Generalized Least-squares Method Research Articles

Revisiting design wind speed and cyclonic factor for east coast of India

It is very much challenging to estimate the accurate design wind speed for engineering structures of India, those experiencing extreme wind events such as cyclones. However, there are different categories of structures based on their importance level and a trade-off always exists between structural safety and economy. Indian code IS15498 specifies uniform cyclonic factors. However, a wide variation has been found while considering different return periods and threshold wind speeds above which cyclones have been modelled. Accordingly, a cyclonic factor (k4) has been calculated using three distinct approaches. These include the Gumbel distribution with the Generalized Least Squares Method (GLSM), the Generalized Extreme Value distribution with the Least Squares Method, and the Peaks over Threshold approach employing the De Haan method. These methods were applied to the dataset of peak values from all cyclone events to determine the design wind speed and, subsequently, the cyclonic factor. The analysis shows that Gumbel distribution with GLSM provides the best results with 1.03 value of cyclonic factor. Reverse Weibull can also be used to fit the extreme wind speed data. The outcomes from all approaches consistently underscore the necessity of a cyclonic factor for the east coast of India.

Estimating genomic relationships of metafounders across and within breeds using maximum likelihood, pseudo-expectation–maximization maximum likelihood and increase of relationships

BackgroundThe theory of “metafounders” proposes a unified framework for relationships across base populations within breeds (e.g. unknown parent groups), and base populations across breeds (crosses) together with a sensible compatibility with genomic relationships. Considering metafounders might be advantageous in pedigree best linear unbiased prediction (BLUP) or single-step genomic BLUP. Existing methods to estimate relationships across metafounders Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} are not well adapted to highly unbalanced data, genotyped individuals far from base populations, or many unknown parent groups (within breed per year of birth).MethodsWe derive likelihood methods to estimate Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document}. For a single metafounder, summary statistics of pedigree and genomic relationships allow deriving a cubic equation with the real root being the maximum likelihood (ML) estimate of Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document}. This equation is tested with Lacaune sheep data. For several metafounders, we split the first derivative of the complete likelihood in a term related to Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document}, and a second term related to Mendelian sampling variances. Approximating the first derivative by its first term results in a pseudo-EM algorithm that iteratively updates the estimate of Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} by the corresponding block of the H-matrix. The method extends to complex situations with groups defined by year of birth, modelling the increase of Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} using estimates of the rate of increase of inbreeding (ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document}), resulting in an expanded Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} and in a pseudo-EM+ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document} algorithm. We compare these methods with the generalized least squares (GLS) method using simulated data: complex crosses of two breeds in equal or unsymmetrical proportions; and in two breeds, with 10 groups per year of birth within breed. We simulate genotyping in all generations or in the last ones.ResultsFor a single metafounder, the ML estimates of the Lacaune data corresponded to the maximum. For simulated data, when genotypes were spread across all generations, both GLS and pseudo-EM(+ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document}) methods were accurate. With genotypes only available in the most recent generations, the GLS method was biased, whereas the pseudo-EM(+ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document}) approach yielded more accurate and unbiased estimates.ConclusionsWe derived ML, pseudo-EM and pseudo-EM+ΔF\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta F$$\\end{document} methods to estimate Γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\varvec{\\Gamma}}$$\\end{document} in many realistic settings. Estimates are accurate in real and simulated data and have a low computational cost.

Generalized least squares beamforming of infrasound data

Generalized least squares (GLS) beamforming is a method for determining the direction of arrival and trace velocity of transient infrasound signals that may be otherwise obscured by persistent, correlated background noise, such as microbaroms. This method complements the adaptive F-detector by using an estimate of the noise background to form a generalized power ratio, which is used to estimate plane wave parameters (trace velocity and back-azimuth). Using a suite of fully synthetic signals, we first investigate the resolving power of the GLS estimator as a function of signal to noise ratio compared with a conventional, non-adaptive estimator. Fstatistic and plane wave parameter estimates will then be compared between GLS beamforming, conventional Bartlett beamforming, Capon beamforming, and the MUSIC algorithm. Recorded infrasonic signals from the Forensics Surface Experiment, where a persistent signal was observed from the south, will then be used to evaluate the GLS method. Initial analyses suggest that GLS beamforming results in a lower F-statistic during noise regions and a higher F-statistic value for transient signals compared to the Bartlett beam. Algorithmically determining an optimal window to characterize the background noise presents a significant challenge and different approaches will be discussed.

The Colouration in Dicrurus caerulescens (White-bellied Drongo) Follows the Precipitation and Temperature of the Environment

The variation in climatic conditions over space and time is considered a major driving force in speciation. Gloger's rule is one such rule that broadly explains the variation in the colouration of endotherms (birds and mammals) with the effects of climatic parameters. This predicts that endothermic animals tend to have darker colouration in warm and rainy climates. Here we have tried to explain the variation in the belly white colour in Dicrurus caerulescens (White-bellied Drongo), an overall black coloured bird with a variable white belly. The dark bellied birds are considered the subspecies D. c., and the pale bellied birds are considered D. c.insularis (in dryzone of Sri Lanka). A total number of 112 individuals of adult White-bellied Drongo, including field (n=23) and museum samples (n=36) from Sri Lanka (Total 59), and museum samples from India (n=45) and Nepal (n=8) which were deposited at the National Museum of Sri Lanka, Field Museum of Natural History, USA and the Natural History Museum, UK were taken into consideration. The length of the white belly was measured using a dial calliper adopting a standardized method to minimize inconsistencies. To understand how the extent of white belly varied with respect to precipitation and temperature, we adopted generalized least squares (GLS) methods accounting for spatial autocorrelation between data points. The climatic variables were extracted from the world climatic data through QGIS software. The GLS methods were carried out using the ―nlme‖ package in the R Software for different orders of Autoregressive-moving average (ARMA) for the squared value of white belly length. To find the best fitting model, we used the Akaike information criterion (AIC) using the package ―AICcmodavg‖ in the R. From this, we found a negative significant effect of the interaction between the temperature and precipitation for the white belly length over the studied area in ARMA (1, 1) correlation (-3.72e-01±-0.022, t= -2.23, p<0.05). The white belly is smaller; hence the birds are darker where there is higher temperature and precipitation. The reverse (pale belly) is true when lower temperatures and/or humidity. Therefore, this follows the Gloger's rule of becoming darker in colouration with the high rainfall and temperature in D. caerulescens. 
 Keywords: Dicrurus caerulescens, Colouration, Gloger's rule, Temperature, Precipitation

Social and economic determinants of electoral behavior in Turkey

PurposeIn this study the authors aim to comprehensively investigate the determinants of voting behavior in Turkey, with a specific focus on the dynamics of the center-periphery debate. Mainly, the authors focus on regional voting patterns during the period that is dominated by the Justice and Development Party (JDP/AKP) in the elections. The authors apply the random effects generalized least squares (GLS) methodology, and analyze electoral data covering four pivotal parliamentary elections (2007, 2011, 2015 and 2018) across all 81 provinces (NUTS III regions). The authors individually examine voting dynamics of the four major parties in parliament: the JDP/AKP, the Republican People's Party (RPP/CHP), the Nationalist Movement Party (NMP/MHP) and the Peoples' Democratic Party (PDP/HDP). The authors contribute to a comprehensive understanding of how socioeconomic cleavages, economic performance, party alignment and social dynamics shape voter preferences in the Turkish context, thereby addressing gaps in the existing literature.Design/methodology/approachThis research employs an ecological study of Turkish NUTS III sub-regions, covering national elections from 2007 to 2018. The authors utilize the random effects GLS method to account for heteroscedasticity and time effects. The inclusion of the June and November 2015 elections enables a comprehensive analysis of the evolving dynamics in Turkish voting behavior. The results remain robust when applying pooled OLS and fixed effect OLS techniques for control.FindingsThe study's findings reveal that economic performance, specifically economic growth, plays a pivotal role in the sustained dominance of the JDP/AKP party. Voters closely associate JDP preference with economic growth, resulting in higher voting shares during periods of economic prosperity. Along with economic growth; share of agriculture in regions' GDP, female illiteracy rate, old population rate, net domestic migration, terrorism and party alignment are also influential factors in the Turkish case. Furthermore, differences among sociocultural groups, and East–West dichotomy seem to be important factors that reveal the impact of social cleavages to understand electoral choice in Turkey.Originality/valueThis study contributes to the existing literature by offering a comprehensive multidimensional analysis of electoral behavior in Turkey, focusing on the JDP/AKP dominance period. The main contribution of this study is its multidimensional perspective on the power bases of all main parties, considering key voter choice theories (cleavages, party alignment and retrospective economic performance voting) that have not been systematically analyzed in prior research. The main research question of this study is to examine which factors affect voting behavior in Turkey and how the dynamics of center-periphery or eastern-western region voting behavior under the JDP hegemony can be explained. The contribution of this study consists not only in its empirical testing of panel data approaches but also in its comprehensive analysis of four major political parties. Building upon existing studies in the literature, this research seeks to extend the understanding of voting dynamics for the four main parties in the parliament — JDP/AKP, RPP/CHP, NMP/MHP and PPDP/HDP — by delving into their dynamics individually, thereby expanding the scope of previous studies. This study aims to make a contribution by not only empirically testing panel data approaches but also conducting a comprehensive analysis of four major political parties. Furthermore, the separate inclusion of the 2015 elections and utilization of a panel data approach enrich the analysis by capturing the evolving dynamics of Turkish voting behavior. The study underscores the significance of socioeconomic factors, economic performance and social cleavages for voters' choices within the context of a dominant party rule.

Efficient single-run implementation of generalized Einstein relation to compute transport coefficients: A binary-based time sampling.

A robust and simple implementation of the generalized Einstein formulation using single equilibrium molecular dynamics simulation is introduced to compute diffusion and shear viscosity. The unique features underlying this framework are as follows: (1) The use of a simple binary-based method to sample time-dependent transport coefficients results in a uniform distribution of data on a logarithmic time scale. Although we sample "on-the-fly," the algorithm is readily applicable for post-processing analysis. Overlapping same-length segments are not sampled as they indicate strong correlations. (2) Transport coefficients are estimated using a power law fitting function, a generalization of the standard linear relation, that accurately describes the long-time plateau. (3) The use of a generalized least squares (GLS) fitting estimator to explicitly consider correlations between fitted data points results in a reliable estimate of the statistical uncertainties in a single run. (4) The covariance matrix for the GLS method is estimated analytically using the Wiener process statistics and computed variances. (5) We provide a Python script to perform the fits and automate the procedure to determine the optimal fitting domain. The framework is applied to two fluids, binary hard sphere and a Lennard-Jones near the triple point, and the validity of the single-run estimates is verified against multiple independent runs. The approach should be applicable to other transport coefficients since the diffusive limit is universal to all of them. Given its rigor and simplicity, this methodology can be readily incorporated into standard molecular dynamics packages using on-the-fly or post-processing analysis.

Extended least squares (ELS) and generalized least squares (GLS) for clutter suppression in hyperspectral images: A theoretical description

Hyperspectral imaging (HSI) has been demonstrated to be useful for estimating spatial distributions of different chemical constituents on surfaces and in imaged scenes, as well as for detecting anomalies and known targets that may be present. In target detection, the objective is to find pixels that contain significant signal attributable to a known “target” spectrum. Unfortunately, measured images are often dominated by “non-target” interference signals with unknown spectra that vary image-to-image. To be effective, target detection algorithms must account for the non-target signal, commonly referred to as “clutter.” The utility of HSI for target detection in spatially complex scenarios can be attributed to two aspects associated with the sensing system: 1) The target can dominate the measured signal in a few individual pixels, although it may have only a minor contribution to the overall signal, and 2) HSI measures many pixels relevant for characterizing clutter that can be used in clutter suppression algorithms to account for interferences.Clutter suppression enables the detection of minor signal sources in HSI, resulting in a sensing system uniquely suited to highly variable environmental scenarios. Popular clutter suppression methods such as extended least squares (ELS) and generalized least squares (GLS) can be described as extensions of the classical least squares (CLS) model. The main advantage of these methods is that they are fast and easy to train on an image-to-image basis providing highly sensitive, image-specific target detection systems. However, the optimization of such models affects the sensitivity and selectivity of the detection. In an effort to assist practitioners interested in clutter suppression, this paper presents a detailed description of the theoretical aspects of ELS and GLS methods used for target detection on hyperspectral images. The concepts are demonstrated with practical examples of the detection of an acrylonitrile butadiene styrene (ABS) particle in sand based on a single near infrared hyperspectral image.

Analysis of a regional metrology organization key comparison: model-based unilateral degrees of equivalence

A statistical testing method is developed to analyze a Regional Metrology Organization (RMO) key comparison (KC) with linking to the corresponding KC conducted by the International Committee of Weights and Measures (CIPM). We establish a statistical model to which a generalized least-squares method is applied to determine a linking invariant, ensuring that the CIPM KC reference value is unchanged by the analysis. The proposed method generally gives different unilateral and bilateral degrees of equivalence (DOEs) from those assessed by other methods. The approach accounts for uncertainty information provided by all participating laboratories and correlation information provided by the linking laboratories. Since decisions are made on the basis of calculated DOEs, we emphasize that it is valuable to have adequate knowledge of the properties of methods used for analyzing the results from an RMO KC.

Factors affecting English scores at national high school exam of sophomores at Thu Dau Mot University: Empirical evidence from GLS method

English, undeniably is now playing an extremely important role in many fields, particularly in cultural promotion and economic development of the country. Although the government has implemented several reforms to enhance the quality of nationwide English teaching and learning, the results, particularly shown through the national high school exams, are far below expectation. Therefore, as an effort to better the students’ score in the exam, this paper aimed at investigating factors, mostly those relating to students, affecting their scores at the national high school exams. To achieve that goal, data will be collected from 250 sophomores of Thu Dau Mot University through questionnaires. These students are chosen to get more reliable results because the latest NHE in 2021 was held amidst the coronavirus outbreak with lots of its unavoidable impacts. The generalized least squares (GLS) method is then used with eight independent variables and the dependent variable is the English score of these students in the National high school exam. Based on the findings, some recommendations are then made.

Modeling of Clinical Judgment Using Lasso Regression and Reduction of Assessment Items

In the medical field, accidents involving the fall of inpatients around beds have become an increasing problem. To prevent such accidents, nurses record the condition of inpatients using a check sheet referred to as the Fall Assessment Score Sheet (Fall AS). These sheets are utilized for each inpatient and provide useful information for daily clinical judgment. However, compiling these sheets adds to the excessive workload of nurses, as the Fall AS contains numerous items to be checked and assessments frequently necessitate revision. We sought to model the clinical judgment of nurses using Lasso regression, with the aim of reducing the number items of the Fall AS. Three nurses were assigned a questionnaire that included 200 patterns of Fall AS and seven patterns of patient posture, and were requested to fill in the fall risk (0% to 100%) for all 1400 pattern combinations. The clinical judgment model was constructed based Lasso regression, adopting each fall risk and Fall AS as objective and explanatory variables, respectively. The proposed model succeeded in reducing the number of Fall AS items from 50 to 25. Moreover, the mean error of estimated values for the clinical judgment increased by only 3.44% compared with the general least-squares method.

Active Shape Control of Satellite Antenna Reflectors with Limited Power Supplies

This paper proposes an active shape control method for an antenna control system under a limited number of power supplies with a reduction in weight and cost. An analytical model of a grid reflector with embedded piezoelectric actuators was first established, from which an influence coefficient matrix was derived for controller design. A shape controller was designed by synergistically optimizing the actuator group and power supply voltage. The optimization objectives include the root-mean-square (RMS) error, control energy consumption, and a multiobjective combination of both. The design variables include the actuator group and power supply voltage. A dynamic actuator group design with a detached power supply is proposed to improve the control performance, which also significantly increases the design domain of this optimization problem. A hybrid optimization method is proposed based on the general genetic algorithm and least-squares method to reduce the difficulty and complexity of this optimization problem with a large-dimensional design domain. Numerical simulations were carried out to illustrate the effect of the proposed hybrid optimization control method. The results indicate that the RMS error can be reduced by more than 90%, even if only two power supplies are used. The introduction of a group design with a detached power supply can clearly improve the control performance, especially by reducing the energy consumption. By employing multiobjective optimization with a uniform consideration of the RMS error and control energy consumption, a series of Pareto-optimal results can be obtained, and these results can provide meaningful references for engineering designers.

Cognitive and Emotional Motivation to Explain Infection-Prevention Behaviors with Social Support as a Mediator During the COVID-19 Pandemic: A Nationwide Cross-Sectional Study in Korea.

To predict the performance of infection-prevention behaviors among adults in Korea based on the health belief model, using social support as a mediator. A nationwide cross-sectional survey of 700 participants from the local community was conducted using both online and offline methods from 8 metropolitan cities and 9 provinces in Korea from November 2021 to March 2022. The questionnaire was composed of 4 sections: demographic information, motivational factors for behavior change, social support, and infection-prevention behaviors. Data were analyzed using structural equation modeling with the AMOS program. The general least-squares method was applied to assess the fit of the model and the bootstrapping method was tested for indirect effect and the total effect. Motivation factors that directly affected infection-prevention behaviors were self-efficacy (γ=0.58, p<0.001), perceived barriers (γ=-.08, p=0.004), perceived benefits (γ=0.10, p=0.002), perceived threats (γ=0.08, p=0.009), and social support (γ=0.13, p<0.001), after controlling for related demographic variables. Cognitive and emotional motivation factors together explained 59% of the variance in infection-prevention behaviors. Social support exerted significant mediating effects between each cognitive and emotional motivation variable and infection-prevention behaviors, along with a significant direct effect on infection-prevention behaviors (γ=0.12, p<0.001). The engagement of prevention behaviors among community-dwelling adults was influenced by their self-efficacy, perceived barriers, perceived benefits, and perceived threats with social support as a mediator. Prevention policy approaches could include providing specific information to improve self-efficacy and build awareness of the severity of the disease while establishing a supportive social environment for promoting health behaviors during the COVID-19 pandemic.

Why trust nuclear data evaluations with Bayesian networks

Bayesian networks were recently suggested as a framework for nuclear data evaluation. Their theory was to some extent described in a recent preprint and some example evaluations were presented. However, due to their newness in the context of nuclear data evaluation and consequently the lack of experience with them within the community makes it dicult to develop trust in the underlying methodology and consequently also the results produced by it. In this contribution, we aim to make a case why evaluators can trust this methodology in principle but will also elaborate on the fact that Bayesian networks are not a silver bullet for evaluation work. On the contrary, evaluators must assess and quantify essential assumptions about nuclear models and experiments with the same dilligence that is already necessary for the application of the wellestablished Generalized Least Squares (GLS) method. We also explain that the increased ease and flexibility to introduce assumptions regarding nuclear models, experiments and their relationships can help an evaluator to rigorously account for assumptions that are very often neglected in evaluations with the GLS method, such as the non-negativity of cross sections, relative experimental normalization uncertainties and the non-linearity in ratios of cross sections. We believe that adopting the Bayesian network paradigm can help both humans to produce evaluations with clearly traceable assumptions and machines to deal with nuclear data more eciently in terms of execution speed and storage size requirements.

Noise exposure and risk of myocardial infarction incidence and mortality: a dose-response meta-analysis.

The strength and shape of the dose-response relationship between different types of noise and myocardial infarction (MI) remain unclear. Therefore, we aimed to summarize the evidence of the association between various types of noise and MI incidence and mortality through a dose-response meta-analysis. We performed a systematic search of the PubMed, Embase, and Web of Science databases up to December 19, 2021. The generalized least-squares method and restricted cubic splines were used to assess the potential linear and nonlinear dose-response relationships between noise exposure and the risk of MI events. Twenty observational studies with 34 reports met the eligibility criteria. In the linear models, the pooled relative risk and corresponding 95% confidence interval (CI) for MI incidence was 1.04 (95% CI: 1.02 - 1.05), and the MI mortality was 1.02 (95% CI: 1.02 - 1.03) for each 10dB(A) increase in noise exposure. In addition, we observed an approximately J-shaped dose-response relationship between noise and MI mortality (Pnonlinearity = 0.0037), whereas the threshold for the statistical impact of noise on MI mortality may be 42dB(A). Our findings support the notion that various types of noise exposure have a positive dose-response relationship with the risk of MI incidence and mortality.

Efficient Identification of Multiple Parameter Errors in Power Grids by Mixed-Effects Models and Generalized Least Squares

Accurate knowledge of static parameters forms the basis for nearly all applications in the energy management system. This paper proposes an efficient method for the simultaneous identification and correction of multiple network parameter errors based on a linear mixed-effects (LME) model and the generalized least squares (GLS) method. An LME model for parameter error identification is formulated using the residual equations of multiple-snapshot state estimation with equality constraints. The parameter errors are considered as the fixed effects and the measurement errors are considered as the random effects. Then, using the measurement error variances estimated by the LME model, the GLS method is used to estimate the parameter errors along with a hypothesis testing to infer whether each parameter error is zero. The semi-supervised adversarial autoencoder is used for bad data detection in the presence of erroneous parameters and limited labels such that only measurement snapshots that are free of any bad data are used. The proposed methodology is efficient in that the LME model is only used to estimate the variances of the measurement errors using a small number of measurement snapshots, therefore the huge computation burden needed to solve a large-scale LME model is avoided. In addition, the GLS only involves inversion of low-dimension matrices, which is very efficient even a large number of measurement snapshots are used. Thorough tests of the proposed methodology on a large number of scenarios are provided to show the effectiveness of the proposed methodology with promising results.

Recursive and Non-Recursive Generalized Least-Squares Methods for Estimation of Time Series Models with Exogenous Variables

We present in this paper two generalized least-squares (GLS) methods for estimating regression coefficients of time series models with exogenous variables. The non-recursive GLS method is a generalization of the GLS method suggested by Cochrane and Orcutt (1949). The proposed GLS method consists of a sequence of four linear regressions. A first regression is fitted and provides residuals. These residuals are modeled as an autoregressive process and are used in a second regression (or autoregression) for obtaining estimators of autoregressive coefficients. These estimators are used to generate transformed endogenous and exogenous variables. A third regression makes use of the lagged values of these transformed variables to estimate the regression coefficients. The estimators of the regression coefficients are used to determine the true residuals which are modeled as an ARMA process which is finally used for obtaining the estimators of autoregressive and moving average parameters. The second GLS method is a recursive version of the first GLS method where the estimators are updated at each time point on receipt of the additional observations. The Simulation results based on different model structures with varying numbers of observations are used to compare the performance of our methods with that of exact maximum likelihood (EML) estimates.

Exploring Structural Relationships in Attracting and Retaining International Students in STEM for Sustainable Development of Higher Education

Attracting and retaining international students has been widely discussed in higher education settings. Increasing the number of international students has become an indispensable strategy for national and global competition. This study focuses on effective strategies and international students’ issues regarding satisfaction in the most popular STEM (science, technology, engineering, and mathematics) programs. We designed a structural equation modeling (SEM) method to determine the effect of institutional mediation between push factors and satisfaction factors for the development of better strategies by which to attract and retain international students. This study employed a self-designed questionnaire to collect data: 485 degree-seeking international students in STEM programs were invited and successfully participated in this study during spring 2021 in Taiwan. IBM SPSS 26 and AMOS 26 (Analysis of Moment Structure) were used to carry out the data analysis. We employed reliability, factor, and SEM analyses. This study assumed that the impact of push factors could be modified by institutional situations and result in international students’ satisfaction with their learning and environment and regarding migration policy. The results revealed that the predictors, mediation, and criteria were significant at the 0.05 or 0.01 levels. The findings suggest that push factors impact international students’ satisfaction when using institutional leadership and strategy. The results of the bootstrap with a generalized least-squares method showed that the SEM model fit in 2000 bootstrap samples. The effect of institutional mediation can provide useful information for STEM programs to boost their future recruitment and retention strategies. This study provides an innovative approach to the detection of issues among international students in specific programs. The design of the study can be extended to similar higher education settings. These findings can enrich our knowledge regarding attracting and retaining global students in higher education.

Disclosure, Shariah governance and financial performance in Islamic banks

PurposeThis study aims at investigating the impact of the disclosure and the Shariah governance on the financial performance in MENASA (Middle East, North Africa and Southeast Asia) Islamic banks.Design/methodology/approachWe use the Generalized Least Squares (GLS) regression models to check the interdependence relationship between the disclosure, the Shariah governance and the financial performance of 47 Islamic banks (IBs) from ten countries operating in MENASA region. The sample period is from 2012 to 2019. In these regressions models, Return on Assets (ROA) and Return on Equity (ROE) are the dependent variables. The disclosure and the Shariah governance indicators are the independent factors. To measure the Shariah governance, we use the three sub-indices, which are the Board of Directors (BOD), the Audit Committee (AC) and the Shariah Supervisory Board (SSB). Size, Leverage and Age of the bank are used as control variables. We also used The Generalized Method of Moments (GMM) and the three-stage least squares (3SLS) estimations for robustness check.FindingsResult shows a negative relationship between the disclosure and the two performance measures in IBs. Furthermore, as far as the governance indicators are concerned, we found that the BOD and AC, as well as the BOD and SSB, have a positive and significant impact on the ROA and ROE, respectively. This reveals that good governance had a significant association with higher performance in MENASA IBs.Originality/valueThe paper considers both IBs that adopt mandatory as well as voluntary AAOIFI standards and the GLS method to investigate the impact of the AAOIFI disclosure and the Shariah governance on ROA and ROE. Also, it uses the GMM and the 3SLS estimations for robustness check. It is relevant for researchers, policymakers and stakeholders concerned with IBs' performance.

Checking for model consistency in optimal fingerprinting: a comment

Allen and Tett (1999, herein AT99) introduced a Generalized Least Squares (GLS) regression methodology for decomposing patterns of climate change for attribution purposes and proposed the “Residual Consistency Test” (RCT) to check the GLS specification. Their methodology has been widely used and highly influential ever since, in part because subsequent authors have relied upon their claim that their GLS model satisfies the conditions of the Gauss-Markov (GM) Theorem, thereby yielding unbiased and efficient estimators. But AT99 stated the GM Theorem incorrectly, omitting a critical condition altogether, their GLS method cannot satisfy the GM conditions, and their variance estimator is inconsistent by construction. Additionally, they did not formally state the null hypothesis of the RCT nor identify which of the GM conditions it tests, nor did they prove its distribution and critical values, rendering it uninformative as a specification test. The continuing influence of AT99 two decades later means these issues should be corrected. I identify 6 conditions needing to be shown for the AT99 method to be valid.

Bank credit and transmission mechanisms of monetary policy in Uganda

Abstract. This paper examines the effect of monetary policy on bank credit in Uganda during the January 2008 to December 2017 period. By using macro level monthly data, it tests for the existence of monetary policy transmission channels, in particular the presence of bank credit channel in the economy of Uganda. This is done by showing that bank credit growth in Uganda is affected by monetary policy shocks. Before conducting data analysis, tight bank credit models were built with the view of making the analysis mimic the actual behavior of bank credit and the monetary policy transmission mechanism. Data used in the empirical analysis are from Bank of Uganda. Empirical analysis is conducted by using the generalized least squares (GLS) technique. The advantage with the GLS method is that it is generally more efficient because it eliminates both serial correlation and variance values that are not constant. The empirical results establish presence of the bank credit channel of the monetary policy transmission mechanism in Uganda. Secondly, the empirical tests conducted establish that the relationship between reserves and loans typically operates in the reverse way to that described in some economics textbooks. Similarly, the relationship between bank deposits and bank deposits is found in principle to operate only during the current month in the reverse way to that described in some economics textbooks. Thirdly, empirical tests conducted indicate that 1 percent increase in money supply (M2) is responsible for causing 2.2 percent monthly increase in bank deposits in Uganda during the sample period, ceteris paribus. Keywords. Financialization, Political economy, Financial regulation. JEL. C01, C10, G18, P16, P34.