Statistical Linearization Method Research Articles

The Effect of ESG Ratings Information on Share Prices

With various encouragement from the world community since the Paris Agreement, companies continue to be encouraged to implement sustainable activities. For this reason, the government through the Financial Services Authority has required public companies to report sustainability activities, also known as ESG (Environment, Social and Governance). The latest development is that public companies listed on the Indonesia Stock Exchange (BEI) are also being assessed regarding ESG activities, which is being carried out by an independent party, namely Morningstar Sustainalitycs. The assessment submitted to the public is related to the score or value of the ESG activities carried out by the company, including whether there is a decrease or increase in value from the previous assessment. This research aims to see whether ESG value information also influences investors' assessments of companies, which is proxied by the company's share price. This research was concluded that there was an influence of information on the decline of ESG risk value provided by the IDX on company share prices using the linear regression statistical method. However the information regarding the ESG Risk clasiffication did not have significant moderating effect on the share price. It is hoped that the implications of this research will increase investor awareness of ESG activities, which will ultimately encourage companies to implement ESG activities seriously.

Dynamic reliability estimation of onshore wind turbine tower based on frequency-domain method

With the upscaling of wind turbine, the issue of dynamic reliability of wind turbine towers has become increasingly evident. However, the complexity and nonlinearity of wind turbine dynamics present significant challenges in evaluating their dynamic reliability under stochastic loads, requiring substantial computational resources. In this paper, a computationally cost-effective frequency-domain method is proposed as a means of estimating the dynamic reliability of an onshore wind turbine tower. An analytical approach is employed to obtain the power spectral density (PSD) of the rated wind speed and load on the rotating blades. Secondly, a dynamic model of onshore horizontal axis onshore wind turbine is established using flexible multi-body dynamics (MBD). The linear time-varying dynamic equation is transformed into a linear time-invariant form through the application of statistical linearization method. Subsequently, complex modal analysis is used to employ the displacements and internal forces of the tower. Moreover, the first-passage reliability and fatigue reliability of the wind turbine tower are determined through power spectral analysis. A numerical example of a 5-MW wind turbine is presented to demonstrate that the proposed method yields results in good agreement with Monte Carlo simulations (MCS), thereby demonstrating its effectiveness. The reliability analysis of the 5-MW wind turbine reveals that over a 20-year design life, the first-passage reliability is 0.9997 when adopting fore-after displacement at the tower top as the failure criterion, and the fatigue reliability of the tower base is 0.9445.

Sequential Mixed Cost-Based Multi-Sensor and Relative Dynamics Robust Fusion for Spacecraft Relative Navigation

The non-redescending convex functions degrade the filtering robustness, whereas the redescending non-convex functions improve filtering robustness, but they tend to converge towards local minima. This work investigates the properties of convex and non-convex cost functions from robustness and stability perspectives, respectively. To improve filtering robustness and stability to the high level of non-Gaussian noise, a sequential mixed convex and non-convex cost strategy is presented. To avoid the matrix singularity induced by applying the non-convex function, the M-estimation type Kalman filter is transformed into its information filtering form. Further, to address the problem of the estimation consistency in the iterated unscented Kalman filter, the iterated sigma point filtering framework is adopted using the statistical linear regression method. The simulation results show that, under different levels of heavy-tailed non-Gaussian noise, the mixed cost strategy can avoid the non-convex function-based filters falling into the local minimum, and further can improve the robustness of the convex function-based filter. Therefore, the mixed cost strategy provides a comprehensive improvement in the efficiency of the robust iterated filter.

A robust and efficient cubature Kalman filter based on the variational Bayesian method and its application in target tracking

Abstract In this paper, we propose a robust cubature Kalman filter (CKF) for nonlinear state-space models with unknown state and measurement noise covariance matrix. We study situations in which sensors are independent of each other. Therefore, the unknown measurement noise variance is modeled as an unknown inverse gamma (IG) distribution. The Gaussian-Student-t-inverse-Wishart (GSTIW) mixture distribution is used to model the one-step prediction distribution. Modeling generates numerous unknown parameters. Therefore, we adopt the statistical linearization method to linearize the observation function and then estimate the state and parameters separately to reduce the computational burden of estimating unknown parameters, significantly improving the algorithm’s efficiency. Finally, using the variational Bayesian method, a novel and efficient robust CKF based on the IG distribution and GSTIW mixture distributions (IG-GSTIW-CKF) is obtained. Simulation and experimental results show that the proposed method has better estimation accuracy than several advanced algorithms when sensors are independent. In addition, the efficiency of the proposed algorithm is significantly higher than that of other CKF-based methods.

Analysis of Good Corporate Governance and Intellectual Capital on Profitability Ratio

Increased competition in business at this time is experiencing rapid development. This causes every company to have a strategy in competing so as not to experience bankruptcy and improve the company's financial performance. This study aims to determine the effect of Good Corporate Governance on Financial Performance and to determine the effect of Intellektual Capital on Financial Performance at Commercial and Conventional Banks listed on the Indonesia Stock Exchange (IDX) for the period 2020-2022. The research method that researchers use in solving problems is descriptive and associative research methods with a quantitative approach. The population in this study were conventional bank companies listed on the Indonesia Stock Exchange in 2020-2022. The samples used in this study were 10 commercial bank issuers listed on the IDX in the 2020-2022 period. The data collection techniques used in this research are literature study and documentation. The data analysis technique in this study is the multiple linear regression statistical method using SPSS software version 25. The results showed that there was a negative and insignificant effect of Good Corporate Governance on the Profitability Ratio and there was a positive and insignificant effect between Intellectual Capital on the Profitability Ratio.

Iterative UKF under generalized maximum correntropy criterion for intermittent observation systems with complex non-Gaussian noise

The traditional unscented Kalman filters (UKFs) under the maximum correntropy criterion provide a powerful tool for nonlinear state estimation with heavy-tailed non-Gaussian noise. Nevertheless, the above-mentioned filters may yield biased estimates because the Gaussian kernel function can only handle certain types of non-Gaussian noise. Additionally, the use of statistical linearization methods can result in approximation errors when solving linear observation equations, while the system may also experience observation data loss. Therefore, a new iterative UKF with intermittent observations under the generalized maximum correntropy criterion is proposed for systems with complex non-Gaussian noise, called GMCC-IO-IUKF. Firstly, the connection between the UKF with and without intermittent observations is established by designing a coefficient matrix including intermittent observation variables, so as to derive the UKF with intermittent observations under the maximum correntropy criterion. Secondly, for the measurement update of GMCC-IO-IUKF, a nonlinear regression augmented model that can deal with both prediction and observation errors is established using the coefficient matrix and the nonlinear function. To better adapt to different types of non-Gaussian noise, the generalized Gaussian kernel function is substituted for the traditional Gaussian kernel function. Theoretically, GMCC-IO-IUKF can achieve better estimation performance by directly employing the nonlinear function and the latest iteration value. Finally, a classical target tracking model is used to evaluate the estimation performance and feasibility of our proposed GMCC-IO-IUKF algorithm. It appears from the experiment results that our proposed GMCC-IO-IUKF can not only promote estimation precision but also handle complex non-Gaussian noise flexibly.

Robust recursive sigma point Kalman filtering for Huber-based generalized M-estimation

For nonlinear state estimation driven by non-Gaussian noise, the estimator is required to be updated iteratively. Since the iterative update approximates a linear process, it fails to capture the nonlinearity of observation models, and this further degrades filtering accuracy and consistency. Given the flaws of nonlinear iteration, this work incorporates a recursive strategy into generalized M-estimation rather than the iterative strategy. The proposed algorithm extends nonlinear recursion to nonlinear systems using the statistical linear regression method. The recursion allows for the gradual release of observation information and consequently enables the update to proceed along the nonlinear direction. Considering the correlated state and observation noise induced by recursions, a separately reweighting strategy is adopted to build a robust nonlinear system. Analogous to the nonlinear recursion, a robust nonlinear recursive update strategy is proposed, where the associated covariances and the observation noise statistics are updated recursively to ensure the consistency of observation noise statistics, thereby completing the nonlinear solution of the robust system. Compared with the iterative update strategies under non-Gaussian observation noise, the recursive update strategy can facilitate the estimator to achieve higher filtering accuracy, stronger robustness, and better consistency. Therefore, the proposed strategy is more suitable for the robust nonlinear filtering framework.

Cash dividend: Debt policy and liquidity in Indonesian consumer cyclicals company

This study aims to investigate whether debt policy in Indonesia tends to commit cash dividend and whether liquidity maximizes cash dividend. Using a purposive sampling technique, 16 consumer cyclicals companies were selected as research samples that are wholly registered on the Indonesia Stock Exchange. A total of 142 firm-year observations were provided by their annual reports from 2018-2021 and processed within the multiple linear regression statistical method. The study depicts that debt policy negatively affects cash dividend. Meanwhile, the liquidity ratio cannot maximize or minimize cash dividend payments by companies.

Maximum weighted correntropy filters for nonlinear non‐Gaussian systems

AbstractIn this paper, we focus on the filtering issue for nonlinear non‐Gaussian systems. Considering the limitations of the Gaussian function with a single kernel bandwidth, we design a novel extended version called weighted Gaussian function, which consists of a weighting coefficient and two distinct kernel bandwidths. The additional coefficient can directly balance two kernel bandwidths, which improves the flexibility and performance of the correntropy. We develop a cost function utilizing the suggested weighted Gaussian function and statistical linearization method, followed by deriving a maximum weighted correntropy filter based on the maximum correntropy criterion. The proposed algorithm is demonstrated to converge to a Gaussian filter as the kernel bandwidths approach infinity. In addition, we derive the corresponding information filter, which takes the form of information matrix and information vector. It is computationally efficient and easier to generalize to multisensor systems. The performance of the proposed algorithms is compared with other filters based on the third‐order spherical cubature rule and the fixed point iteration technique in a target tracking system. Simulation results confirm the effectiveness of the new approaches.

The impact of entrepreneurial ecosystems on digital transformations: micro-level analysis

Purpose Entrepreneurial ecosystems (EEs) contribute to organisational success through enabling actors to restructure businesses, stimulating exchanges of resources as well as applications and improvements to technological approaches. Hence, the purpose of this paper is to study the impact of EE micro-level variables on digital transformations. Design/methodology/approach Based on data spanning 5,000 Portuguese firms, the authors selected a random sample of 300, including businesses in the industrial, retail and service sectors. The authors deployed the multiple linear regression statistical method to validate the hypotheses. Findings As a result, the authors may conclude the organisation and the micro-level actors of EEs, owners and their members of staff produce influences on digital transformation processes. Originality/value This study contributes to completing the gap in the literature regarding the shortage of quantitative empirical studies approaching micro-level EE variables from the perspective of their relationships with digital transformation processes.

Wind-Induced Dynamic Response of Inter-Story Isolated Tall Buildings with Friction Pendulum Bearing Based on an Enhanced Simplified Model

Isolation technology, especially for base isolation, is increasingly being applied in earthquake-prone areas. To satisfy some special demands (such as prevention from seawater erosion of an isolation layer, story-adding retrofit of existing buildings, avoidance of collision between base-isolated tall buildings, and so on), the isolation layer sometimes has to be set in the middle of a building to constitute inter-story isolated buildings. This special structural form inevitably encounters strong wind loads during service life, and its wind-resistant performance deserves to be investigated. This study conducts the wind-induced vibration analysis of inter-story isolated tall buildings with friction pendulum bearing (FPB). The nonlinear time domain analysis model and statistical linearization method to compute the wind-induced response of FPB inter-story isolated tall buildings are addressed based on an enhanced simplified model. Considering the independence of the upper and lower structures, two structural design schemes for inter-story isolated tall buildings are provided. Their dynamic characteristics are analyzed, and wind-induced responses are compared. Finally, the accuracy of the statistical linearization method is verified. This study provides an important theoretical basis for the structural design and wind resistance of inter-story isolated tall buildings.

Improved Autuoformer Electricity load forecasting based on model fusion

The development of the power system is undergoing rapid changes. The future direction of power system development is inevitably smart grids. This paper proposes an optimization of the Autoformer model based on model fusion to improve the accuracy of electricity load forecasting. By incorporating the predictive results of traditional statistical multivariate linear regression (MLR) and machine learning method LightGBM into the Autoformer model, we aim to fully leverage the strengths of both traditional statistical and machine learning approaches to enhance the predictive accuracy of the Autoformer model. Experimental results, using mean MAE and MSE as the evaluation metrics, demonstrate that the algorithm proposed in this paper exhibits better performance in the prediction results.

Analisis Model Tarikan Pergerakan Sekolah Di Kota Palangka Raya

The school zone is a land use that can create the trip attraction. The purpose of this study is to model the trip attraction and to analysis the factors that most influence attraction to the Catholic school area in Palangka Raya. The study was conducted at four schools in one zone. Primary data, i.e. the number of vehicles entering the school premises, was obtained through direct observation in the target schools. Secondary data was obtained through documentation and interviews with the operators of the schools. The collected data was analysed using the linear regression statistical method assisted by SPSS software version 25. All variables involved, i.e. number of students, number of teachers, number of school benches, class capacity and class area, are strongly correlated with the variable 'traffic volume'. From the linear regression analysis, the number of students is the independent variable that is highly correlated and significantly influence the trip attraction, as shown by the value of r=0.99 and R2 = 0.998. The model of physical activity attractiveness in this study is Y = -10.686 + 0.934X1. The results of this study contribute to education field especially in transportation, as they support the theory that the number of students significantly influences the trip attraction in school land use

System with nano piezoengine under randomly influences for biomechanics

The sufficient condition of absolute stability system with nano piezoengine by using the derivative of the hysteretic piezoengine deformation is determined for the randomly influences. The set of equilibrium positions of the piezoengine in the control system is stable relative to mathematical expectations, when the condition of absolute stability with the maximum piezo module is met. The statistical linearization method is using for the determination condition of absolute stability control system with the nano piezoengine.

The Role of Debt to Ratio and Profitability in Economic Growth and Company Development

This study analyses the relationship and influence of Debt Asset Ratio (DAR) on company profitability. The data used in this study were obtained from the financial reports of companies listed on the Indonesia Stock Exchange during 2018-2022. The analytical method used is simple linear regression using statistical software. The steps are planning, collecting, analyzing, and interpreting data to answer research questions. DAR and profitability as variables in this study, population, and samples from 6 companies for five years. The influence of Debt to Asset Ratio applied to Company Profitability was analyzed using the linear regression statistical method. Debt to Asset Ratio is the independent variable, and Company Profitability is the dependent variable. The results of the correlation analysis found a linear relationship between the variables. The results of the regression analysis found that there was a shallow level of influence. This study has several limitations on the number of samples, variables, and industrial sectors studied. In addition, other factors also need to be considered-some recommendations for further research. They expanded the study sector, examining other factors such as accounts receivable turnover and company size.

PENGARUH INDEPENDENSI, OBJEKTIVITAS, PENGALAMAN KERJA, KOMPETENSI, INTEGRITAS DAN SKEPTISME PROFESIONALTERHADAP KUALITAS AUDIT (STUDI DI INSPEKTORAT PROVINSI BENGKULU)

This study aims to find empirical evidence regarding the effect of independence, objectivity, work experience, competence, integrity and professional skepticism on audit quality at the Bengkulu Provincial Inspectorate. The type of research used in this research is quantitative research. The type of data used is primary data in the form of values or scores for the answers given by respondents to the questions in the questionnaire. The sampling technique used is the saturated sample/census method.
 Based on sampling through the saturated sample/census method, 46 samples of Bengkulu Province Inspectorate auditors were obtained. The data was processed through the multiple linear regression statistical test method using SPSS software version 21. The results of the study found that competence had a positive effect on audit quality. Meanwhile, independence, objectivity, work experience integrity and professional skepticism have no effect on audit quality.

Robust production forecast and uncertainty quantification for waterflooding reservoir using hybrid recurrent auto-encoder and long short-term memory neural network

History-matching-based production forecast and uncertainty quantification are essential to achieve reliable risk assessment for waterflooding reservoir in the community of petroleum engineering, however, the conventional model-based history matching procedure presents intensive computation-cost due to numerous high-fidelity reservoir simulations. The use of direct forecast approach, e.g., data-space inversion (DSI) and its variants, is urgent to achieve direct production forecast without explicitly performing history matching. This work presents a generic data-driven post-history production forecasting framework using a novel deep recurrent neural network. We construct a hybrid recurrent neural network proxy through combining recurrent autoencoder with long short-term memory neural network, e.g., referred to as RAE-LSTM. The proposed RAE-LSTM takes history prediction and post-history well controls as input, while the post-history production responses (oil and water production, water injection rate) as output. Training samples are constructed by simulating high-fidelity models parameterized with various post-history well-controls and geomodels. Once the deep recurrent neural network proxies are trained offline, the online post-history production forecast and uncertainty quantification can be efficiently achieved by input user-specific well control sequences and history measurement. The proposed proxy model is demonstrated on two examples with varying complexity, e.g., a synthetic 2D Gaussian model and a 3D channelized EGG model. The use of our proposed RAE-LSTM neural network can be regarded as a nonlinear alternative to the DSI-type of linear statistical interpolation method. The comparisons between DSI and RAE-LSTM proxy confirm that the application of our proposed data-driven prediction method can effectively obtain more robust predictions and thus support rapid and reliable decision-making during the process of real-time waterflooding production management and optimization.

PENGARUH PERUBAHAN LAHAN PERTANIAN MENJADI NON PERTANIAN TERHADAP ASPEK SOSIAL EKONOMI MASYARAKAT

The transformation that occurs in the peri-urban area will cause changes in the morphological peri-urban area and affect changes in the form of agricultural land use and have an impact on changes in the socio-economic conditions of the people in the area. The purpose of this study was to determine the influence of changes in agricultural land to non-agriculture on the socio-economic aspects of the community in Jati Agung. This research uses a deductive approach. The data collection method used primary data from questionnaires and interviews, the secondary data collected from Landsat 8 satellite image and BPS South Lampung Regency in 2013-2020. The method of analysis uses the predominant peri-urban zone classification with urban or predominantly urban characteristics divided into Urban Frame Zones and Urban-rural Frame Zones, which have a percentage of agricultural land use less than 50%. Statistical linear regression method is used to see the effect of the transformation of agricultural land use into non-agriculture on the socio-economic aspects of the peoples. The results of statistical tests show that there is a disproportionate effect with a large effect of 29.5% on the total population, 7.6% on population density, 8.6% on Livelihoods and 20.2% on income.

Pengaruh Anggaran Pendapatan Belanja Daerah Terhadap Program Pengentasan Kemiskinan Di Kabupaten Aceh Selatan

South Aceh Regency has 18 scattered sub-districts, and the distribution of the sub-district population is uneven so that it has an impact on differences in the population's economy between Tapak Tuan City and other sub-districts. It is inevitable that poverty will still hit this district, according to data from the Central Statistics Agency (BPS) the poverty rate for South Aceh Regency will reach 32.700 in 2022. To help the less fortunate, the government has made efforts to provide assistance sourced from the regional expenditure revenue budget.This research was made to find out and explain the extent of the influence of regional expenditure budgets on poverty alleviation in South Aceh district. This research was conducted from January to May 2023, using the Simple Linear Regression statistical data analysis method to make it easier for researchers to get the desired results. data brought by this researcher from the Central Statistics Agency (BPS) of South Aceh RegencyThe regression coefficient obtained is 4.468E-6 with a significant value of 0.259> 0.05 where it is shown that the coefficient is positive, so what is obtained is that there is a close relationship between the regional expenditure budget and poverty, where if the APBD budget has increased then there is also an increase in poverty. From the results of R square, it is found that 77.3% of this can be used to see the magnitude of the influence. From the calculated t value of 3.197%, which means it is in the receiving area of Ha. F value count 10.220

Alongwind and crosswind response of friction-pendulum base-isolated high-rise buildings

Base isolation technology is widely accepted as an effective strategy for the seismic design of buildings. Although the elastomeric isolator is still extensively adopted for the base-isolation system in practice, there is a trend to use the frictional pendulum bearing (FPB) due to its high vertical bearing capacity, stable performance and good recentering capability. In this paper, the dynamic response of base-isolated high-rise buildings with FPB having a velocity-dependent friction coefficient subjected to uncoupled alongwind and crosswind loads is studied. The response characteristics calculated from response history analysis (RHA) are analyzed. Meanwhile, two statistical linearization methods are utilized to estimate the standard deviation (STD) of the structural response and to obtain the equivalent parameters of the linearized system. The time-varying mean of the base displacement under alongwind load with a non-zero mean is also discussed by the linearization method. Finally, a parameter analysis of the isolation bearing is performed to investigate the effects of the radius and friction coefficient of the sliding plate on the wind-induced vibration. This study highlights the strong nonlinear response characteristics of base-isolated high-rise buildings with FPB and offers design guidelines under strong winds.