Assumption Of Normal Distribution Research Articles

Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning

In deep reinforcement learning, estimating the value function to evaluate the quality of states and actions is essential. The value function is often trained using the least squares method, which implicitly assumes a Gaussian error distribution. However, a recent study suggested that the error distribution for training the value function is often skewed because of the properties of the Bellman operator, and violates the implicit assumption of normal error distribution in the least squares method. To address this, we proposed a method called Symmetric Q-learning, in which the synthetic noise generated from a zero-mean distribution is added to the target values to generate a Gaussian error distribution. We evaluated the proposed method on continuous control benchmark tasks in MuJoCo. It improved the sample efficiency of a state-of-the-art reinforcement learning method by reducing the skewness of the error distribution.

Bayesian model updating of super high‐rise building for construction simulation

SummaryA finite element model was established using SAP2000 software for the C1 tower, a super high‐rise building in the second phase of the Nanjing Financial City project, and the construction process of the tower was simulated. The C1 tower adopts a frame core tube extension arm and ring truss structure system, with 87 floors above ground and five floors underground. The roof structure has an elevation of 416.6 m. Precise measurements of inter‐story compression deformation were conducted using advanced surveying equipment. Sensitivity analysis, based on the finite difference method, identified the shear wall elastic modulus, frame column elastic modulus, steel beam elastic modulus, and shear wall unit weight as four highly influential parameters. Employing the Bayesian principle, the Markov Chain Monte Carlo (MCMC) method was applied to determine the posterior density probability function of the parameters targeted for modification. Subsequently, the Metropolis–Hastings (MH) sampling algorithm was employed to refine the C1 Tower model. This refinement significantly reduced the root mean square error between the measured and simulated vertical displacements, achieving an error reduction of approximately 10% from 6.082 to around 2.160. The modified material parameters, for the most part, adhered to a normal distribution assumption and exhibited mean values in the posterior probability density functions for the elastic modulus of Q345 steel beams, C70 frame columns, and C60 shear walls. Compared to the initial finite element parameters, the variation range was approximately 13% to 17%. These results serve as a validation of the effectiveness of the proposed method.

Probabilistic modeling of double linear damage accumulation and its application in fatigue reliability analysis

AbstractIn this paper, a probabilistic double linear damage accumulation model is proposed to characterize the uncertainty of damage accumulation using the statistics of material fatigue life. The probabilistic modeling framework is performed under normal distribution assumption of fatigue life. By applying a one‐to‐one probability density function transformation method, the probability distribution of critical damage and cumulative damage can be extracted and derived. The proposed probabilistic damage accumulation model is then used for fatigue reliability analysis by building the reliability formulas with respect to the cumulative damage and the critical damage under both constant and variable amplitude loadings. It treats the mean and variance of cumulative damage as time‐dependent values that dynamically vary with increase in usage life. The reliability can be easily calculated by limited data necessary from fatigue life samples. The applicability of the proposed method is demonstrated using the experimental data of two metallic materials under constant amplitude and two‐block loadings. The results show that the model predictions are in good agreement with the experimental observations under constant amplitude loading and L‐H loading sequence. In addition, the proposed method provides better predictive ability than the Miner rule under constant amplitude loading and H‐L loading sequence. Moreover, the predicted reliability plots can typically present a two‐stage fatigue characteristic during crack initiation and crack propagation periods.

대학전공선택검사의 성차에 의한 차별기능문항 탐색

Objectives Paying attention to the phenomenon of a gender gap in the choice of a major in the engineering de-partment, this study set out to examine whether there was differential item functioning by gender and what caused it by analyzing the items related to engineering in the Korean College Major Interest Inventory, 2nd Edition(KCMII-2).
 Methods For this purpose, the study used the data of 9,069 respondents who answered the activity preference items and the data of 9,040 respondents who answered the vocational preference items in the engineering sec-tion of the inventory in its analysis before checking if the data met the normal distribution and unidimensionality assumption requirements. Differential item functioning was extracted with the ‘lordif’ package of R and the PARSCALE program. The items whose differential function was found both in the programs were chosen as the final differential item functioning.
 Results The results of testing the assumption of unidimensionality suggested the possibility of other dimensions of latent trait beyond activity and career preferences. Subsequent analysis conducted through differential item functioning analysis revealed that nine out of the 30 activity preference items and seven out of the 30 vocational preference items were extracted as differential item functioning. All these items except for one activity preference item were favorable to female students. The activity preference items whose differential function included majors in life science and biotechnology, computer software, environmental studies and energy, and chemistry and chemical engineering. In terms of career preference, the differential functioning items encompassed majors in life science and biotechnology, chemistry and chemical engineering, and environmental studies and energy.
 Conclusions The study then inferred the causes of the differential item function extracted based on the findings and discussed the future direction of career education to help high school students overcome the gender gap in the choice of an engineering career and make a reasonable and sustainable career and occupation choice.

Statistical Methods for Selective Biomarker Testing

AbstractBiomarkers are critically important tools in modern clinical diagnosis, prognosis, and classification/prediction. However, there are fiscal and analytical barriers to biomarker research. Selective Genotyping is an approach to increasing study power and efficiency where individuals with the most extreme phenotype (response) are chosen for genotyping (exposure) in order to maximize the information in the sample. In this article, we describe an analogous procedure in the biomarker testing landscape where both response and biomarker (exposure) are continuous. We propose an intuitive reverse-regression least squares estimator for the parameters relating biomarker value to response. An expression for robust standard error and corresponding confidence interval are derived. A simulation study is used to demonstrate that this method is unbiased and efficient relative to estimates from random sampling when the joint normal distribution assumption is met, and to compare the estimator to an alternative under a related sampling design. We illustrate application of proposed methods on data from a chronic pain clinical trial.

Integrated Stochastic Underground Mine Planning with Long-Term Stockpiling: Method and Impacts of Using High-Order Sequential Simulations

The integrated optimization of stope design and underground mine production scheduling is an approach that has been shown to effectively leverage the synergies among these two underground mine planning components to generate truly optimal stope layouts and extraction sequences. The existing stochastic integrated methods, however, do not include several elements of a mining complex, such as stockpiles, due to the computational complexity and non-linearity that they might add to the optimization of the long-term mine plan. Additionally, sequential simulation methods that rely on two-point statistics and Gaussian distribution assumptions are commonly used to generate the input realizations of the mineral deposit. These methods, however, are not able to properly characterize complex spatial geometries or the high-grade connectivity of non-Gaussian and non-linear natural phenomena. The present work proposes an extension of previous developments on the integrated stope design and underground mine scheduling optimization through an expanded stochastic integer programming formulation that incorporates long-term stockpiling decisions. An application of the proposed method at an operating underground copper mine compares the cases in which the geological simulated orebody models are based on high-order and Gaussian sequential simulation methods. The extraction sequence and related final stope design are shown to be physically different. It is seen that the optimization process takes advantage of the better representation of high-grade connectivity when high-order sequential simulations are used, by targeting the areas with grades that follow the mill’s blending requirements and by making less use of the stockpiles. Overall, a 4% higher copper metal production and a resultant 6% higher net present value are observed when high-order sequential simulations are used.

ESTIMASI TAIL VALUE AT RISK SAHAM BLUE CHIPS MENGGUNAKAN COPULA ALI-MIKHAIL-HAQ

When making investments, investors definitely want high returns with low risk. However, high returns are usually accompanied by high risks and vice versa. Value at Risk (VaR) and other measurement tools help manage risk. VaR measures possible losses. However, VaR has weaknesses, thus Tail Value at Risk (TVaR) can be used to evaluate the likelihood of larger losses than VaR. Copula can be used in risk management because it does not require normal distribution assumptions so it is well applied to financial data. The purpose of this research is to use the Ali-Mikhail-Haq copula to estimate TVaR value of blue chip stock portfolios, including those of BRI, BCA, and Bank Mandiri. Data used is the closing price of daily stocks for the period Jan 1 2021 to Jun 30 2023. The results of calculating TVaR value at the 90%, 95%, and 99% confidence level of the combination of BMRI and BBRI stocks are 0,0574; 0,0668; dan 0,0807. At a confidence level of 90%, 95% and 99% TVaR value of the combination of BMRI and BBCA stocks is 0,0569; 0,0669; dan 0,0886. The combination of BBRI and BBCA stocks resulted in TVaR at 90%, 95% and 99% confidence levels are 0,0238; 0,0283; 0,0376.

Attribute Control Chart based on SkSP-R Scheme under Assumption of Half Normal Distribution

Attribute Control Chart based on SkSP-R Scheme under Assumption of Half Normal Distribution

Robust Generalized Scalar-on-Tensor Regression

High-dimensional (HD) data, such as images and profiles, are commonly collected from complex systems and contain significant explanatory and predictive information for effective systems monitoring and control. Therefore, developing accurate and robust predictive models based on HD data is crucial. In literature, various methods, including linear scalar-on-tensor regression, are developed to model a complex system based on HD data. However, existing estimation techniques ignore the presence of outliers and are prone to biased estimations. This paper proposes a robust scalar-on-tensor regression framework that handles multi-dimensional HD input data when the data contain outliers. Our proposed estimation method is constructed using maximum Lq-likelihood estimation instead of the classical maximum likelihood estimation. The asymptotic analysis under the Gaussian distribution assumption and the guideline on choosing the tuning parameter of our proposed method is provided. Several simulations and case studies evaluate the proposed method’s efficacy compared to several benchmark methods in the literature.

Rheumatoid arthritis increases the risk of malignant neoplasm of bone and articular cartilage: a two-sample bidirectional mendelian randomization study

ObjectivePrior research has revealed a heightened prevalence of neoplasms in individuals diagnosed with rheumatoid arthritis (RA). The primary objective of this study is to delve into the causal association between RA and two distinct types of neoplasms: benign neoplasm of bone and articular cartilage (BNBAC) and malignant neoplasm of bone and articular cartilage (MNBAC).MethodsWe employed summary data from genome-wide association analyses (GWAS) to investigate the causal relationship between RA and two neoplasms, BNBAC and MNBAC, using a two-sample bidirectional Mendelian randomization (MR) study design. The IEU OpenGWAS database provided the GWAS summary data for RA, while the Finnish consortium supplied the GWAS summary data for BNBAC and MNBAC. Our analysis involved the utilization of eight distinct MR methods, namely random-effects inverse variance weighted (IVW), MR Egger, weighted median, simple mode, weighted mode, maximum likelihood, penalized weighted median, and fixed effects IVW. Subsequently, we conducted assessments to evaluate heterogeneity, horizontal pleiotropy, outliers, the impact of a single-nucleotide polymorphism (SNP), and adherence to the assumption of normal distribution in the MR analysis.ResultsThe results from the MR analysis revealed that there was no significant genetic association between RA and BNBAC (P = 0.427, odds ratio [OR] 95% confidence interval [CI] = 0.971 [0.904–1.044]). However, a positive genetic association was observed between RA and MNBAC (P = 0.001, OR 95% CI = 1.413 [1.144–1.745]). Conducting a reverse MR analysis, we found no evidence to support a genetic causality between BNBAC (P = 0.088, OR 95% CI = 1.041 [0.994–1.091]) or MNBAC (P = 0.168, OR 95% CI = 1.013 [0.995–1.031]) and RA. Our MR analysis demonstrated the absence of heterogeneity, horizontal pleiotropy, and outliers and confirmed that the effect was not driven by a single SNP. Additionally, the data exhibited a normal distribution.ConclusionThe findings of this study demonstrate that RA constitutes a significant risk factor for MNBAC. In the context of clinical application, it is advisable to conduct MNBAC screening in RA patients and remain vigilant regarding its potential manifestation. Importantly, the outcomes of this investigation introduce a fresh vantage point into the understanding of the tumorigenesis associated with RA.

Stochastic Subspace Identification-Based Automated Operational Modal Analysis Considering Modal Uncertainty

An automated operational modal analysis (AOMA) method that considers the uncertainty in modal parameters is presented and data acquired from actual bridges are used to validate it. The proposed method processes stepwise, from SSI to pre-cleaning, clustering and the removal of outliers. The stochastic subspace identification (SSI) step also calculates the uncertainty of the modal parameters. In this step, the MAC (modal assurance criterion) index and its variability are additionally calculated by exploiting the alteration of the mode shapes. The pre-cleaning stage sorts out the spurious modes by means of the frequency, the coefficient of variation related to the frequency and the damping ratio, as well as the MAC index and its standard deviation. Under the assumption of normal distributions for the frequency and the MAC index, the clustering stage constructs clusters of identical modes with reference to the uncertainty of each mode. The outliers that may be contained in each of these clusters are then removed based upon the frequency, the MAC index and the damping ratio. Values for the parameters that make the proposed method applicable are suggested and are applied unilaterally to three instrumented bridges of different types. The results show that the proposed AOMA method provides accurate mode identification regardless of the bridge type.

Dynamic self-correcting key performance indicator anomaly detection algorithm

ABSTRACT The operation and maintenance of the background system is always an important link to ensure the system’s high availability. With the increasing number of background systems, their operation, and maintenance have to develop from the initial huge-crowd strategy to the direction of intelligence. The key to intelligent operation and maintenance is the abnormal detection of key performance indicators (KPI), such as CPU utilisation. However, the existing KPI anomaly detection algorithms not only cannot select the dynamic threshold under the non-parametric methods but also have no false-positive correction mechanism to correct the false alarms. In order to overcome the above shortcomings, this work proposes a dynamic self-correcting Key Performance Indicator (KPI) anomaly detection algorithm, hereafter referred to as DSCAD. To the best of our knowledge, in the field of KPI anomaly detection, the DSCAD algorithm is the first dynamic threshold algorithm that does not rely on the assumption of normal distribution. Compared with the existing KPI anomaly detection methods, the F-score of the DSCAD algorithm increased by 3% and had the best performance.

Common autoimmune diseases and urticaria: the causal relationship from a bidirectional two-sample mendelian randomization study.

The immune response assumes a pivotal role in the underlying mechanisms of urticaria pathogenesis. The present study delves into an investigation of the genetic causal connections between urticaria and prevalent autoimmune afflictions, notably rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), ulcerative colitis (UC), and Crohn's disease (CD). A bidirectional two-sample Mendelian randomization (MR) analysis was conducted to investigate the causal relationships involving four autoimmune diseases and urticaria. The genome-wide association study (GWAS) summary data of four autoimmune disease were sourced from the IEU OpenGWAS database. The GWAS summary data for urticaria were derived from the Finnish consortium dataset. The principal analytical approach employed in this study was the random-effects inverse variance weighted (IVW) method. Subsequently, a series of sensitivity analyses were performed, encompassing assessments of heterogeneity, horizontal pleiotropy, outliers, "Leave-one-out" analyses, and tests for adherence to the assumption of normal distribution. The random-effects IVW analysis indicate a positive genetic causal association between RA and urticaria (P < 0.001, OR 95% CI = 1.091 [1.051-1.133]). Conversely, SLE, UC, and CD do not exhibit a significant genetic causal relationship with urticaria. The reverse MR analysis reveals a positive genetic causal linkage between urticaria and SLE (P = 0.026, OR 95% CI = 1.289 [1.031-1.612]). However, the analysis demonstrates no substantial genetic causal relationship between urticaria and RA, UC, or CD. Importantly, the genetic causal assessment absence of heterogeneity, horizontal pleiotropy, and outliers. Furthermore, it remains unaffected by any individual single nucleotide polymorphism (SNP), demonstrating adherence to a normal distribution. This investigation establishing RA as a predisposing factor for urticaria. Moreover, urticaria as a plausible risk determinant for SLE. Heightened vigilance is recommended among RA patients to monitor the manifestation of urticaria within clinical settings. Similarly, individuals afflicted by urticaria should duly acknowledge the prospective susceptibility to SLE.

Examınatıon of the leadership characteristics of individual and team sports coaches according to some demographic variables

Coaches' leadership orientations play an important role in their professional success. This study aims to examine coaches' leadership orientation behaviors according to certain socio-demographic variables. In the study, it has been used a type of quantitative research using a 'descriptive survey' model. The research is conducted with individual and team coaches (n=196). Among the coaches, 55.6% are individual coaches, while 44.4% are team coaches. The Multidimensional Leadership Orientation Scale and a Demographic Data Form have been used to collect information. For statistical analysis, it is determined whether the assumption of normal distribution of the data is met or not through Kolmogorov-Smirnov and Shapiro-Wilk tests. For examining the status of leadership orientation with respect to the relevant variables, parametric (Independent Samples t-test, One-Way Analysis of Variance ANOVA) hypothesis tests have been applied, taking into account the results of the normality tests. The study has revealed that coaches' leadership orientation scores differ significantly according to age and sports branch variables (p&lt;0.05). As a result of the research, it is determined that coaches aged 21-30 and those in individual sports prefer '’Structural and Political’' behavior. Furthermore, coaches' leadership orientations are found not to be influenced by gender, income status, years of coaching, and educational status.

Conditional likelihood inference in a heteroscedastic functional measurement error model

In this paper, we deal with inference about the structural parameters in a heteroscedastic functional measurement error models under the normal distribution assumption. Given a minimal sufficient statistic for the incidental parameters, the conditional maximum likelihood (CML) approach is used. We show that CML estimators have explicit expressions and their sampling distribution is exact. We also show that the classical test statistics to test hypotheses of interest coincide and have exact distributions. We apply the statistical inference tools developed to a data set on comparison of measurement methods.

Measuring the Psychometric Properties of the Customers' Experience with Banking Electronic Services During the COVID-19 Pandemic

The use of banking electronic services (e-services) increased dramatically during the coronavirus disease (COVID‐19) pandemic. This increase has been associated with failures, defects, and shortcomings in such services. Research on this topic is increasingly using different methodologies. The aim of this study was to examine the psychometric properties (i.e., reliability and validity) of a recently developed scale designed to measure customers’ experiences with banking e-services in Saudi Arabia during the COVID‐19 pandemic. This scale has been reported to have acceptable face and content validity. The quantitative research approach is applied and data is collected through a research survey. The scale’s questionnaire has two parts: one part consists of items on demographic variables (e.g., age, gender, and experience of service failure) and the other of 75 items on topics such as perceived information quality, digital commitment, service satisfaction, and cultural impact on choice of e-banking services. A descriptive-explorative, cross-sectional research design was used in this study, which focused on Saudi banks and utilized a non-probability sample of 555 customers. The scale was subjected to normality testing and did not violate normal distribution assumptions. The scale also showed acceptable levels of sphericity, and the Q–Q plot showed a significant relationship among items, reflecting normal distribution of items. The scale’s internal consistency value was 0.986. The results of a principal component analysis showed that all 75 items loaded above the cutoff point of 0.50 and could be subsumed under four factors: information processing and quality of services, perceived justice, security and safety, and e-services recovery and banking choices. The newly developed scale is valid and reliable for the target population and can be used to measure customers’ experiences with banking e-services. Further testing is warranted to ensure the scale’s suitability and stability over time and for other populations.

Component selection for optimal reliability of a repairable load-sharing system

This study determines the number of components in each subsystem that maximizes the reliability of a load-sharing [Formula: see text] out of [Formula: see text] system when repair is performed at the subsystem level. Previous studies have obtained the system availability given that repair is performed for each component failure. We explain how the statistical flowgraph model is used for computing system reliability under the assumption of an inverse Gaussian distribution for the repair of each subsystem and an exponential distribution for the lifetime of a component operating at a fixed load. A closed-form expression is derived for the transition probability between the system states and the moment generating function of the corresponding waiting time distribution conditional on the transition. By comparing the reliability of systems using different numbers of components in subsystems, we explain how the optimal solution is affected by the repair process and the component homogeneity among subsystems. We discover that, if repair is not considered, it is optimal to use a single subsystem that has the maximum reliability across different operating loads, whereas the use of multiple subsystems is beneficial if repair is performed for a subsystem with a small number of components.

Accounting for COVID-19-type shocks in mortality modeling: a comparative study

AbstractMortality shocks such as the one induced by the COVID-19 pandemic have substantial impact on mortality models. We describe how to deal with them in the period effect of the Lee–Carter model. The main idea is to not rely on the usual normal distribution assumption as it is not always justified. We consider a mixture distribution model based on the peaks-over-threshold method, a jump model, and a regime switching model and introduce a modified calibration procedure to account for the fact that varying amounts of data are necessary for calibrating different parts of these models. We perform an extensive empirical study for nine European countries, comparing the models with respect to their parameters, quality of fit, and forecasting performance. Moreover, we define five exemplary scenarios regarding the future development of pandemic-related mortality. As a result of our evaluations, we recommend the peaks-over-threshold approach for applications with a possibility of extreme mortality events.

A distributed multi-area power system state estimation method based on generalized loss function

For power system state estimation, the measurement noise is usually assumed to follow the Gaussian distribution, and the widely used estimator is the weighted least squares (WLS). However, the Gaussian distribution assumption is not always true, and the performance of WLS becomes bad when the measurement noise is non-Gaussian. In this paper, a new distributed state estimation (SE) method is proposed for multi-area power systems. The proposed distributed method is based on the generalized loss function so that it can reduce the influence of non-Gaussian noise and bad data. Further, thanks to the matrix-splitting technology, the proposed distributed method can be implemented in a distributed way so that the computation time in each local area can be reduced. The simulation results carried out in the IEEE 30-bus and 118-bus systems verify the robustness and effectiveness of the proposed distributed SE method.

A Fatal Disaster: Accompanying Aortic Dissection Obscured by Acute Coronary Syndrome.

The present study investigated the characteristics of rare but often fatal cases of acute coronary syndrome and aortic dissection and the factors affecting mortality. HEART score, aortic dissection detection risk score, and Global Registry of Acute Coronary Event (GRACE) Score were calculated by evaluating biochemical tests and symptoms/signs of the 20 patients who were admitted to our clinic with a preliminary diagnosis of acute myocardial infarction and subsequently diagnosed with aortic dissection. The assumption of normal distribution was checked with the Shapiro-Wilk test, and independent group comparisons were made with the t test or the Mann-Whitney U test. Relationships between qualitative variables were analyzed with chi-square tests. Relationships between quantitative variables were analyzed with Pearson or Spearman correlation coefficients. Patients with conditions like hypertension and diabetes are at higher risk for aortic dissection. We recommend the use of the GRACE Score with the risk parameters we have determined for the prediction of operative mortality. We suggest that the axillary region and antegrade cerebral perfusion may be preferred as the cannulation area in dissection repair. We emphasize preliminary risk assessment according to the aortic dissection detection risk score and remind physicians that ECG ST-T and troponin changes may be misleading.