Error Variance Research Articles

Comparative Analysis of Univariate SARIMA and Multivariate VAR Models for Time Series Forecasting: A Case Study of Climate Variables in Ninahvah City, Iraq

This study involves a comparison between the application of the univariate SARIMA model and the utilization of VAR methods (vector autoregressive models) for multivariate time series analysis. The analysis is conducted using three-time series variables derived from data representing the monthly average of Humidity (H), Rainfall (R), and Temperature (T) in Ninahvah City, Iraq. Both univariate and multivariate time series approaches are employed to model these series. The paper also outlines the implementation of vector autoregressive, structural vector autoregressive, and structural vector error correction models using the 'vars' package. Additionally, it provides functions for diagnostic testing, estimation of constrained models, prediction, causality analysis, impulse response analysis, and forecast error variance decomposition. Furthermore, it introduces three fundamental functions, VAR, SVAR, and SVEC, for estimating these models. The comparison between the methods is based on evaluating the mean error produced by each approach. The findings of the study indicate that univariate linear stationary methods outperform multivariate models. The analysis of the data was carried out using the R software platform. The primary objective is to assess the performance of univariate and multivariate time series models in handling the given data. The research gap lies in the need for a comparative evaluation of SARIMA and VAR methods for time series analysis in the context of monthly environmental variables. These models were chosen due to their effectiveness in capturing temporal dependencies and interactions among multiple variables in time series data, providing a comprehensive analysis of climatic patterns in Ninahvah City, Iraq. The study aims to address the research gap by comparing these models and justifying their selection based on their capabilities to analyze the specified time series data.

Quantity properties of variate and coefficient in errors-in-variables model under Gaussian noise

Total least-squares (TLS) aims to estimate the unknown parameters of an errors-in-variables (EIV) model from noisy observations when the coefficients are also perturbed by errors. It is helpful to know whether more variates and coefficients lead to more accurate estimation for TLS. In this study, our motivation was to reveal the relationship between the estimation performance and the number of variates or coefficients. The challenge was how to observe a performance change when using additional variates/coefficients. First, the Cramér–Rao bound (CRB) and the hybrid Bhattacharyya–Barankin (HBB) bound were derived for the multivariate EIV model under Gaussian noise typical to most systems. Second, we theoretically affirmed the properties by validating the analytical CRB that additional variates/coefficients were favorable. Third, the quantity properties were verified by simulation about CRB and HBB, considering the source direction estimation using time difference of arrival as an example. We conclude that the estimation performance can be improved if additional variates/coefficients are available. The multivariate TLS should be superior to the univariate one. The conclusions have significance for guiding practical system development.

Analyzing the Role of the Real Estate Sector in the Sectoral Network of the Chinese Economy

The real estate sector in China has strong connections with other sectors. By employing forecast error variance decompositions from least absolute shrinkage and selection operator vector autoregression (LASSO-VAR) model, this study explores the real estate sector's role in China's sectoral network between January 1, 2000, and June 30, 2023. Our analysis reveals that the total sectoral connectedness ranged between 80% and 90% before 2016, but fluctuated between 55% and 90% from 2016 to June 2023. We also identify that shocks primarily spread from the real estate sector to the financial sector from 2014 to 2019. However, this pattern was reversed, with shocks spreading from the financial sector to the real estate sector from early 2020 to June 2023. Furthermore, we observe that the government's long-term mechanisms for the real estate sector have a significant impact on the connection between the real estate sector and the other sectors.

THE APPLICATION OF STANDARD GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (SGARCH) MODEL IN FORECASTING THE STOCK PRICE OF BARITO PACIFIC

Stock potentially yields higher returns than other investments, but is riskier due to volatile prices. To minimize the risk of loss, investors can forecast the stock price to help in deciding whether to buy, sell, or hold the stock. Several methods are available for forecasting the stock price such as ARIMA, ARCH, and SGARCH. ARIMA model works best for series with a constant variance of error. However, almost all stock price series have a non-constant variance of error, known as heteroscedasticity, as such ARIMA isn’t suited for modeling the stock price. In contrast, the SGARCH model can handle series with heteroscedasticity. This makes it better suited for modeling stock prices as they have similar characteristics. PT Barito Pacific (BRPT) is a publicly traded firm that works mainly in petrochemical and geothermal energy. BRPT’s net profit increased in 2023 by 243% and the demand for geothermal energy is expected to increase due to the government's renewable energy transition project. Therefore, this study forecasts the BRPT’s stock price using the SGARCH model with R Studio. The stock price used ranges from October 1st, 2018 to August 16th, 2023 gotten from the Yahoo Finance Website. Based on the least AIC, this study found that ARMA(6,2)-SGARCH(1,1) is the best model for forecasting the stock price. This model gives a very accurate prediction of the stock price from April 1st, 2023 – April 19th, 2023 with a mean absolute error of 78.11, root mean square error of 89.51, and mean absolute percentage error of 9.81%.

Impact of Remittance on Educational Outcomes: Empirical Evidence from Nepal

The remittances are the treasure for irrigating Nepal's economy. Because emigration is increasing, per capita remittance is also increasing in Nepal. However, the question is signaling in its utilization for educational attainment. In this context, the study examines the impact of remittance on educational outcomes in Nepal. The study applied autoregressive distributed lag (ARDL) estimations, Granger causality, forecast error variance decomposition (FEVD) technique, and impulse response functions (IRFs), with time-series data spanning from 1981 to 2021. The overall findings revealed that remittance had a positive impact on educational outcomes in Nepal. In the short run, remittance was found to be crucial for secondary schooling; in the long run, remittance had a significant effect on tertiary schooling in Nepal. The FEVD confirmed that remittance might be the inevitable predictor of educational outcomes in Nepal. The Granger causality also found a unidirectional causality from remittance to educational outcomes. The policymakers and stakeholders, therefore, should focus on funneling remittance toward financing education as well as a productive sector to accelerate per capita GDP and on cropping the demographic dividend in the Nepali economy.

Effects of Recall and Selection Biases on Modeling Cancer Risk From Mobile Phone Use: Results From a Case-Control Simulation Study.

The largest case-control study (Interphone study) investigating glioma risk related to mobile phone use showed a J-shaped relationship with reduced relative risks for moderate use and a 40% increased relative risk among the 10% heaviest regular mobile phone users, using a categorical risk model based on deciles of lifetime duration of use among ever regular users. We conducted Monte Carlo simulations examining whether the reported estimates are compatible with an assumption of no effect of mobile phone use on glioma risk when the various forms of biases present in the Interphone study are accounted for. Four scenarios of sources of error in self-reported mobile phone use were considered, along with selection bias. Input parameters used for simulations were those obtained from Interphone validation studies on reporting accuracy and from using a nonresponse questionnaire. We found that the scenario simultaneously modeling systematic and random reporting errors produced a J-shaped relationship perfectly compatible with the observed relationship from the main Interphone study with a simulated spurious increased relative risk among heaviest users (odds ratio = 1.91) compared with never regular users. The main determinant for producing this J shape was higher reporting error variance in cases compared with controls, as observed in the validation studies. Selection bias contributed to the reduced risks as well. Some uncertainty remains, but the evidence from the present simulation study shifts the overall assessment to making it less likely that heavy mobile phone use is causally related to an increased glioma risk.

High-Order Extended Kalman Filter for State Estimation of Nonlinear Systems

In general, the extended Kalman filter (EKF) has a wide range of applications, aiming to minimize symmetric loss function (mean square error) and improve the accuracy and efficiency of state estimation. As the nonlinear model complexity increases, rounding errors gradually amplify, leading to performance degradation. After multiple iterations, divergence may occur. The traditional extended Kalman filter cannot accurately estimate the nonlinear model, and these errors still have an impact on the accuracy. To improve the filtering performance of the extended Kalman filter (EKF), this paper proposes a new extended Kalman filter (REKF) method that utilizes the statistical properties of the rounding error to enhance the estimation accuracy. After establishing the state model and measurement model, the residual term is used to replace the higher-order term in the Taylor expansion, and the least squares method is applied to identify the residual term step by step. Then, the iterative process of updating the extended Kalman filter is carried out. Within the Kalman filter framework, a higher-order rounding error-based extended Kalman filter (REKF) is designed for the joint estimation of rounding error and random variables, and the solution method for the rounding error is considered for the multilevel approximation of the original function. Through numerical simulations on a general nonlinear model, the higher-order rounding error-based extended Kalman filter (REKF) achieves better estimation results than the extended Kalman filter (EKF) and improves the filtering accuracy by utilizing the higher-order rounding error information, which also proves the effectiveness of the proposed method.

Factors associated with the co-utilization of oral rehydration solution and zinc for treating diarrhea among under-five children in 35 sub-saharan Africa countries: a generalized linear mixed effect modeling with robust error variance

IntroductionEven though childhood diarrhea is treated with a simple treatment solution, it continues to be one of the leading causes of under-five child mortality and malnutrition globally. In resource-limited settings such as Sub-Saharan Africa (SSA), the combination of oral rehydration salts (ORS) and zinc is regarded as an effective treatment for diarrhea; however, its utilization is very low. The purpose of this study was to determine the proportion and associated factors of co-utilization of ORS and zinc among under-five children with diarrhea in SSA.MethodsThe proportion and associated factors of co-utilization of ORS and zinc among under-five children with diarrhea in SSA were determined using secondary data analysis of recent Demographic and Health Surveys (DHS) of 35 SSA countries. The study included a total of 44,341 under-five children with diarrhea in weighted samples. A generalized linear mixed-effects model with robust error variance was used. For the variables included in the final model, adjusted prevalence ratios (aPR) with 95% confidence intervals (CI) were estimated. A model with the lowest deviance value were considered as the best-fitted model.ResultThe pooled proportion of co-utilization of ORS and zinc for the treatment of diarrhea among under five children in SSA countries was 43.58% with a 95% CI (43.15%, 44.01%). Sex of the child, maternal age, residence, maternal educational and employment status, wealth index, media exposure, perceived distance to health facility and insurance coverage were statistically significant determinants of ORS and Zinc co-utilization for treating diarrhea among under five children in SSA.ConclusionOnly less than half of under-five children with diarrhea in SSA were treated with a combination of ORS and zinc. Thus, strengthening information dissemination through mass media, and community-level health education programs are important to scale up the utilization of the recommended combination treatment. Furthermore, increasing health insurance coverage, and establishing strategies to address the community with difficulty in accessing health facilities is also crucial in improving the use of the treatment.

Per- and Polyfluoroalkyl Substances May Be Correlated With Chlamydia trachomatis: Data From the National Health and Nutrition Examination Survey 2003-2016.

Per- and polyfluoroalkyl substances (PFAS) alter immune function increasing infectious diseases risk. We examined the relationship between PFAS and chlamydia. A total of 3965 nonpregnant adults ages 18-39 years from the National Nutrition Examination Survey 2003-2016 cycles were included. Poisson regression with robust error variance estimated the prevalence ratio and 95% confidence intervals for the association between PFAS and chlamydia. A g computation model was used to examine PFAS mixtures and chlamydia. In adjusted age and sex-stratified models, an increase in PFAS mixtures by one quintile was associated with chlamydia in older males and younger females. Associations were not observed before stratification. PFAS exposure associated with higher chlamydia prevalence, but only in stratified models suggesting biological differences by gender and age. However, small sample sizes could have affected the precision of our models.

Manufacturing sensitivity study of tensegrity structures using Monte Carlo simulations

The successful construction of a tensegrity structure requires not only design techniques but also a means to account for any manufacturing or assembly errors. As a tensegrity is prestressed and is often stabilised by its prestress, it requires a form-finding technique to determine the balanced configuration in the design phase; this property also makes it challenging for engineers to evaluate the effect of manufacturing imperfections on the self-equilibrated configuration. In this paper we investigate the sensitivity of tensegrity structures to manufacturing imperfections, using a stiffness-matrix-based form-finding technique in combination with the Monte Carlo simulation method to introduce manufacturing length errors. The effects at the structural level are captured, and two general relationships between the variance of the input manufacturing error and the output distribution of the member tensions, between the variance and the mean across all groups, are observed. By identifying the most sensitive member group, this work provides a design and analysis strategy when the natural length errors of members are considered for tensegrity structures.

Prevalence of Mental Health Disorders and Their Associated Risk Factors Among People Living with HIV in Rwanda: A Cross-Sectional Study

While life expectancy of people living with HIV is increasing, their burden of non-communicable diseases, including mental health disorders, is growing as well. The aim of this study is to investigate the prevalence and identify the risk factors associated with mental health disorders among this population in Rwanda. This cross-sectional study enrolled people living with HIV from 12 HIV clinics across Rwanda using random sampling. Trained HIV nurses conducted the Mini International Neuropsychiatric Interview to estimate the prevalence of major depressive episode, post-traumatic stress disorder, and generalized anxiety disorder. Sociodemographic, psychosocial, and HIV-related data were also collected. Associated risk factors for being diagnosed with one of the mental health disorders were assessed using modified Poisson regression with robust error variance. Of 428 participants, 70 (16.4%) had at least one mental health disorder with major depressive episode being most prevalent (n = 60, 14.0%). Almost all participants were adherent to antiretroviral therapy (n = 424, 99.1%) and virally suppressed (n = 412, 96.9%). Of those diagnosed with a mental health disorder, only few were aware of (n = 4, 5.7%) or under treatment for this mental health disorder (n = 5, 7.2%). Mental health disorders were associated with experiences of HIV-related stigma and discrimination (aRR = 2.14, 95%CI 1.30–3.53, p = 0.003). The results demonstrate underdiagnosis and undertreatment of mental health disorders among Rwandan People Living with HIV. Using HIV nurses to diagnose mental health disorders could serve as a low-cost strategy for integrating mental health care with existing HIV services and could inspire the implementation in other low-resource settings.

Almost Sure Central Limit Theorem for Error Variance Estimator in Pth-Order Nonlinear Autoregressive Processes

In this paper, under some suitable assumptions, using the Taylor expansion, Borel–Cantelli lemma and the almost sure central limit theorem for independent random variables, the almost sure central limit theorem for error variance estimator in the pth-order nonlinear autoregressive processes with independent and identical distributed errors was established. Four examples, first-order autoregressive processes, self-exciting threshold autoregressive processes, threshold-exponential AR progresses and multilayer perceptrons progress, are given to verify the results.

Study protocol to examine the effects of acute exercise on motor learning and brain activity in children with developmental coordination disorder (ExLe-Brain-DCD)

Introduction Developmental coordination disorder (DCD) is one of the most prevalent pediatric chronic conditions. Without proper intervention, significant delays in motor skill performance and learning may persist until adulthood. Moderate-to-vigorous physical exercise has been proven to improve motor learning (adaptation and consolidation) in children with or without disorders. However, the effect of a short bout of physical exercise on motor adaptation and consolidation in children with DCD has not been examined. Furthermore, the role of perceptual-motor integration and attention as mediators of learning has not been examined via neuroimaging in this population. Objectives Therefore, the primary aims of this project will be to compare children with and without DCD to (a) examine the effect of acute exercise on motor learning (adaptation and consolidation) while performing a rotational visuo-motor adaptation task (rVMA), and (b) explore cortical activation in the dorsolateral- and ventrolateral-prefrontal cortex areas while learning the rVMA task under rest or post-exercise conditions. Methods One hundred twenty children will be recruited (60 DCD, 60 controls) and within-cohort randomly assigned to either exercise (13-minute shuttle run task) or rest prior to performing the rVMA task. Adaptation and consolidation will be evaluated via two error variables and three retention tests (1h, 24h and 7 days post adaptation). Cortical activation will be registered via functional near-infrared spectroscopy (fNIRS) during the baseline, adaptation, and consolidation. Discussion We expect to find exercise benefits on motor learning and attention so that children with DCD profiles will be closer to those of children with typical development. The results of this project will provide further evidence to: (a) better characterize children with DCD for the design of educational materials, and (b) establish acute exercise as a potential intervention to improve motor learning and attention.

Total Least Squares Estimation in Hedonic House Price Models

In real estate valuation using the Hedonic Price Model (HPM) estimated via Ordinary Least Squares (OLS) regression, subjectivity and measurement errors in the independent variables violate the Gauss–Markov theorem assumption of a non-random coefficient matrix, leading to biased parameter estimates and incorrect precision assessments. In this contribution, the Errors-in-Variables model equipped with Total Least Squares (TLS) estimation is proposed to address these issues. It fully considers random errors in both dependent and independent variables. An iterative algorithm is provided, and posterior accuracy estimates are provided to validate its effectiveness. Monte Carlo simulations demonstrate that TLS provides more accurate solutions than OLS, significantly improving the root mean square error by over 70%. Empirical experiments on datasets from Boston and Wuhan further confirm the superior performance of TLS, which consistently yields a higher coefficient of determination and a lower posterior variance factor, which shows its more substantial explanatory power for the data. Moreover, TLS shows comparable or slightly superior performance in terms of prediction accuracy. These results make it a compelling and practical method to enhance the HPM.

Inversion of the Permeability Coefficient of a High Core Wall Dam Based on a BP Neural Network and the Marine Predator Algorithm

The parameters’ inversion of saturated–unsaturated is important in ensuring the safety of earth dams; many scholars have conducted some research regarding the inversion of hydraulic conductivity based on seepage pressure monitoring data. The van Genuchten model is widely used in saturated–unsaturated seepage analysis, which considers the permeability connected to the water content of the soil and the soil’s shape parameters. A BP neural artificial network is a mature prediction technique based on enough data, and the marine predator algorithm is a new nature-inspired metaheuristic inspired by the movement of animals in the ocean. The BP neural artificial network and marine predator algorithm are applied in the permeability coefficient inversion of a core-rock dam in China; the results show that in the normal operation status, the BP network shows better accuracy, and the average of the absolute error and variance of the absolute error are both minimum values, which are 2.21 m and 1.43 m, respectively. While the water storage speed changes, the marine predator algorithm shows better accuracy; the objective function is calculated to be 0.253. So, the marine predator algorithm is able to accurately reverse the desired results in some situations. According to the actual condition, employing suitable methods for the inverse permeability coefficient of a dam can effectively ensure the safe operation of dams.

Adaptive Critic Learning-Based Optimal Bipartite Consensus for Multiagent Systems With Prescribed Performance.

Developing a distributed bipartite optimal consensus scheme while ensuring user-predefined performance is essential in practical applications. Existing approaches to this problem typically require a complex controller structure due to adopting an identifier-actor-critic framework and prescribed performance cannot be guaranteed. In this work, an adaptive critic learning (ACL)-based optimal bipartite consensus scheme is developed to bridge the gap. A newly designed error scaling function, which defines the user-predefined settling time and steady accuracy without relying on the initial conditions, is then integrated into a cost function. The backstepping framework combines the ACL and integral reinforcement learning (IRL) algorithm to develop the adaptive optimal bipartite consensus scheme, which contributes a critic-only controller structure by removing the identifier and actor networks in the existing methods. The adaptive law of the critic network is derived by the gradient descent algorithm and experience replay to minimize the IRL-based residual error. It is shown that a compute-saving learning mechanism can achieve the optimal consensus, and the error variables of the closed-loop system are uniformly ultimately bounded (UUB). Besides, in any bounded initial condition, the evolution of bipartite consensus is limited to a user-prescribed boundary under bounded initial conditions. The illustrative simulation results validate the efficacy of the approach.

The Bergen–Social Media Addiction Scale (BSMAS): longitudinal measurement invariance across a two-year interval

ABSTRACT Objective This study aims to examine the longitudinal measurement invariance of the Bergen Social Media Addiction Scale (BSMAS) over a two-year interval, addressing a gap in research on its consistency over time. Method Confirmatory Factor Analysis with a robust maximum likelihood chi-square estimator was utilised to assess the BSMAS among 276 adults (mean age = 31.86 years; SD = 9.94 years; 71% male) at three time points across two years. This method evaluates the scale’s structural consistency and reliability longitudinally. Results The analysis supported full measurement invariance (configural, metric, scalar, and error variance) of the BSMAS, indicating stable measurement properties over time. Furthermore, temporal stability and equivalency of the BSMAS total mean scores were confirmed across the three-time points, suggesting consistent measurement of social media addiction. Conclusions The findings validate the BSMAS as a reliable instrument for measuring social media addiction over extended periods. Demonstrating its psychometric stability enhances its utility for longitudinal studies, making it a valuable tool for tracking changes in social media addiction behaviours. These results have significant implications for future research and clinical practice, highlighting the BSMAS’s applicability in long-term studies.

Robust Principal Component Analysis via Joint Reconstruction and Projection.

Principal component analysis (PCA) is one of the most widely used unsupervised dimensionality reduction algorithms, but it is very sensitive to outliers because the squared l2 -norm is used as distance metric. Recently, many scholars have devoted themselves to solving this difficulty. They learn the projection matrix from minimum reconstruction error or maximum projection variance as the starting point, which leads them to ignore a serious problem, that is, the original PCA learns the projection matrix by minimizing the reconstruction error and maximizing the projection variance simultaneously, but they only consider one of them, which imposes various limitations on the performance of model. To solve this problem, we propose a novel robust principal component analysis via joint reconstruction and projection, namely, RPCA-RP, which combines reconstruction error and projection variance to fully mine the potential information of data. Furthermore, we carefully design a discrete weight for model to implicitly distinguish between normal data and outliers, so as to easily remove outliers and improve the robustness of method. In addition, we also unexpectedly discovered that our method has anomaly detection capabilities. Subsequently, an effective iterative algorithm is explored to solve this problem and perform related theoretical analysis. Extensive experimental results on several real-world datasets and RGB large-scale dataset demonstrate the superiority of our method.

A formal model accounting for measurement reliability shows attenuated effect of higher education on intelligence in longitudinal data.

The effect of higher education on intelligence has been examined using longitudinal data. Typically, these studies reveal a positive effect, approximately 1 IQ point per year of higher education, particularly when pre-education intelligence is considered as a covariate in the analyses. However, such covariate adjustment is known to yield positively biased results if the covariate has measurement errors and is correlated with the predictor. Simultaneously, a negative bias may emerge if the intelligence measure after higher education has non-classical measurement errors as in data from the 1970 British Cohort Study that were used in a previous study of the effect of higher education. In response, we have devised an estimation method that used iterated simulations to account for both classical measurement errors in the covariate and non-classical errors in the dependent variable. Upon applying this method in a reanalysis of the data from the 1970 British Cohort Study, we find that the estimated effect of higher education diminishes to 0.4 IQ points per year. Additionally, our findings suggest that the impact of higher education is somewhat more pronounced in the initial 2 years of higher education, aligning with the notion of diminishing marginal cognitive benefits.

Operative Time and Accrual of Postoperative Complications in Minimally Invasive Versus Open Myomectomy

Study ObjectiveTo compare the prevalence and accrual of 30-day postoperative complications by operative time for open myomectomy (OM) and minimally invasive myomectomy (MIM). DesignRetrospective cohort study SettingHospitals participating in the National Surgical Quality Improvement Program database from January 2015 to December 2021. PatientsFemale patients aged ≥18 years undergoing OM or MIM. InterventionsPatients were categorized into OM and MIM cohorts. Covariates associated with operative time and composite complications were identified using general linear model and chi-square or Fisher's exact test as appropriate. Adjusted spline regression was performed as a test of linearity between operative time and composite complications. Adjusted risk ratios of 30-day postoperative individual, minor, major, and composite complications by 60-minute operative time increments were estimated using Poisson regression with robust error variance. Measurements and Main ResultsOf 27 728 patients, 11 071 underwent MIM and 16 657 underwent OM. Mean operative times (SD) were 164.6 (82.0) for MIM and 129.2 (67.0) for OM. Raw composite complication rates were 5.5% for MIM and 15.8% for OM. Adjusted spline regression demonstrated linearity between operative time and relative risk of composite postoperative complications for both MIM and OM. MIM had higher adjusted relative risk (aRR, 95% CI) compared to OM of blood transfusion (1.55, 1.45–1.64 versus 1.29, 1.25–1.34), overall minor complications (1.13, 1.03–1.23 versus 1.01, 0.92–1.10), and overall major complications (1.43, 1.35–1.51 versus 1.27, 1.12–1.32). Operative time had greater impact on risk of composite complications for MIM than OM, reaching aRR 2.0 at 296 minutes versus 461 minutes for OM. ConclusionOM has a higher overall rate of composite, minor, and major complications compared to MIM. While operative time is independently and linearly associated with postoperative complications with myomectomy regardless of approach, optimizing surgical efficiency for MIM may be more critical than for OM.