Stochastic Analysis Method Research Articles

The Efficiency of Islamic Banks in the Middle East and Southeast Asia: Stochastic Frontier Approach

<p>This research aims to compare and analyze the efficiency of Islamic banking in Southeast Asia and the Middle East by examining particular inputs such as third-party funds, operational expenses, labor costs, and return on assets, focusing on financing as the output. This quantitative study uses a purposive sampling technique to sample 30 sharia banks from Southeast Asia and 30 sharia banks from the Middle East from 2015 to 2022. The data used is secondary data from BankFocus and the annual financial reports of each bank. Data was processed using the Frontier 4.1 application with the Stochastic Frontier Analysis method. The results of this research show that the efficiency of sharia banking in the Middle East is higher than sharia banking in Southeast Asia. On the other hand, the return on assets has no impact on financing, but the input of third-party funds, operational expenses, and labor costs all have an impact. These findings show the importance of efficiently supervising the management of third-party funds and costs incurred by banks in order to improve banking performance. Hence, this will ultimately lead to an increase in the efficiency of sharia banking and a growth of banking profits.</p>

Fixed/predefined-time generalized synchronization for stochastic complex dynamical networks with delays

<abstract><p>In this paper, the fixed/predefined-time generalized synchronization problem of stochastic complex dynamical networks with delays is studied for the first time. First, based on the feedback controller without linear terms, the results show that the controlled system has strong stability. Second, stochastic analysis methods, inequality techniques, and an extension of the existing fixed/predefined-time stability lemma ($ \eta $ range extension) are used to make the results of this paper more general. The sufficient conditions for generalized synchronization are established, and the settling time independent of the initial values are given. To illustrate the theoretical results, a numerical example is given.</p></abstract>

The Stationary Distribution and Extinction of a Stochastic Five‐Dimensional COVID‐19 Model

This paper investigates a stochastic SEIAIR epidemic model with nonlinear incidence and contacting distance to describe the transmission dynamics of COVID‐19. Firstly, we show the global existence of positive solution of the system. Then, using the Lyapunov function method and theory of stochastic analysis, we set out the sufficient conditions of the existence and uniqueness of an ergodic stationary distribution to the stochastic model. Furthermore, we obtain the sufficient condition for extinction of the disease. Finally, we go through some numerical simulations to demonstrate the theoretical results. Our study extends and improves the results of previous studies.

Productivity analysis and technical efficiency of sweet corn farming

This study examines the determinants of productivity in Secada F1 sweet corn varieties. We conducted a survey involving 34 selected farmers to assess production efficiency using the Stochastic Frontier Analysis (SFA) method. The results highlight the significant impact of precise Lannet pesticide application and efficient labor input on sweet corn yield. Notably, our findings indicate that judicious pesticide use and skilled labor management lead to increased crop production. Technical efficiency assessments demonstrated that the majority of farmers operate within a range of 0.69 to 0.99, with an average of 0.96, highlighting their effective utilization of resources. These insights have the potential to guide and inform agricultural practices, offering valuable guidance for the industry as it navigates the intricate landscape of modern agriculture. In a contemporary context marked by agricultural complexities, these revelations contribute to the pursuit of sustainable growth within the sector.

Pinning Synchronization for Stochastic Complex Networks With Randomly Occurring Nonlinearities: Tackling Bit Rate Constraints and Allocations.

In this article, the ultimately bounded synchronization problem is investigated for a class of discrete-time stochastic complex networks under the pinning control strategy. Communication between system nodes and the remote controller is facilitated via wireless networks subject to bit rate constraints. The system model is distinguished by the inclusion of randomly occurring nonlinearities. A coding-decoding transmission mechanism under constrained bit rates is introduced to characterize the digital transmission process. To achieve synchronization of the network nodes with the unforced target node, a pinning controller is specifically devised based on the information from partially selected nodes. Through the application of the stochastic analysis method, a sufficient condition is derived for ensuring the mean-square boundedness of the synchronization error system. In addition, an optimization algorithm is introduced to address bit rate allocation and the design of desired controller gains. Within the presented theoretical framework, the correlation between the mean-square synchronization performance and bit rate allocation is further elucidated. To conclude, a simulation example is provided to substantiate the efficacy of the recommended pinning control approach.

Adaptive Neural Consensus of Unknown Non-Linear Multi-Agent Systems with Communication Noises under Markov Switching Topologies

In this paper, the adaptive consensus problem of unknown non-linear multi-agent systems (MAs) with communication noises under Markov switching topologies is studied. Based on the adaptive control theory, a novel distributed control protocol for non-linear multi-agent systems is designed. It consists of the local interfered relative information and the estimation of the unknown dynamic. The Radial Basis Function networks (RBFNNs) approximate the nonlinear dynamic, and the estimated weight matrix is updated by utilizing the measurable state information. Then, using the stochastic Lyapunov analysis method, conditions for attaining consensus are derived on the consensus gain and the weight of RBFNNs. The main findings of this paper are as follows: the consensus control of multi-agent systems under more complicated and practical circumstances, including unknown nonlinear dynamic, Markov switching topologies and communication noises, is discussed; the nonlinear dynamic is approximated based on the RBFNNs and the local interfered relative information; the consensus gain k must to be small to guarantee the consensus performance; and the proposed algorithm is validated by the numerical simulations finally.

Can China's regional carbon market pilots improve power plants' energy efficiency?

Power plants are facing huge climate change and policy risks. This paper identifies the effect of China's carbon emissions trading system (ETS) on plants' energy efficiency. For this purpose, we use a two-step approach. In the first stage, we apply a Meta-frontier Stochastic Frontier Analysis (MSFA) method to estimate the total-factor energy efficiency of China's large coal power plants. In the second stage, we use a bootstrapped truncated difference-in-differences (BT-DID) estimator to investigate the ETS' impact on power plants. Results show that the ETS trading policy significantly improves participating plants' energy efficiency by 0.043, compared to non-ETS plants, while the announcement policy doesn't. Besides, several other methods are introduced to solve the potential autocorrelation and heteroskedasticity problems in the second-stage regression. Further, we unveil that the trading policy improves plants' energy efficiency by reducing coal consumption without affecting power generation. Finally, the heterogeneity analysis proves that China Southern Power Grid, local plants, and plants with high carbon prices benefit more from ETS.

Classification Study of New Power System Stability Considering Stochastic Disturbance Factors

Power system instability causes many local or large-scale power outage accidents. To maintain sustainable development, a new power system construction aimed at maximizing new energy consumption is being put on the agenda. However, with a large increase in stochastic disturbance factors (SDFs), the system gradually shows strong stochasticity, and the stability presents greater complexity. It is necessary to analyze the impact on the system based on different processing methods of SDFs to maintain system stability. This paper delves into the impact of SDFs on system stability by analyzing and summarizing both stochastic variables and processes. Initially, the SDFs in the power system are meticulously analyzed and categorized. When the SDFs are treated as stochastic variables, the probabilistic stability is classified and evaluated based on a probability analysis method, which includes the probabilistic small-disturbance stability, the probabilistic transient stability, and the probabilistic voltage stability. When the SDFs are treated as stochastic processes, the stochastic stability is classified and evaluated by using a stochastic analysis method, including the stochastic small-disturbance stability, the stochastic transient stability, and the stochastic voltage stability. Finally, the research perspectives on SDFs and system stability are discussed and prospected.

The SMAA-MABAC approach for healthcare supplier selection in belief distribution environment with uncertainties

Supplier selection is critical to the healthcare industry, especially in emergencies. Accurate and timely supply of healthcare resources can reduce economic cost and ensure the safety of people’s lives. However, the healthcare supplier market is enormous and complex; making accurate evaluations of different suppliers and helping managers choose the most suitable one when considering many factors is becoming an important task. Many multicriteria decision-making (MCDM) methods have been applied to supplier selection in existing literature. However, the research on healthcare supplier selection still needs to be completed. In this paper, to deal with healthcare supplier selection problems when there is no sufficient time and information to decide with uncertainty, the stochastic multicriteria acceptability analysis (SMAA) method and multi-attributive border approximation area comparison (MABAC) method are first combined by taking both the advantages of SMAA and MABAC methods when the assessments are expressed by belief distributions (BDs). In the SMAA-MABAC method, the global ignorance in BDs and the uncertain criteria weights are considered by judging the feasible space to derive the final rank of healthcare suppliers with the most possible. The more straightforward calculation process of SMAA-MABAC makes it faster to make an accurate choice among various suppliers. Moreover, it is flexible to generate different recommended suggestions when diverse preference information is added in SMAA-MABAC. Finally, a case study of healthcare supplier selection is examined for managers based on distributed reviews, and targeted selection advice is given concerning different demands. Besides, the sensitive analysis is provided to better understand the influence of the criteria weights on the proposed model. The results of the comparative analysis also show the effectiveness and rationality of the proposed method.

Nonfragile state estimation for semi-Markovian switching CVNs with general uncertain transition rates: An event-triggered scheme

This paper tackles the problem of nonfragile state estimation for semi-Markovian switching complex-valued networks with time-varying delay. The concerned transition rates of the semi-Markov process are uncertain, including both the completely unknown ones and the inaccurately known ones with known bounds. To reduce the communication burden, a particular event-triggered generator is constructed, which depends on the latest available measurement output and a predefined positive threshold. Combining the stochastic analysis method with the Lyapunov stability theory, some less conservative criteria are obtained to ascertain the global asymptotic stability of the estimation error system in the mean-square sense. In addition, by solving some matrix inequalities, the desired nonfragile estimator gains are explicitly designed. Finally, a numerical example with simulations is given to illustrate effectiveness of the established estimation scheme.

A stochastic LATIN method for stochastic and parameterized elastoplastic analysis

The LATIN method has been developed and successfully applied to a variety of deterministic problems, but few work has been developed for nonlinear stochastic problems. This paper presents a stochastic LATIN method to solve stochastic and/or parameterized elastoplastic problems. To this end, the stochastic solution is decoupled into spatial, temporal and stochastic spaces, and approximated by the sum of a set of products of triplets of spatial functions, temporal functions and random variables. Each triplet is then calculated in a greedy way using a stochastic LATIN iteration. The high efficiency of the proposed method relies on two aspects: The nonlinearity is efficiently handled by inheriting advantages of the classical LATIN method, and the randomness and/or parameters are effectively treated by a sample-based approximation of stochastic spaces. Further, the proposed method is not sensitive to the stochastic and/or parametric dimensions of inputs due to the sample description of stochastic spaces. It can thus be applied to high-dimensional stochastic and parameterized problems. Five numerical examples demonstrate the promising performance of the proposed stochastic LATIN method.

Phân tích hiệu quả của các công ty bảo hiểm ở Việt Nam

The paper analyzes the efficiency of non-life insurance companies in Vietnam by parametric approach with Stochastic Frontier Analysis (SFA) method. The data were taken from financial statements of insurance companies for the period 2015 - 2020. The results show that the mean efficiency of all non-life insurance companies in the research period is 0.524, the lowest efficiency is 0.092, the highest efficiency is 0.852. Among the non-life insurance companies studied, Tokio Marine had the highest efficiency, with a mean efficiency over the study period is 0.852, and the Global had the lowest efficiency e, with an mean efficiency during the study period is 0.076. This result also shows that the efficiency of Vietnamese non-life insurance companies in the period 2015 - 2020 is relatively low, with this result if the average output are constant, insurance companies can maximum reduction of 43.7% input.

A non‐fragile approach to fault estimation for a class of multi‐rate systems under binary encoding schemes

AbstractThis article is concerned with the non‐fragile fault estimation issue for a class of multi‐rate systems under binary encoding schemes (BES). A specific dynamics relationship with zero second‐order difference is employed to characterize the evolution of the actuator fault. The signal communication is regulated by the BESs, where the resulting random bit errors are characterized by a certain stochastic noise. The aim of this article is to design a fault estimator such that the estimation error dynamics is exponentially ultimately bounded in mean square. First, an equivalent single‐rate system is formulated via the lifting technique. Then, by virtue of the stochastic analysis method and the matrix inequality approach, sufficient conditions are developed to ensure the existence of the desired fault estimator. Finally, the effectiveness of the proposed estimator design strategy is testified through a simulation example.

Towards energy conservation by constructing more transportation infrastructure?: An endogenous stochastic frontier analysis framework

Whether constructing more transportation infrastructure can be helpful for the achievement of energy conservation is a long-running and debatable issue. To answer this question, the relationship between transportation infrastructure and energy efficiency must first be clarified. Nonetheless, the existence of the endogeneity problem poses a challenge to defining the relationship. In this paper, an endogenous stochastic frontier analysis method is used to investigate the influence of transportation infrastructure on energy efficiency. Based on the prefecture-city level panel data in China, we find that after addressing the endogeneity problem, the impact of transportation infrastructure on energy efficiency increases dramatically. Moreover, this impact is more pronounced in small-scale cities compared to large and medium-scale cities. Regardless of the measurement of transportation infrastructure, instrumental variable, or production function form, we get the similar conclusions, demonstrating the robustness of our findings. Additional simulation analysis shows that the energy conservation potential would be 1222–2935 million kilowatt hours if the level of transportation infrastructure could be optimized. We recommend accelerating the transportation infrastructure construction, particularly in the small-scale cities so as to boost the energy efficiency and achieve energy conservation targets.

PDEM-based seismic performance evaluation of circular tunnels under stochastic earthquake excitation

ABSTRACT Tunnels are a vital component of urban infrastructure that must be robust against seismic hazards. Given the randomness of earthquake occurrence, the seismic response of tunnel structures mut be studied by stochastic analysis methods. To this end, this study proposes a probability density evolution method (PDEM)-based framework to investigate the seismic performance of a circular tunnel under stochastic earthquake excitation. First, a suite of nonstationary earthquake motions compatible with the seismic design code was derived using a stochastic earthquake model. Then, a series of nonlinear dynamic numerical simulations were conducted for a typical circular tunnel that considers the soil-structure interaction. Finally, using the tunnel inclination angle as the performance index, the probability density function of the structural response of the tunnel was solved using the PDEM to obtain the corresponding exceedance probabilities of the tunnel under various damage states. The results show that the PDEM-based framework can be applied to evaluate the seismic performance of circular tunnels and could serve as a reference on the seismic fragility of tunnels and underground structures.

Static homotopy response analysis of structure with random variables of arbitrary distributions by minimizing stochastic residual error

The modelling of realistic engineering structures with uncertainties often involves various probabilistic distribution types, which bring forward higher requirements for the generality of stochastic analysis methods. This paper proposes a novel stochastic homotopy method to evaluate the static response of the structure with random variables of arbitrary distributions. In this method, a homotopy series expansion is used to approximate the stochastic static response, and the optimal form of the expansion can be determined by minimizing the residual error about the stochastic static equilibrium equation regardless of distribution types of random variables. The numerical results of a logarithmic function and a thin plate show that the new method exhibits excellent accuracy and stability compared to the homotopy stochastic finite element method depending on the sample selection. Compared with the arbitrary polynomial chaos method (aPC), the proposed method is more efficient under equivalent accuracy. On the other hand, as the expansion order increases, this new method shows better convergence than the aPC method and the perturbation stochastic finite element method in the case of non-Gaussian distributed random variables of large fluctuation. In addition, a cable-stayed bridge example illustrates the application of the proposed method on the large-scale structure.

A Granular Computing-Driven Best–Worst Method for Supporting Group Decision Making

In group decision making (GDM), there are seldom ideal scenarios that all the preference information given by all individuals reach a highly level of agreement. Conflicts are present in the information fusion process and decision makers (DMs) have to negotiate and reconcile differences. To address this issue, it becomes inevitable to consider intelligent GDM method. In this article, we propose the granular neural network (GNN) to realize the aggregation process from the perspective of granular computing and machine learning. Our study is involved in an extension of best–worst method to the GDM scenario. The procedure is outlined as follows: first, information granules are allocated around the prototype of individuals’ preferences, complying with the principle of justifiable granularity. Thereby, the granular inputs are brought into a well-trained GNN. An adaptive particle swarm optimization algorithm is applied to optimize allocation of information granules. We calculate the threshold of consistency index for this granular model. Finally, a case study about hotel selection on Booking.com is presented to illustrate the performance of the proposed model. In addition, we use the stochastic analysis method to randomize the weights of group members with the objective to assess the robustness of the model. The feasibility and validity of the model are demonstrated by completing comparative analysis. The originality of this article is to establish a real data-driven granular GDM model both considering the optimization of group consistency and consensus.

Measurement of Education Effectiveness in the BRICS countries and Turkey

The high literacy rate in a country shows that educational attainment is progressing well and efficiently. In this study, we aim to compare the effectiveness of education in the BRICS countries - Brazil, India, Russia, China, South Africa - and Turkey. We use the Stochastic Frontier Analysis method to measure the technical efficiency of education. The study utilizes 2017 and 2018 data, and linear functions to examine technical efficiency. The dataset comprises cross-sectional data. As a result, South Africa and India show the lowest technical efficiency scores, followed by China. Russia, Brazil, and Turkey scored high on technical efficiency for 2017 and 2018. This means that education levels in South Africa and India are unequal and shared among all residents. The 2017 and 2018 productivity scores are the same at 0.99% in Turkey, Russia, and Brazil. India's productivity score increased by 0.10% to 0.55% in 2018. Likewise, the productivity score calculated for China decreased by an average of 0.07 percent to 0.70 percent. However, South Africa's productivity score fell by 0.08 percent to 0.01 percent in 2018.

Leader-following identical consensus for Markov jump nonlinear multi-agent systems subjected to attacks with impulse

The issue of leader-following identical consensus for nonlinear Markov jump multiagent systems (NMJMASs) under deception attacks (DAs) or denial-of-service (DoS) attacks is investigated in this paper. The Bernoulli random variable is introduced to describe whether the controller is injected with false data, that is, whether the systems are subjected to DAs. A connectivity recovery mechanism is constructed to maintain the connection among multi-agents when the systems are subjected to DoS attack. The impulsive control strategy is adopted to ensure that the systems can normally work under DAs or DoS attacks. Based on graph theory, Lyapunov stability theory, and impulsive theory, using the Lyapunov direct method and stochastic analysis method, the sufficient conditions of identical consensus for Markov jump multi-agent systems (MJMASs) under DAs or DoS are obtained, respectively. Finally, the correctness of the results and the effectiveness of the method are verified by two numerical examples.

Banking Credit Risk and Efficiency: Some Countries in ASEAN

Banking efficiency is an important strategy to increase competitiveness. The ultimate goal of improving bank performance is the ability of business groups, banks, or countries to excel in competition. Banking can increase competitiveness in various ways, including increasing efficiency. This study presents an empirical analysis of the effect of credit risk on the value of banking cost efficiency in ASEAN. Cost efficiency is measured using panel data of banks in 10 ASEAN countries, employing stochastic frontier analysis and assuming a fixed effect. The efficiency is obtained using the Panel Stochastic Frontier Analysis method. The relationship between loan risk and efficiency is assessed using a linear regression model, specifically, Feasible Generalized Least Squares. In general, banking efficiency in ASEAN exceeds 80%. Another finding from this study is a negative relationship between credit risk and banking efficiency. In this case, the risk that most significantly reduces efficiency is the risk obtained from the loan-to-asset ratio indicator. The greater the risk the bank takes, the lower the cost-efficiency value of the bank. The implications of this research include that bank managers must reduce credit risk to increase the efficiency of bank operational costs.