Assumption Of Normal Distribution Research Articles

PSYCHOLOGICAL PROFILING OF NEWLY RECRUITED FEMALE POLICE CONSTABLES IN RELATION WITH SOCIO-DEMOGRAPHIC PARAMETERS: A PRELIMINARY STUDY

Purpose of Study The purpose of the study was to create the Psychological Profiling (PP) of 100 Newly Recruited Female Police Constables (NRFPC) and determine the association among selected psychological parameters with their sociodemographic parameters; i.e., age and highest educational qualifications. Material and Methods A total of 100 female recruits participated in the study. The PP was formulated by the observed frequencies for each psychological parameter. The dataset had violated the assumption of normal distribution (Shapiro Wilk p<0.05). Hence, the non-parametric test; Chi-square (χ2) test of independence was performed to determine the association among the selected psychological parameters and sociodemographic parameters (different age groups; Age Group Pre-Median (AGPRM) 18 – 23 years and Age Group Post-Median (AGPOM) 24 – 34 years, and different highest education qualifications; Under-Graduates (UG) and Post-Graduates (PG)). Effect sizes were calculated using Phi and Cramer’s V test. The Post hoc analysis, Mann-Whitney U test was applied to compare these groups and its effect sizes was determined by eta square (η2). Results The PP highlighted newly recruited female police constable, who fall in the category of severe aggression level (33 NRFPC), severe anxiety issues (12 NRFPC), low emotional intelligence (4 NRFPC), higher perceive stress (11 NRFPC), low self-esteem (8 NRFPC) and extremely dissatisfied with life (3 NRFPC). Although, the chi-square test of independence was failed to reject the null hypothesis (H01), hence no association among psychological parameters and sociodemographic parameters was established. The post hoc test rejects the null hypothesis (H02) and self-esteem was found significant (p = 0.03) when compared in UG and PG group, UG poses higher self-esteem with (1%) variability in mean rank. Conclusion the PP was a potential method to identify the personnel on the edge of the selected psychological parameters and self-esteem may vary in different levels of highest education groups.

The effect of the 12-week judo physical activity program on the self-esteem of secondary school students during the COVID-19 period

Background and Study Aim. During the COVID-19 period, participation in physical activity decreased, and at the same time, many psychological problems occurred in children and adolescents. Therefore, this study examines the impact of secondary school students’ participation in regular exercise on their self-esteem.
 Material and Methods. The research was carried out with an experimental method and sequential design. The study, carried out in 2 sessions, started with the first measurement in November 2021 and ended with the last measurement in January 2022. 46 volunteer students forming the study sample were divided into experimental and control groups. Students in both groups completed the ‘Personal Information Form’ and the ‘Coopersmith Self-Esteem Inventory’ in the first week and the end of the 12th week. Statistical Package for Social Sciences (SPSS) 17.0 was used for data analysis. First, a descriptive statistical test was conducted to indicate the socio-demographic characteristics of the participants. Then, normality tests (Skewness and Kurtosis) were applied to determine whether the data showed normal distribution, and it was determined that the data showed normal distribution. Finally, the independent sample t-test and Paired Samples t-test were applied because the data met the assumption of normal distribution. The significance level was determined as p<0.05.
 Results. A statistically significant difference was found between the self-esteem values of the experimental group before the physical activity program and after the exercise sessions. There was no significant difference in the self-esteem levels of the control group pre-test and post-test.
 Conclusions. In conclusion, this study reveals that participation in 12 weeks of regular exercise significantly increases middle school students’ self-esteem.

Dengue in Brazil: Regression and probabilistic modeling

The work aimed to adjust Poisson models and their generalizations (Quasi-Poisson and Negative Binomial) to the behavior of the number of dengue cases grouped in Recife-PE. Weekly epidemiological records of dengue were used, from 2009 to 2018, and they were made available through the Citizen Information Service. Initially, Poisson models and their generalizations were applied, followed by a diagnostic analysis of the residuals, using influence measures, in order to analyze how much a specific observation affects some property of the studied model. The adjustment results indicated that the Negative Binomial model showed a satisfactory adjustment, compared to other models for description of the data, minimizing dispersions parameters more accurately. Regarding the analysis of the residuals, it can be stated that the assumptions of normal distribution and homogeneity of the residuals in the Generalized Linear Models (GLM) were provided. In addition, the existence of atypical and influential values was verified, which should be examined carefully. Therefore, it can be concluded that diagnosis is an important tool for researchers, in which they present basic premises for the reliability of the GLM, data set, and goodness of fit. Finally, we point out that more scientific studies are needed, in this context, to have more consistency in this application.

Efficacy of GARCH-EVT Model in Intraday Risk Management: Evidence from Severely Pandemic-affected Countries

Countries around the world have experienced severe-to-moderate economic restrictions during the first two waves of COVID-19 pandemic. The present article captures the time frame of this unprecedented turmoil to test the efficacy of the conditional extreme value theory (EVT) model to forecast value at risk (VaR) and expected shortfall (ES). The article considers the eight most-affected countries and applies the generalized autoregressive conditional heteroskedasticity (GARCH) process in appropriate GARCH-EVT models assuming Student’s t distribution to accommodate the fat-tailed behaviour of financial return series. The study confirms that our model is substantially superior in forecasting intraday VaR and ES in comparison to other conditional EVT models with a classical assumption of normal distribution as well as an unconditional EVT model. Contrary to more commonly used 5-minute-frequency data in intraday risk management, we use hourly data, which is much less researched in the context of stock market volatility forecasting. Moreover, our study adds to the reliability of conditional EVT models because of its accuracy in predicting intraday risk during extreme economic uncertainty in severely pandemic-hit countries.

Transfer learning in high-dimensional semiparametric graphical models with application to brain connectivity analysis.

Transfer learning has drawn growing attention with the target of improving statistical efficiency of one study (dataset) by digging up information from similar and related auxiliary studies (datasets). In this article, we consider transfer learning problem in estimating undirected semiparametric graphical model. We propose an algorithm called Trans-Copula-CLIME for estimating an undirected graphical model while uncovering information from similar auxiliary studies, characterizing the similarity between the target graph and each auxiliary graph by the sparsity of a divergence matrix. The proposed method relaxes the restrictive Gaussian distribution assumption, which deviates from reality for the fMRI dataset related to attention deficit hyperactivity disorder (ADHD) considered here. Nonparametric rank-based correlation coefficient estimators are utilized in the Trans-Copula-CLIME procedure to achieve robustness against normality. We establish the convergence rate of the Trans-Copula-CLIME estimator under some mild conditions, which demonstrates that if the similarity between the auxiliary studies and the target study is sufficiently high and the number of informative auxiliary samples is sufficiently large, the Trans-Copula-CLIME estimator shows great advantage over the existing non-transfer-learning ones. Simulation studies also show that Trans-Copula-CLIME estimator has better performance especially when data are not from Gaussian distribution. Finally, the proposed method is applied to infer functional brain connectivity pattern for ADHD patients in the target Beijing site by leveraging the fMRI datasets from some other sites.

Nonparametric discriminant analysis with network structures in predictor

ABSTRACT Multiclassification, known as classification for multi-label responses, has been an important problem in supervised learning and has attracted our attention. Discriminant analysis (DA) is a popular method to deal with multiclassification. With the increasing availability of complex data, it becomes more challenging to analyse them. One of the important features in complex data is the network structure, which is ubiquitous in high-dimensional data because of strong or weak correlations among variables. In addition, in the framework of DA, an assumption of normal distributions is imposed on the predictors, but it is usually invalid in applications. To relax the normality assumption, we propose a nonparametric discriminant function to address multiclassification. In addition, to incorporate the network structure and improve the accuracy of classification, we develop three different network-based surrogate predictors to replace conventional predictors. The key features of the proposed method include the incorporation of network structures in predictors and allowance of predictors to follow exponential family distributions. Finally, numerical studies, including simulation and real data analysis, are conducted to assess the performance of the proposed method.

Shoe Bending Stiffness Influence on Lower Extremity Energetics in Consecutive Jump Take-Off.

Objective This study examined the influence of shoe bending stiffness on lower extremity energetics in the take-off phase of consecutive jump. Methods Fifteen basketball and volleyball players wearing control shoes and stiff shoes performed consecutive jumps. Joint angle, angular velocity, moments, power, jump height, take-off velocity, take-off time, and peak vertical ground reaction force data were simultaneously captured by motion capture system and force platform. Paired t-tests were performed on data for the two shoe conditions that fit the normal distribution assumptions, otherwise Wilcoxon signed-rank tests. Results There are significant differences (P < 0.05) in take-off velocity and take-off time between stiff and control shoe conditions; the stiff shoes had faster take-off velocity and shorter take-off time than control shoes. There was no significant difference between two conditions in jump height (P = 0.512) and peak vertical ground reaction force (P = 0.589). The stiff shoes had significantly lower MTP dorsiflexion angle and greater joint work than the control shoes (P < 0.05). The MTP range of motion and maximum angular velocity in stiff shoe condition were significantly lower than those in control shoe condition (P < 0.01). However, there are no significant differences between two conditions in kinetics and kinematics of the ankle, knee, and hip joint. Conclusions The findings suggest that wearing stiff shoes can reduce the effect of participation of the MTP joint at work and optimize the energy structure of lower-limb movement during consecutive jumps.

Covariance Estimation for Matrix-valued Data

Covariance estimation for matrix-valued data has received an increasing interest in applications. Unlike previous works that rely heavily on matrix normal distribution assumption and the requirement of fixed matrix size, we propose a class of distribution-free regularized covariance estimation methods for high-dimensional matrix data under a separability condition and a bandable covariance structure. Under these conditions, the original covariance matrix is decomposed into a Kronecker product of two bandable small covariance matrices representing the variability over row and column directions. We formulate a unified framework for estimating bandable covariance, and introduce an efficient algorithm based on rank one unconstrained Kronecker product approximation. The convergence rates of the proposed estimators are established, and the derived minimax lower bound shows our proposed estimator is rate-optimal under certain divergence regimes of matrix size. We further introduce a class of robust covariance estimators and provide theoretical guarantees to deal with heavy-tailed data. We demonstrate the superior finite-sample performance of our methods using simulations and real applications from a gridded temperature anomalies dataset and an S&P 500 stock data analysis. Supplementary materials for this article are available online.

When all we have is not enough: a search for the optimal method of quantifying inflation expectations

Although inflation expectations are pivotal variables for central banks, they are not directly observable. Therefore, central banks use qualitative survey results to proxy consumer expectations, and their quantification in this manner is often criticized. In this study, we investigate and identify an optimal quantification procedure for survey results based on a set of regression and probabilistic models. Specifically, we seek to identify the method that returns time series that are most highly correlated with an unbiased representative of survey-based expectation: balance statistics. We place additional constraints on this criterion to identify the procedure that returns expectations that are most closely related to consumer intentions (directional co-movements and forecast accuracy). Our sample covers the European Union member states over the period of January 2002 to June 2019. We also test a post-crisis subsample. Our results suggest that different procedures may be optimal for different economies, in line with previous findings on cross-country divergences of expectations formation. However, we find that the most applied assumption of normal distribution does not prove to be the best one. Our recommendation is to apply probabilistic procedures rather than regression methods.

Bayesian inference of spatially correlated random parameters for on-farm experiment

Accounting for spatial variability is crucial while estimating treatment effects in large on-farm trials. It allows to determine the optimal treatment for every part of a paddock, resulting in a management strategy that improves sustainability and profitability of the farm. We specify a model with spatially correlated random parameters to account for the spatial variability in large on-farm trials. A Bayesian framework has been adopted to estimate the posterior distribution of these parameters. By accounting for spatial variability, this framework allows the estimation of spatially-varying treatment effects in large on-farm trials. Several approaches have been proposed in the past for assessing spatial variability. However, these approaches lack an adequate discussion of the potential problem of model misspecification. Often the Gaussian distribution is assumed for the response variable, and this assumption is rarely investigated. Using Bayesian post sampling tools, we show how to diagnose the problem of model misspecification. To illustrate the applicability of our proposed method, we analysed a real on-farm strip trial from Las Rosas, Argentina, with the main aim of obtaining a spatial map of locally-varying optimal nitrogen rates for the entire paddock. The analysis of these data revealed that the assumption of Gaussian distribution for the response variable is unsatisfactory; the Student-t distribution provides a more robust inference. We finish the paper by discussing the difference between the proposed Bayesian approach and geographically weighted regression, and comparing the results of these two approaches.

Bridge deformation prediction based on SHM data using improved VMD and conditional KDE

Deformation is a paramount index of bridge health monitoring. Accurate prediction of bridge deformation is of great significance to evaluate bridge performance. However, owing to the complex mechanical mechanism of bridge performance evolution, it is arduous to obtain a satisfactory forecasting result by simple data-driven methods. To this end, this paper proposes an innovative data-driven method based on an improved variational mode decomposition (IVMD) and conditional kernel density estimation (CKDE). Specifically, the raw deformation data are firstly pretreated by IVMD. This newly developed technique could not only adaptively optimize the decomposition level number through Hilbert transform and empirical mode decomposition, but also hinder the disturbance of irrelevant components by Kullback-Leibler divergence and Gram-Schmidt orthogonal. Then, auto-regression integrated moving average model is established to excavate the linear feature hidden in the data. Finally, CKDE in conjunction with Gaussian distribution assumption is designed to describe the above modeling errors, by which both point and interval predictions are generated. Obviously, this method avoids exploring the complex internal mechanism of structural behavior evolution. Three case studies based on structural health monitoring data are used to systematically evaluate its effectiveness. The results manifest that this method possesses higher reliability in comparison with the other concerned models, and could lay a foundation for early warning purpose.

A comparative assessment on gene expression classification methods of RNA-seq data generated using next-generation sequencing (NGS).

Next-generation sequencing or massively parallel sequencing have revolutionized genomic research. RNA sequencing (RNA-Seq) can profile the gene-expression used for molecular diagnosis, disease classification and providing potential markers of diseases. For classification of gene expressions, several methods that have been proposed are based on microarray data which is a continuous scale or require a normal distribution assumption. As the RNA-Seq data do not meet those requirements, these methods cannot be applied directly. In this study, we compare several classifiers including Logistic Regression, Support Vector Machine, Classification and Regression Trees and Random Forest. A simulation study with different parameters such as over dispersion, differential expression rate is conducted and the results are compared with two mRNA experimental datasets. To measure predictive accuracy six performance indicators are used: Percentage Correctly Classified, Area Under Receiver Operating Characteristic (ROC) Curve, Kolmogorov Smirnov Statistics, Partial Gini Index, H-measure and Brier Score. The result shows that Random Forest outperforms the other classification algorithms.

Revisiting the building energy consumption in China: Insights from a large-scale national survey

The increasing building energy consumption in China has been acknowledged as a key concern in future climate mitigation and sustainable development. Though reliable accounting of energy consumption is the prerequisite, estimates of China's building energy consumption remain subject to large discrepancies. One of the primary causes of these disparities is an inaccurate estimation of building energy intensities due to flaws in estimation methods and data. Here, we present a bottom-up approach and use large-scale nationwide building energy survey data (covering 30 provinces, four different types of buildings, and district heating from 2009 to 2016) to correct the existing, widely used assumption of normal distribution for building energy intensity and provide a more accurate estimate of building energy consumption in China. Our improved estimate of China's building energy consumption differs by −10% to 36% from existing mainstream estimates, indicating a significant bias in existing research and applications.

Effect of non-Gaussian turbulence on extreme buffeting response of a high-speed railway sea-crossing bridge

The non-Gaussian property of turbulence has been emphasized in some measured wind data. However, the classic Scanlan buffeting theory with the Gaussian distribution assumption of turbulence may cause errors in predicting the structural extreme response. In this study, a hybrid approach for predicting bridge's extreme buffeting response is proposed and validated, which considers multi-dimensional non-Gaussian random variables. Then the extreme buffeting response of a cable-stayed bridge under the attack of non-Gaussian wind is investigated, where the in-situ measured wind data of Typhoon ‘Bailu’ is adopted. The sensitivity of extreme buffeting response to turbulence skewness and kurtosis is also analyzed. The study results show that the lateral and vertical buffeting displacements are in skewed distributions under the attack of Typhoon ‘Bailu’. Compared to the Gaussian wind assumption, the non-Gaussian typhoon decreases the lateral extreme response, while increases the vertical extreme response. The results from the sensitivity analysis reveal a strong correlation between the non-Gaussian intensity of component u and that of the lateral buffeting response. In addition, the lateral extreme buffeting response increases significantly with the skewness and kurtosis of component u at a high probability level.

Seismic Vulnerability Assessment of Hammer Head and Multicolumn Bridge Pier using Fragility Curve

Beingan earthquake-prone country, Nepal has seen numerous high-magnitude earthquakes in recent years, resulting significant damage to the bridge and its components. Even after an earthquake, bridges should be able to be used for emergency and rescue operations. Fragility curves are an important technique for measuring the seismic vulnerability of bridges. Hence, this research is focused on the use of analytical method for development of fragility curve for generally used type of pier in Nepal which is hammerhead pier and multicolumn pier. Capacity of bridge is determined by non-linear static analysis and Demand parameter is estimated using non-linear time history analysis. The assumption of a log normal distribution is used to characterize fragility functions. The probability of exceedance of Slight, Moderate, Extensive and collapse damage states for hammerhead pier are 98.28%, 59.36%, 12.29%, 5.11% and 80.11%, 15.07%, 1.54%, 0.42% for MCE and DBE and those values for multicolumn pier are 97.79%, 37.91%, 5.49%, 2.01% and 72.35%, 4.22%, 0.33%, 0.08% for MCE and DBE respectively.

Echocardiographic assessment of the left ventricular systolic function in patients with inflammatory bowel disease

Abstract Funding Acknowledgements Type of funding sources: None. Background/Introduction Research data have shown evidence of association between inflammatory bowel disease (IBD) and cardiovascular disease (CVD). Early detection of CVD in IBD patients is crucial for their further management. Purpose In this case–control study, we assessed the left ventricular (LV) systolic function of IBD patients with no evidence of CVD and compared it with healthy volunteers. Methods The study population consisted of consecutive IBD patients with no evidence of CVD or relative medical history who were referred to our laboratory from January 2021 to July 2021 and a group of healthy volunteers. Standard 2-dimensional transthoracic echocardiography was performed in each group and LV systolic function was assessed by LV ejection fraction (LVEF) with modified Simpson’s rule and LV global longitudinal strain (LVGLS). As the normal distribution assumption was not met, the Mann Whitney U test (new procedure) was used to compare the variables within the two groups, while correlations between the 2 continuous variables were examined with the Spearman"s correlation analysis. A p value of &amp;lt;0.05 was considered statistically significant. Results Twenty-four IBD patients (15 men, 9 women, 12 with Crohn’s disease, 12 with ulcerative colitis) of a mean age of 39.63 years [minimum = 23, maximum = 56, standard deviation (SD)=9.56] were enrolled in the study. The control group consisted of 24 healthy volunteers (15 men, 9 women) of a mean age of 37.75 years (minimum = 17, maximum = 58, SD = 12.12). For the IBD group, the mean LVEF was 57.71% and the median was 58% (minimum = 52%, maximum = 63%, SD = 3.56%), while the mean LVGLS was -18.23% and the median was -18.65% (minimum absolute value = 12.40%, maximum absolute value = 22.70%, SD = 2.48%). For the control group, the mean LVEF was 61.21% and the median was 60% (minimum = 55%, maximum = 68%, SD = 3.43%), while the mean LVGLS was -20.08% and the median was -20% (minimum absolute value = 17%, maximum absolute value = 25.2%, SD = 1.96%). The IBD patient group showed significantly lower LVEF measurements (mean rank = 18.98, Mann-Whitney U = 155.500, p = 0.006) and absolute LVGLS values (mean rank = 19.40, Mann-Whitney U = 165.500, p = 0.011) compared to the control group (LVEF mean rank = 30.02, LVGLS absolute value mean rank = 29.60). Correlation analysis revealed that LVEF was positively correlated with LVGLS absolute values in both groups (IBD patients: Spearman"s rs = 0.532, p = 0.008, control group: Spearman"s rs = 0.565, p = 0.004). Conclusion Subclinical LV systolic impairment seems to be evident in IBD patients and echocardiographic assessment may facilitate its early detection. Abstract Figure.

Relationship between Empowering Leadership and Teachers’ Job Performance: Organizational Commitment as Mediator

This study investigates the mediating role of organizational commitment on the relationship between principals' empowering leadership and teachers’ job performance. To this end, it employed a cross-sectional design, one of the quantitative methods. Data were collected through the "Empowering Leadership Behaviors Scale", "Organizational Commitment Scale" and "Job Performance Scale". The sample consisted of 324 teachers working in different cities in Turkey. They were employed through convenience sampling and responded to the scales online. Predictive relationships between variables were revealed using a structural equation model. Since the data did not satisfy the multivariate normal distribution assumption, bootsrapping was used. The findings indicated that empowering leadership is positively associated with teachers’ job performance and organizational commitment. On the other hand, organizational commitment boosts job performance. Lastly, empowering school leadership enhances organizational commitment which in turn teachers’ job performance. The findings were discussed based on the literature and some suggestions were made based on the findings.

Canonical Variate Nonlinear Principal Component Analysis for Monitoring Nonlinear Dynamic Processes

Conventional multivariate statistical process monitoring methods constrained by the assumptions of linear and normal distributions for the measurements, such as principal component analysis and canonical variate analysis, demonstrate significantly higher fault alarm rates and lower fault detection rates in nonlinear dynamic industrial process monitoring. Kernel principal component analysis (KPCA) based on the radial basis function, has already been applied in numerous nonlinear industrial processes. However, an infinite-dimensional nonlinear mapping might be inefficient and redundant. To improve the efficiency of traditional methods, this study proposes the implementation of canonical variate nonlinear principal component analysis for monitoring nonlinear dynamic processes. The training data are first preprocessed by performing canonical variate analysis to reduce the effects of the dynamic characteristics of the data. Then, the state vectors are projected into a high dimension feature space by an explicit second-order polynomial mapping. The first k principal components and the remaining residual vectors are obtained in the feature space via conventional principal component analysis for fault detection. The combined statistic Qc is proposed for monitoring the variations in the linear and nonlinear residual spaces; its upper control limits can be estimated by the kernel density function. In comparison to the results of KPCA and nonlinear dynamic principal component analysis, the proposed method yielded significantly higher fault detection rates and relatively lower fault alarm rates in a simulated nonlinear dynamic process and the benchmark Tennessee Eastman process.

Sequence graph transform (SGT): a feature embedding function for sequence data mining

Sequence feature embedding is a challenging task due to the unstructuredness of sequences—arbitrary strings of arbitrary length. Existing methods are efficient in extracting short-term dependencies but typically suffer from computation issues for the long-term. Sequence Graph Transform (SGT), a feature embedding function, that can extract a varying amount of short- to long-term dependencies without increasing the computation is proposed. SGT’s properties are analytically proved for interpretation under normal and uniform distribution assumptions. SGT features yield significantly superior results in sequence clustering and classification with higher accuracy and lower computation as compared to the existing methods, including the state-of-the-art sequence/string Kernels and LSTM.

Extrusion width critically affects fibre orientation in short fibre reinforced material extrusion additive manufacturing

This study develops new understanding of fibre orientation distribution in material extrusion additive manufacturing of short fibre reinforced polymer composites. Short carbon fibre reinforced polylactic acid was 3D printed at several nozzle temperatures (T n ), printing speeds (V), and extrusion widths set in the GCode (W set ), to identify the effects of these parameters on fibre orientation. Microscopic characterisation of the top surface of 3D printed filaments allowed detailed analysis of fibre orientation including dependencies on extruded filament width and printing parameters. The printing parameters directly affected measured width of extruded filaments and a wider extrusion resulted from increased T n , increased W set , and decreased V. An assumption of normal distribution appropriately represented the planar fibre orientation distribution. A direct relationship was found between fibre orientation and extrusion width: fibres were more highly oriented along the printing direction when the extruded filament was narrower. This is logical because when extruded material spreads laterally to the printing direction - to achieve wider extrusions - the polymer-fibre-composite melt flow is less aligned to the printing direction. A consistent relationship between fibre orientation and extrusion width was found even when printing conditions varied several-fold (4-fold change in V; 2-fold change in W set ; 20 °C change in T n ), highlighting the dominant influence of the extruded-filament geometry. This relationship also existed for considerably different 3D printing hardware and a different polymer (acrylonitrile butadiene styrene). A case study for tensile modulus of three different short-carbon-fibre-reinforced polymers, each with three different extrusion widths, showed how the findings related to fibre orientation can explain variation in mechanical properties for multiple materials. The results of this study allow more in-depth understanding and analysis of processing-structure-property correlations. They also highlight the crucial role of extrusion width and fibre orientation as well as the importance of characterising both direct and indirect effects of printing parameters.