using unfolding bracket questions showed better performance than the method without unfolding bracket questions. The performance of the imputation method using the survival function estimated by the nonparametric maximum likelihood estimation method was better when the nonresponse rate was low and when the nonresponse rate is large in terms of root mean square error. When the non-response rate is large, the linear regression-based nearest neighbor hotdeck imputation method showed better results in terms of mean bias.

Analisis Pengaruh Pendapatan Premi, Hasil Investasi dan Beban Klaim Terhadap Laba Perusahaan Asuransi Jiwa di Indonesia

This study aims to examine whether the effect of premium income, the effect of investment returns, the effect of claim expenses on profits in Life Insurance companies registered with the Financial Services Authority (OJK). This study uses a descriptive research type quantitative approach, which is measured using multiple linear regression-based methods with SPSS 25. The population of this study is a conventional life insurance company registered with the Financial Services Authority (OJK) from 2015 to 2019. The sample was determined based on purposive sampling method, with a total sample of 25 life insurance companies. The data used in this study is secondary data. The data collection technique uses the documentation method through the official website www.ojk.go.id. The results of the study prove that (1) premium income has a significant positive effect on the profits of life insurance companies listed on the Financial Services Authority (OJK) for the 2015-2019 period, (2) investment returns have a significant positive effect on the profits of life insurance companies listed on the Financial Services Authority (OJK) for the 2015-2019 period, (3) claim expenses have a significant negative effect on the profits of life insurance companies listed on the Financial Services Authority (OJK) for the 2015-2019 period.

Cheap Turns Superior: A Linear Regression-Based Correction Method to Reaction Energy from the DFT.

Workflows to predict chemical reaction networks based on density functional theory (DFT) are prone to systematic errors in reaction energy due to the extensive use of cheap DFT exchange-correlation functionals to limit computational cost. Recently, machine learning-based models are increasingly applied to mitigate this problem. However, machine learning models require systems similar to trained data, and the models often perform poorly for out-of-distribution systems. Here, we present a simple bond-based correction method that improves the accuracy of DFT-derived reaction energies. It is based on linear regression, and the correction terms for each bond are derived from reactions among the QM9 data set. We demonstrate the effectiveness of this method with three DFT functionals in three different rungs of Jacob's ladder. The simple correction method is effective for all rungs but especially so for the cheapest PBE functional. Finally, we applied the correction method to a few reactions with molecules significantly different from those in the QM9 data set that was used to fit the linear regression model. Once corrected by this method, we found that the DFT reaction energies for such out-of-distribution reactions are within 0.05 eV of the G4MP2 method.

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

We demonstrate levels of skill for forecasts of seasonal-mean wind speed and solar irradiance in Europe, using seasonal forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that, where there is skill, a simple linear regression-based method using the hindcast and forecast ensemble means provides a straightforward approach for producing calibrated probabilistic seasonal forecasts. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, and we show that this provides opportunities to improve the skill in some cases.We further demonstrate that, on seasonal-average and regional (e.g. national) average scales, wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance). The detailed non-linear transformations from meteorological quantities to energy quantities, which are essential for detailed simulation of power system operations, are usually not necessary when forecasting gross wind or solar generation potential at seasonal-mean regional-mean scales.Together, our results demonstrate that where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, skilful forecasts of seasonal mean wind and solar power generation can be made based on the climate variable alone, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.

Ultrasound estimation of strain time constant and vascular permeability in tumors using a CEEMDAN and linear regression-based method

Ultrasound poroelastography focuses on the estimation of the spatio-temporal mechanical behavior of tissues using data often corrupted with non-stationary noise. The cumulative strain calculated from prolonged temporal acquisition of RF data can face the problem of aggregate noise. This noise can significantly affect the accuracy of curve fitting techniques necessary to estimate the clinically significant strain Time Constant (TC) and related parameters. We present a new technique, which decomposes the non-linear temporal behavior of the differential strain to extract the monotonic decaying trend by using the time-domain and data-driven Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm. A linear regression scheme is then used to obtain the slope of the transformed non-linear trend, which carries information about the strain TC. Assessment of Vascular Permeability (VP), a transport parameter indicative of tumor growth, requires accurate strain TC estimations. Finite Element (FE), ultrasound simulations and in vivo experiments are used to investigate the performance of the proposed technique. Based on the simulation analysis, the average Percentage Relative Error (PRE) values of our method are 4.15% (for TC estimation) and 5.00% (for VP estimation) at 20 dB SNR level for different Percentage of Good Frames (PGF) (i.e., 20%, 50%, 75%, and 100%). These PRE values are substantially lower than those obtained using other conventional elastographic techniques. Our proposed method could become a new data-adaptive tool for analyzing the non-linear time-dependent response of complex tissues such as cancers.

Regression-Based Methods for Daily Peak Load Forecasting in South Korea

This study examines the daily peak load forecasting problem in South Korea. This problem has become increasingly important due to the continually changing energy environment. As such, it has been studied by many researchers over the decades. South Korea is geographically located such that it experiences four distinct seasons. Seasonal changes are among the main factors affecting electricity demand. In addition, much of the electricity consumption in a strong manufacturing country like South Korea is driven by industry rather than by residential customers. In order to forecast daily peak loads of South Korea, in this study we proposed multiple linear regression-based methods where several season-specific regression models (i.e., summer, winter, and all-season models) were included. The most appropriate model among the three models was selected considering the characteristics of the electricity demand, and was then applied to daily forecasting. The performance of the proposed methods were evaluated through computational experiments. Forecasts obtained by the proposed methods were compared with those obtained by existing forecasting methods, including a machine learning method. The results showed that the proposed methods had mean absolute percentage errors around 1.95% and outperformed all benchmarks.

LINAC: A Spatially Linear Accelerator for Convolutional Neural Networks

We first propose a method to utilize the spatially linear correlation between activations and weights of CNN models and accelerate the inference process via linear regression. With the proposed linear regression-based acceleration method, stronger bit-sparsity is excavated to reduce ineffectual computations during the inference in CNN acceleration systems without introducing any errors to the algorithm. We also propose the corresponding hardware accelerator based on the linear convolution mechanism: LINAC, which implements the linear convolution that exploits bit-sparsity to boost performance and energy efficiency significantly. In our experiments, LINAC boosts the inference performance by 11× on average over the present value-agnostic accelerator and outperforms other state-of-the-art bit-sparse accelerators.

Investigation of Influential Factors towards Predicting Death Rate in Bangladesh

The concept of death rate is very momentous for development planning in a country. Bangladesh has largely extended its demographic transition from Death Rate (DR). Death Rate observes the economic growth and the standard of living in a country and sometimes controls the country classification defined by the World Economic Situation and Prospects (WESP). This paper looks into numerous social and economic factors responsible for determining the DR in Bangladesh and builds a prediction model that discloses many unexplored and useful observations. The World Bank data repository, based on the World Development Indicator (WDI) – 2015, has been inquired for the investigation and model building process. Primarily, a multiple stepwise linear regression-based method has been applied to build various models, and their performances are measured using MAE, RMSE, and RAE under the shed of k-fold cross-validation. Experimental results show that the final identified model forecasts the DR convincingly.

Practical Linear Regression-Based Method for Detection and Quantification of Stiction in Control Valves

Practical Linear Regression-Based Method for Detection and Quantification of Stiction in Control Valves

Renewable Energy Production Forecasting: A Comparative Machine Learning Analysis

As renewable energy has become increasingly popular worldwide, while solar and wind energy has been the leading source of renewable energy up to now, the accuracy of renewable energy forecasts is challenge for the planning, management, and operations of the power system. However, due to the intermediate and frenzied nature of renewable energy data, this is a most challenging task. This study provides a comprehensive and complete review of the renewable energy forecast based on different machine learning algorithms to explore effectiveness, efficiency, competence, and application potential. In this work, we have built time series renewable energy forecasting model with Support Vector Machine (SVM), Linear Regression (LR), and Long Short-Term Memory (LSTM) on twelve (12) countries. The experimental results are very interesting. For example, SVM based forecasting model is a better fit for the countries with small mean and standard deviation while linear regression-based methods show a bit better result in case of larger mean and standard deviation. Meanwhile, LSTM based models provide smoother regular-shaped forecasting. We can forecast two years of daily renewable energy production with these forecasting models. The point should be noted that we have developed different models for different countries. We have able to reach a Root Mean Square (RMS) value of 3.1 38 with SVM based model.

Deep Learning Approaches to Spatial Downscaling of GRACE Terrestrial Water Storage Products Using EALCO Model Over Canada

Estimating terrestrial water storage (TWS) with high spatial resolution is crucial for hydrological and water resource management. Comparing to traditional in-situ data measurement, observation from space borne sensor such as Gravity Recovery and Climate Experiment (GRACE) satellites is quite effective to obtain a large-scale TWS data. However, the coarse resolution of the GRACE data restricts its application at a local scale. This paper presents three novel convolutional neural network (CNN) based approaches including the Super-Resolution CNN (SRCNN), the Very Deep Super-Resolution (VDSR), and the Residual Channel Attention Networks (RCAN) to spatial downscaling of the monthly GRACE TWS products using the outputs of the Ecological Assimilation of Land and Climate Observations (EALCO) model over Canada. We also compare the performance of CNN-based methods with the empirical linear regression-based downscaling method. All comparison results were evaluated by root mean square error (RMSE) between the reconstructed GRACE TWS and the original one. RMSEs over the matched pixels are 22.3, 14.4, 18.4 and 71.6 mm of SRCNN, VDSR, RCAN and linear regression-based method respectively. Obviously, VDSR shows the best accuracy among all methods. The result shows all CNN-based super resolution methods preform much better than traditional method in spatial downscaling.

Machine learning in ground motion prediction

This paper studies the advantages and disadvantages of different machine learning techniques in predicting ground-motion intensity measures given source characteristics, source-to-site distance, and local site conditions. Typically, linear regression-based models with predefined equations and coefficients are used in ground motion prediction. However, restrictions of the linear regression models may limit their capabilities in extracting complex nonlinear behaviors in the data. Therefore, the present paper comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. This study quantifies event-to-event and site-to-site variability of the ground motions by implementing them as random effect terms to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4–500 km in Oklahoma, Kansas, and Texas since 2005. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring predefined equations or coefficients. Moreover, it is found that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Dual robust regression for pattern classification

Linear regression-based and extended methods have been widely used in pattern classification. These methods can be roughly divided into two categories: reconstruction error-based methods and discriminative methods. The reconstruction error-based methods search the target label by learning the representation coefficients to compute the minimal reconstruction error. The goal of the discriminative methods is to learn the projection matrix to predict the target label of the image. To combine the advantages of these two kinds of regression-based methods, this paper presents a dual robust regression framework (DualRR) for pattern classification. In the training stage, a double low-rank robust regression model (DLR) is proposed to learn the projection matrix. In DLR, low-rank robust regression motivates us to model the data as the sum of a low-rank clean data and sparse noise matrix. The low-rank is further used to constrain the projection matrix to enhance the discriminative performance. In the testing stage, the proposed framework employs a robust regression representation model to learn the optimal representation coefficients and obtain the reconstruction sample to approximate the test sample. We thus apply the reconstruction sample to search the classification label by using the projection matrix learned in the training stage. Extensive experiments are conducted on six public available databases, namely, LFW, FRGC, CUHK Sketch, PolyU Palm, NUST-RF and Caltech 101, demonstrating the merits of the proposed model over state-of-the-art regression-based classification methods.

A multiobjective evolutionary optimization method based critical rainfall thresholds for debris flows initiation

At present, most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression. With relatively weak fault tolerance, this method not only ignores nonlinear effects but also is susceptible to singular noise samples, which makes it difficult to characterize the true quantization relationship of the rainfall threshold. Besides, the early warning threshold determined by statistical parameters is susceptible to negative samples (samples where no debris flow has occurred), which leads to uncertainty in the reliability of the early warning results by the regression curve. To overcome the above limitations, this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network (ANN) and a multiobjective evolutionary optimization implemented by particle swarm optimization (PSO). Firstly, the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D. An ANN is used to construct a dual-target (dual-task) predictive surrogate model, and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model, which is intended to overcome the limitations of the existing linear regression-based threshold methods. Finally, a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps. This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.

Evaluation of different methods and data sources to optimise modelling of NO2 at a global scale

BackgroundIn countries where air pollution stations are unavailable or scarce, station measurements from other countries and atmospheric remote sensing could jointly provide information to estimate ambient air quality at a sufficiently fine resolution to study the relationship between air pollution exposure and health. Predicting NO2 concentration globally with sufficient spatial and temporal resolution and accuracy for health studies is, however, not a trivial task. Challenges are data deficiency, in terms of NO2 measurements and NO2 predictors, and the development of a statistical model that can typify the regional and continental differences, such as traffic regulations, energy sources, and local weather. ObjectiveWe investigated the feasibility of mapping daytime and nighttime NO2 globally at a high spatial resolution (25 m), by including TROPOMI (TROPOspheric Monitoring Instrument) data and comparing various statistical learning techniques. MethodWe separated daytime (7:00 am - 9:59 pm) and nighttime (10:00 pm - 6:59 am) based on the local times. To study if one should build models for each country separately, national models in 4 selected countries (the US, China, Germany, Spain) were developed. We build the models for 2017 and used 3636 stations. Seven statistical learning techniques were applied and the impact of the predictors, model fitting, and predicting accuracy was compared between different techniques, national models, national and global models, and models with and without including the NO2 vertical column density retrieved from TROPOMI. Result and conclusionThe ensemble tree-based methods obtained higher accuracy compared to the linear regression-based methods in national and global models. The global tree-based methods obtained similar accuracy to national models. Different spatial prediction patterns are observed even when the prediction accuracy is very similar. Separating between day and night can be important for more accurate air pollution exposure assessment. The TROPOMI variable is ranked as one of the most important variables in the statistical learning techniques but adding it to global models that contain other precedent remote sensing products does not improve the prediction accuracy.

Comparison of Methods Used to Correct Self-Reported Protein Intake for Systematic Variation in Reported Energy Intake Using Quantitative Biomarkers of Dietary Intake

Multiple methods of correcting nutrient intake for misreported energy intake have been proposed but have not been extensively compared. The availability of the Women's Health Initiative (WHI) data set, which includes several objective recovery biomarkers, offers an opportunity to compare these corrections with respect to protein intake. We compared 5 energy-correction methods for self-reported dietary protein against urinary nitrogen-derived protein intake. As part of the WHI Nutritional Biomarkers Study (NBS) 544 participants (50- to 80-y-old women) completed a FFQ and biomarker assessments using doubly labeled water (DLW) for total energy expenditure (TEE) and 24-h urinary nitrogen. Correction methods evaluated were as follows: 1) DLW-TEE; 2) the Institute of Medicine's (IOM's) estimated energy requirement (EER) TEE prediction equation based on sex, height, weight, and age; 3) published NBS total energy TEE prediction (WHI-NBS-TEE) using age, BMI, race, and income; 4) reported protein versus reported energy linear regression-based residual method; and 5) a Goldberg cutoff to exclude subjects reporting energy intakes <1.35 times their basal metabolic rate. Efficacy was evaluated using correlations obtained by regressing corrected protein against biomarker protein (6.25 × urinary nitrogen/0.81). Unadjusted self-reported protein intake from the FFQ (mean=66.7g) correlated weakly (r=0.31) with biomarker protein (mean=74.9g). DLW-TEE-corrected self-reported protein intake (mean=90.7g) had the strongest correlation with biomarker protein (r=0.47). Other energy corrections yielded lower, but still significant correlations: EER, r=0.44 (mean=92.1g); WHI-NBS-TEE, r=0.37 (mean=90.4g); Goldberg cutoff, r=0.36 (mean=88.4g); and residual method, r=0.35 (mean=66.7g). Our data indicate that proportional correction of reported protein intake using a measure of energy requirement from DLW-TEE or IOM-EER performed modestly better than other methods in this cohort. These energy adjustments, however, yielded corrected protein exceeding the biomarker protein, indicating that energy adjustment alone does not eliminate all self-reported protein reporting bias.

Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness.

BackgroundCharacterizing breast cancer progression and aggressiveness relies on categorical descriptions of tumor stage and grade. Interpreting these categorical descriptions is challenging because stage convolutes the size and spread of the tumor and no consensus exists to define high/low grade tumors.MethodsWe address this challenge of heterogeneity in patient‐specific cancer samples by adapting and applying several tools originally created for understanding heterogeneity and phenotype development in single cells (specifically, single‐cell topological data analysis and Wanderlust) to create a continuous metric describing breast cancer progression using bulk RNA‐seq samples from individual patient tumors. We also created a linear regression‐based method to predict tumor aggressiveness in vivo from bulk RNA‐seq data.ResultsWe found that breast cancer proceeds along three convergent phenotype trajectories: luminal, HER2‐enriched, and basal‐like. Furthermore, 31 296 genes (for luminal cancers), 17 827 genes (for HER2‐enriched), and 18 505 genes (for basal‐like) are dynamically differentially expressed during breast cancer progression. Across progression trajectories, our results show that expression of genes related to ADP‐ribosylation decreased as tumors progressed (while PARP1 and PARP2 increased or remained stable), suggesting the potential for a differential response to PARP inhibitors based on cancer progression. Additionally, we developed a 132‐gene expression regression equation to predict mitotic index and a 23‐gene expression regression equation to predict growth rate from a single breast cancer biopsy.ConclusionOur results suggest that breast cancer dynamically changes during disease progression, and growth rate of the cancer cells is associated with distinct transcriptional profiles.

Enhancing Blurred Low-Resolution Images via Exploring the Potentials of Learning-Based Super-Resolution

This paper aims to propose a candidate solution to the challenging task of single-image blind super-resolution (SR), via extensively exploring the potentials of learning-based SR schemes in the literature. The task is formulated into an energy functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The functional includes a so-called convolutional consistency term which incorporates a nonblind learning-based SR result to better guide the kernel estimation process, and a bi-[Formula: see text]-[Formula: see text]-norm regularization imposed on both the super-resolved sharp image and the nonparametric blur-kernel. A numerical algorithm is deduced via coupling the splitting augmented Lagrangian (SAL) and the conjugate gradient (CG) method. With the estimated blur-kernel, the final SR image is reconstructed using a simple TV-based nonblind SR method. The proposed blind SR approach is demonstrated to achieve better performance than [T. Michaeli and M. Irani, Nonparametric Blind Super-resolution, in Proc. IEEE Conf. Comput. Vision (IEEE Press, Washington, 2013), pp. 945–952.] in terms of both blur-kernel estimation accuracy and image ehancement quality. In the meanwhile, the experimental results demonstrate surprisingly that the local linear regression-based SR method, anchored neighbor regression (ANR) serves the proposed functional more appropriately than those harnessing the deep convolutional neural networks.