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Structural equation modeling (SEM) is a widespread approach to test substantive hypotheses in psychology and other social sciences. However, most studies involving structural equation models neither report statistical power analysis as a criterion for sample size planning nor evaluate the achieved power of the performed tests. In this tutorial, we provide a step-by-step illustration of how a priori, post hoc, and compromise power analyses can be conducted for a range of different SEM applications. Using illustrative examples and the R package semPower, we demonstrate power analyses for hypotheses regarding overall model fit, global model comparisons, particular individual model parameters, and differences in multigroup contexts (such as in tests of measurement invariance). We encourage researchers to yield reliable-and thus more replicable-results based on thoughtful sample size planning, especially if small or medium-sized effects are expected. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

This article investigates the predictability of dry bulk shipments' physical shipping costs while testing the predictive significance of derivative products. Accordingly, a comprehensive grid search procedure is needed to simulate combinations of model structures subject to a cross-validation process. In this regard, the intelligent model search engine (IMSE) is implemented as the search algorithm. The IMSE algorithm is selected due to its broad model space naturally consisting most of the traditional econometric model structures and advanced features, such as sample size optimization (also detects structural breaks), lag structure optimization (also reflects seasonality), among others. In contrast to the previous studies in the shipping market research, IMSE executes a relaxation of statistical significance criteria to target the out of sample predictive accuracy (instead of the goodness of fit). Sparsification is retained by utilizing a dropout procedure in line with the cross-validation process. The empirical results indicate the fact that predictive accuracy can be achieved or improved with a shorter sample period. More than half of the entire dataset has not been utilized in the final selections for most simulations. One-month and two-month maturity derivative contracts are tested for predictive features, while none of them is selected for the sparse model selections.

Magnetocardiography (MCG) has been extensively investigated and evaluated in various clinical studies for its potential in detecting the early stages of cardiac diseases. In particular, the establishment of a technology that can provide detailed information on the lesion parts of the heart is expected. The estimation result of the current spatial filter method, such as the exact low-resolution brain electromagnetic tomography, contains errors and has insufficient estimation accuracy for clinical applications. The information of sensors located at the ends cannot be utilized, and the amount of information that affects the estimation result is different for each sensor, which causes the derivation of a biased estimated solution. Therefore, in this study, we considered that the amount of information of each sensor can be used equally by setting an analysis area larger than the sensor area and proposed a method focusing on the relationship between the analysis area of signal source estimation and the sensor plane. We simulated the most efficient and optimized analysis area to be used and performed the source signal estimation by eLORETA using the QRS-complex peak data of the actual MCGs used in adjusting the 51ch sensor directly above the xiphoid process. We compared the following three sizes of the analysis area: x = 120 mm, y = 120 mm, z = 100 mm (a); x = 180 mm, y = 180 mm, z = 100 mm (b); x = 240 mm, y = 240 mm, z = 100 mm (c). Thus, because the signals from the heart are originally considered to be a single block, the estimated solution is also expected to be a single block. The solution was estimated as a single block, except case with analysis area (a), which corresponds to the conventional method. In addition, the position and size of the estimated solution are similar to those of the heart obtained from the CT images. The goodness of fit of each analysis area was approximately 0.99 or more, which indicated that there was no significant difference. Thus, we demonstrated that it is possible to visualize the movement of the myocardium by estimating the time-series waveform of the MCG.

Freshwater is the planet’s most important natural resource and is prone to pollution, making it necessary for real-time monitoring. The IoT-enabled Water Quality Monitoring (WQM) system enables real-time monitoring of freshwater resources. The WQM uses physicochemical parameters such as temperature, pH, dissolved oxygen, electrical conductivity, biochemical oxygen demand, nitrate, and total dissolved solids to control the water quality. The advent of IoT has proven its effectiveness in capturing, studying, and continuously transmitting environmental data in real-time. Mineral-rich watersheds experience the exploitation of available resources in and around rivers, leading to urgent real-time monitoring of river water. The operation pollutes the water by mixing different types of toxic waste, namely urban, industrial, and agricultural, making it unusable for human activities. In India, the traditional method of taking samples from the site, bringing them to the laboratory, and performing the analysis of the samples is in practice, it takes a day or two to get results and it does not happen in real-time, causing water-borne diseases among inhabitants of watersheds. This paper attempts to assess the water quality of the Ghataprabha river. Water samples are taken from the river via the WQM system from identified sampling points and subjected to linear regression analysis to estimate the relationships and goodness of fit between the parameters. Once the parameter relationship is known, a one-way ANOVA is applied to the water samples and the water quality is analyzed using the ANOVA hypothesis. Additionally, the river dataset can be used to train the WQM system.

BACKGROUND: International Classification of Functioning, Disability and Health (ICF) has an important role in rehabilitation in terms of assessments. In the field of vestibular rehabilitation, Vestibular Activities of Participation (VAP) measure is the first assessment developed in a perspective of ICF. OBJECTIVE: It was aimed to adapt cross-culturally and analyze psychometric properties of VAP into Turkish Language. METHODS: The VAP was translated into Turkish language by the authors and back translated by a blind native English speaker. The final version was applied to 310 participants. Internal consistency was analyzed with Cronbach’s alpha and test-retest reliability analyzed with Intraclass Correlation coefficient (ICC). Validity of the scale investigated with Exploratory and Confirmatory Factor analysis (EFA and CFA). RESULTS: Cronbach’s alpha reliability coefficient was found 0.94, and the reliability of the subscales ranged from 0.75 to 0.95; ICC was found 0.94. VAP resulted 8-factor structure and explained 88%of the total variance. In CFA (χ2) / df ratio shows good agreement with 2.472 and the goodness of fit indices of TLI (0.814), CFI (0.893) and RMSEA (0.075) showed acceptable fit. CONCLUSIONS: This study showed that VAP-Turkish version is valid and reliable in Turkish speaking populations, and might be used to determine impact of vestibular disorders on activities and participation.

Penyedia jasa asuransi dalam praktiknya menanggung risiko pemegang polis dengan membayarkan klaim yang diajukan oleh pemegang polis. Sebagai gantinya, pemegang polis perlu membayarkan premi kepada penyedia jasa asuransi. Oleh karena itu diperlukan prediksi besar klaim yang akan diajukan sehingga dapat ditentukan juga besar premi yang perlu dibayarkan oleh pemegang polis. Salah satu cara untuk memprediksi besar klaim adalah dengan menggunakan teori kredibilitas. Teori kredibilitas memungkinkan penyedia jasa asuransi untuk menggunakan informasi dari pengalaman klaim seorang individu dengan informasi lainnya berupa manual rate dalam memprediksi besar klaim di masa yang akan datang. Salah satu model yang dikembangkan dalam teori kredibilitas adalah model kredibilitas Buhlmann. Pada model kredibilitas Buhlmann, diasumsikan risiko antara individu saling independen. Namun dalam beberapa kasus asumsi tersebut tidak terpenuhi. Selain itu, model kredibilitas Buhlmann menggunakan fungsi kerugian berupa squared error loss function untuk mendapatkan estimator kredibilitas model. Pada penelitian ini dijelaskan pembentukan estimator kredibilitas model kredibilitas Buhlmann yang memperhitungkan dependensi risiko antara individunya yang dijelaskan oleh suatu parameter risiko bersama dengan menggunakan proyeksi orthogonal dan fungsi kerugian berupa balanced loss function (BLF). Dengan menggunakan data yang memenuhi asumsi model, dapat diperoleh besar presisi dan goodness of fit dari estimasi klaim yang berbeda-beda dengan mengatur bobot pada BLF.

This study investigated the relationship between project performance measures and project management practices of construction projects for the construction industry in Nigeria. Survey research method was adopted using questionnaire as the primary instrument for data collection. The questionnaire was designed to collect data on nineteen project management performance measures and fifty three project management practices on a five point Likert scale. Altogether, two hundred and fifty questionnaires were distributed to contractors, clients and consultants from ten construction companies, out of which a total of two hundred and twenty one valid responses were obtained. The data collected was put into SPSS 25 (statistical package for social sciences) where Crombach’s alpha test was carried out, the average variance extracted and composite reliability tests were calculated for the constructs in Microsoft Excel to determine their appropriateness for structural equation model (SEM). The data was finally imported from SPSS to LISREL (Linear Structural Relationship) where the hypothetical SEM model was tested and validated using the path coefficients and goodness of fit indices. From the results of the study, the highest relationship exist between customer satisfaction and project success, followed by project performance and project success, and stakeholder management and project success. Altogether, 14 out of the 20 hypotheses used for the data analysis in SEM were validated with path coefficients greater than 0.1. Three hypotheses were changed and three were rejected. In addition to the relationships hypothesized, two additional relationships were discovered which promoted the convergence of the SEM model.

In many statistical modeling frameworks, goodness-of-fit tests are typically administered to the estimated residuals. In the time series setting, whiteness of the residuals is assessed using the sample autocorrelation function. For many time series models, especially those used for financial time series, the key assumption on the residuals is that they are in fact independent and not just uncorrelated. In this paper, we apply the auto-distance covariance function (ADCV) to evaluate the serial dependence of the estimated residuals. Distance covariance can discriminate between dependence and independence of two random vectors. The limit behavior of the test statistic based on the ADCV is derived for a general class of time series models. One of the key aspects in this theory is adjusting for the dependence that arises due to parameter estimation. This adjustment has essentially the same form regardless of the model specification. We illustrate the results in simulated examples.

Uncontrolled residual stresses have significant effects on the service time and defects of the spun parts. Nowadays, X-Ray Diffraction (XRD) method has been widely used in the residual stress measurement of industry products with different forming processes. The calculated residual stress is usually obtained from the data fitting slope of strain and angle with Ordinary Least Squares (OLS) method. But this fitting method is not always suitable for the big fluctuant data. In this paper, the Weighted Least Square (WLS) method is used for the data fitting and compared with the OLS method. The nickel-based superalloy GH3030 and iron-based superalloy GH1140 are applied in the multi-pass cold spinning experiments. The residual stress distributions of normal, potential crack and wrinkle workpieces are discussed with the grain structure. The results show that WLS method has better goodness of fit compared with OLS method. The residual stress distributions have special relationship with potential crack, wrinkle workpiece and grain structure.

This paper considers a new lifetime distribution called Gamma-Shanker distribution which is a compound of Gamma and Shanker distribution. Many important properties of the suggested distribution including its shape, Inverse moments, hazard rate function, reversed hazard rate function, quantile function and stress-strength reliability have been discussed. The estimation of its parameters has been discussed using maximum likelihood estimation. Goodness of fit of the proposed distribution has been explained with two examples of real lifetime data from biomedical sciences and it shows that the proposed distribution gives much closure fit over the considered distributions.

This study proposes a dynamic model for identifying the optimal amount of tourism taxes at the different stages of a destination’s lifecycle. Based on the Ramsey-Cass-Koopmans model of economic growth, we reformulate the Cobb-Douglas function to incorporate new variables to estimate the optimal level of tax that could maximize the total output of the tourism industry while maintaining below the critical point of the destination’s carrying capacity. We illustrate the model with an empirical study using time series data collected from 31 administrative regions in mainland China from 2000 to 2016. The results suggest that the proposed model has satisfactory goodness of fit, and the estimated tax amount was congruent with the trends of tourism development at the destinations. The study offers practical implications for destination policymakers.

The research is done to identify the constructs which affect the performance of a healthcare firm. Specifically, a mediating variable has been utilized to establish a relationship between the latent and dependent variables. The proposed model consists of three latent variables: vendor cooperation, SC efficiency, and quality measurement practices on afirm's performance through a mediator SC innovation. This model was developed using exploratory factor analysis and validated utilizing structural equation modeling. Information was collected from 310 respondents from various healthcare centers. The output suggested the proposed constructs significantly affected the firm's performance and contributed towards the goodness of fit. The output also indicates that a firm performance can be improvised through continuous SC innovation and cooperation with vendors, resulting in the firm's performance. Data utilized in this research were collected from various healthcare centers of India. The generalization of the results may be limited to hospitals in developingcountries. This research provides valuable information on planning in the healthcare segment. The outputs suggested firm's performance improvement is attained through continuous SC innovation.

Background Various conceptual gait models have been created with exploratory factor analysis to assess gait performance in healthy individuals or patients with orthopaedic or neurological diseases. However, a direct comparison between these models, to determine which is best for assessing gait in healthy elderly subjects, has never been performed. Confirmatory factor analysis is a statistical technique which allows to compare the structure of these models and to evaluate their validity and reliability. Research question Which of the current models for evaluating gait shows the best construct, convergent and discriminant validity and reliability when replicated in a sample of healthy elderly subjects? Methods 92 healthy elderly subjects (aged 73.3 ± 6.8 years) were enrolled. Participants were instructed to walk on a baropodometric walkway; gait variables were then extracted and analyzed according to 8 different gait models (published between 2007 and 2019). Correlation between variables of each model were investigated. The number of factors to include for each model was assessed with different criteria of principal component analysis. The construct validity of the gait models was assessed in terms of goodness of fit indexes through confirmatory factor analysis. Convergent and discriminant validity and reliability of the models were also assessed. Results Of the models considered, only our model previously created for patients with Parkinson’s Disease reached convergence, with an excellent fit in all indexes (χ2 = 18.34, df = 13, p = 0.15, RMSEA = 0.06 (95%CI = 0.00-0.13), CFI = 0.98, TLI = 0.96, SRMR = 0.05). The model showed an acceptable convergent validity. The three factors resulted to be mutually independent (correlations among factors Significance Future studies can use our gait model as a framework for clinical practice or research.

Resumen La relación ahusamiento-volumen es indispensable para la planeación del manejo silvícola en los bosques con la finalidad de favorecer la distribución de los productos. Por esta razón, el objetivo del presente estudio fue generar un modelo compatible que permitiera estimar de forma precisa el volumen-ahusamiento de Abies religiosa en varias regiones de México. Se utilizaron 2608 datos en el análisis de la información y el modelo se ajustó simultáneamente mediante la regresión aparentemente no relacionada (RAN) para la estimación de los parámetros compatibles. El modelo generó coeficientes de determinación ajustados superiores al 97 %, raíces del medio cuadrático menores al 0.40, sesgos menores a 0.11, con certeza para modelar cada región forestal; datos que fueron complementados con sus respectivos criterios de información Akaike (AIC). En la mayoría de los casos el factor de forma de la especie fue de neiloide a paraboloide, información que será de utilidad para la elaboración de inventarios forestales. Abstract The taper-volume relationship is an essential tool for planning silvicultural management in forests with the final aim of promoting the distribution of products. The objective of this study is the generation of a compatible model that allows an accurate estimation of the taper-volume of Abies religiosa in several regions of Mexico. A data set of 2608 values was considered for the analysis of the information; the model was simultaneously adjusted by seemingly unrelated regression (SUR) for the estimation of compatible parameters. The model generated coefficients of determination with fit levels higher than 97 %, roots mean square less than 0.40, biases lower than 0.11, with certainty to model each forest region, which are complemented with their respective Akaike information criteria (AIC). In most cases, the shape factor of the species went from neiloid to paraboloid. This information will be useful for timber forest inventories.

Summary Background and aims Lean muscle and fat mass in the human body are important indicators of the risk of cardiovascular and metabolic diseases. Techniques such as dual-energy X-ray absorptiometry (DXA) accurately measure body composition, but they are costly and not easily accessible. Multiple linear regression (MLR) models have been developed to estimate body composition using simple demographic and anthropometric measures instead of expensive techniques, but MLR models do not explore nonlinear interactions between inputs. In this study, we developed simple demographic and anthropometric measure-driven artificial neural network (ANN) models that can estimate lean muscle and fat mass more effectively than MLR models. Methods We extracted the demographic, anthropometric, and body composition measures of 20,137 participants from the National Health and Nutrition Examination Survey conducted between 1999 and 2006. We included 13 demographic and anthropometric measures as inputs for the ANN models and divided the dataset into training and validation sets (70:30 ratio) to build and cross-validate the models that estimate lean muscle and fat mass, which were originally measured using DXA. This process was repeated 100 times by randomly dividing the training and validation sets to eliminate any effect of data division on model performance. We built additional models separately for each sex and ethnicity, older individuals, and people with underlying diseases. The coefficient of determination (R2) and standard error of estimate (SEE) were used to quantify the goodness of fit. Results The ANN models yielded high R2 values between 0.923 and 0.981. These values were significantly higher than those of the MLR models (p Conclusions We developed and validated an inexpensive but effective method for estimating body composition using easily obtainable demographic and anthropometric data.

The aim of this paper is to highlight the use of Zero inflated Poisson distribution and Hurdle Poisson distribution to improve goodness of fit of data with excess zeros. Claim frequency data were used for the Singapore motor insurance database available on the internet. The data are tested for the detection of excess zeros. Parameters of Zero inflated Poisson distribution are estimated using method of moments and method of maximum likelihood. Mean and variance formulas are derived for the distribution of Hurdle Poisson and estimators of method of moment and maximum likelihood of unknown parameters are derived. Parameters of Hurdle Poisson are estimated. The data are modeled using Poisson distribution, Zero inflated Poisson distribution and Hurdle Poisson distribution. Goodness of fit are tested using Chi Square test. The best distribution in the study is selected using AIC & BIC criteria. Hurdle Poisson is the best distribution for modeling the data.

BACKGROUND Premature ejaculation (PE) is a highly prevalent male sexual dysfunction. Its current definition, together with the most used diagnostic tools, does not include non-vaginal sexual intercourse such as anal sex, self-masturbation and/or partnered-masturbation, and other forms of sexual stimulation. However, diagnostic psychometry currently available is exclusively evaluating PE in the vaginal coitus. OBJECTIVES To validate a new tool, the Masturbatory Premature Ejaculation Diagnostic Tool (MPEDT), by assessing the control over ejaculation and its psychological effects during self-masturbation, rather than heterosexual vaginal intercourse. MATERIALS AND METHODS We studied 135 male patients aging from 18 to 40 years seeking medical care for PE in the Infertility and Sexual Medicine Department from June to September 2020. All the participants were asked to fill the PEDT and MPEDT questionnaires to estimate the PE symptoms during, respectively, intercourse and self-masturbation. The reliability/validity, the factor analysis of the tool, and the diagnostic sensitivity/specificity of MPEDT were calculated. RESULTS The overall Cronbach alpha was 0.884. In our adjusted model, both RMSEA (Root Mean Square Error of Approximation) and SRMR (Standardized Root Mean Square Residual) were lower than 0.08, while Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Non-Normed Fit Index (NNFI), and Comparative Fit Index (CFI) were higher than 0.9. The area under the ROC curve (AUC) is 0.943±0.015. The results suggest MPEDT points being ≤5 as "normal", ≥7 as "PE during self-masturbation" and 6 as "suspected PE", with the sensitivity of 91.9% and specificity of 88.1%. DISCUSSION An efficient diagnostic psychometric tool is needed for the individuals who, based on the impaired control over ejaculation during self-masturbation, may also suffer from PE during partnered intercourse. MPEDT is able to evaluate the existence of PE symptoms not necessarily during heterosexual intercourse but rather during self-masturbation, possibly aiding to the diagnosis, as well as planning and follow-up of a prompt therapeutical strategy. CONCLUSION MPEDT could be considered a new, objective and reliable diagnostic tool for the evaluation of the existence of PE symptoms. This article is protected by copyright. All rights reserved.

Allometric scaling relations can be observed in between molecular parameters. Hence, we looked for presence of such relation among sizes (i.e., lengths) of proteins and genes. Protein lengths exist in the literature as the number of amino acids. They can also be derived from the mRNA lengths. Here, we looked for allometric scaling relation by using such data and simultaneously, the data was compared with the sizes of genes and proteins that were obtained from our modified information-theoretic approach. Results implied presence of scaling relation in the calculated results. This was expected due to the implemented modification in the information-theoretic calculation. Relation in the literature-based data was lacking high goodness of fit value. It could be due to physical factors and selective pressures, which ended up in deviations of the literature-sourced values from those in the model. Genome size is correlated with cell size. Intracellular volume, which is related to the DNA size, would require certain number of proteins, the sizes of which can therefore be correlated with the protein sizes. Cell sizes, genome sizes, and average protein and gene sizes, along with the number of proteins, namely the expression levels of the genes, are the physical factors, and the molecular factors influence those physical factors. The selective pressures on those can act through the connection between those physical factors and limit the dynamic ranges. Biological measures could be prone to such forces and are likely to deviate from expected models, regardless of the validity of assumptions, unless those are also implemented in the models. Yet, present discrepancies could be pointing at the need for model improvement, data imperfection, invalid assumptions, etc. Still, current work highlights possible use of information-theoretic approach in allometric scaling relations' studies.

Many failure and fracture studies of reactor pressure vessel material 20MnMoNi55 steel are performed in cryogenic conditions to study the scattered failure distribution property of the material in the lower self of ductile to brittle transition region. The experimental failure values of the RPV material are fitted with generalized extreme value (GEV) distribution and Weibull distribution through the commercial Statistical package R-i368 4.05 to study the nature of the distribution of fracture toughness in the lower self of ductile To brittle transition region. The nature of the distribution of fracture toughness of the RPV material 20MnMoNi55 steel is investigated from those experimental results. It is observed stochastically that the GEV distribution provides a better fit to the experimental failure data than the established Weibull distribution at least in the brittle portion of the DBT region of the tested material. This investigation is also supported by the two goodness of fit tests (Anderson–Darling test and Kolmogorov–Smirnov). Kolmogorov—Smirnov test results qualify Weibull distribution but indicated GEV as a better-fitted distribution of the experimental results. While Weibull distribution failed to qualify Anderson Darling test but GEV distribution qualifies the test. Therefore, GEV distribution can be thought, as a better option than Weibull distribution to capture the failure distribution for the RPV material in the lower self of the DBT region.

Establishing a probabilistic seismic demand model (PSDM), which illustrates the relationship between ground motion intensity measures (IMs) and engineering demand parameters (EDPs), is a critical step in the performance-based earthquake engineering (PBEE) framework. This model expresses the probability of a system experiencing a specific level of demand for a certain IM level. The optimal IM selection for intake towers taking soil-structure-water interaction into account has not been studied previously, and this research uses the Wimbleball intake tower as a case study. First, IMs and EDPs are chosen and classified based on their specifications. The optimal probabilistic seismic demand model is then determined by evaluating the IM-EDP charts generated for 150 ground motion records on a logarithmic scale using goodness of fit, practicality, efficiency, proficiency, and sufficiency criteria. By determining the optimal IM, it is possible to identify which IM has the greatest impact on structural response and better reveals the general trends of damage and failure. The Cordova intensity is determined to be the best option for the intake towers. Finally, seismic fragility curves are displayed, which represent the probability of EDP exceedance in terms of the IM parameter and can be used to assess initial damage to the structure.

Reflection is a key component of teacher development by which teachers can gain an understanding of the teaching knowledge, connect theory to practice and develop teaching skills. This study aimed to investigate the underlying structure of the items that make up ‘reflection-for-action’ in an Iranian EFL context. To present a framework for research and highlight the components of reflection-for-action, this study developed and validated a teacher reflection-for-action questionnaire. To this end, ten components were identified after undertaking a comprehensive review of the literature and conducting interviews with domain experts on Reflection. Then a draft version of the Reflection questionnaire, consisting of 49 items, was pilot tested with 200 teacher evaluators, who were working for various English language institutes and universities in Iran. The results, using Exploratory Factor Analysis (EFA), led to a 38-item questionnaire with strong estimates of reliability and validity. The results also demonstrated that the questionnaire consisted of a seven-factor structure of collaboration, motivation, perception, experience, academic qualification, professional development, and efficacy. Consequently, Confirmatory Factor Analysis (CFA) was carried out with another 200 Iranian EFL teachers to check the fitness of the proposed model. The result of CFA indicated that the model enjoyed a satisfactory level of goodness of fit, showing that the seven-factor were not the result of random variance in the learners’ responses. Finally, statistical results are discussed and implications are provided.

There are many alternative approaches to selecting mortality models and forecasting mortality. The standard practice is to produce forecasts using a single model such as the Lee-Carter, the Cairns-Blake-Dowd, or the Age- Period-Cohort model, with model selection based on in-sample goodness of fit measures. However, increasingly cross-validation measures based on forecasts are used in mortality model selection, and model combination methods such as Bayesian Model Averaging and Model Confidence Set have been proposed as alternatives to using a single model. We present a stacked regression ensemble method that optimally combines different mortality models to reduce the mean squared errors of mortality rate forecasts and mitigate model selection risk. Stacked regression uses a supervised machine learning algorithm to approximate the horizon-specific weights by minimizing the cross-validation criterion for each forecasting horizon. The horizon-specific weights facilitate the development of a mortality model combination customized to each horizon. Unlike other model combination methods, stacked regression simultaneously solves model selection and estimates model combinations to improve model forecasts. We use 44 populations from the Human Mortality Database to compare the stacked regression mortality approach with other alternative methods. We show that using one-year-ahead to 15−year-ahead out-of-sample mean squared errors, the stacked regression ensemble approach improves mortality forecast accuracy by 13% - 49% and 19% - 90% for females over individual mortality models. The stacked regression ensemble has better predictive accuracy than other model combination methods, including Simple Model Averaging, Bayesian Model Averaging, and Model Confidence Set. We provide a user-friendly open-source R package, CoMoMo, that estimates models and provides mortality rate forecasts for different mortality model combination methods.

The main core of Structural Equation Modeling (SEM) is the parameter estimation process. This process implies a variance-covariance matrix Σ that is close as possible to the sample variance-covariance matrix of data input (S). The six Sigma survey uses ordinal (rank) values from 1 to 5. There are several weighted correlation coefficients that overcome the problems of assigning equal weights to each rank and provide a locally most powerful rank test. This paper extends the SEM estimation method by adding the ordinal weighted techniques to enhance the goodness of fit indicators. A two data sets of the Six Sigma model with different statistics properties are used to investigate this idea. The weight 1.3 enhances the goodness of fit indicators with data set that has a negative skewness, and the weight 0.7 enhances the goodness of fit indicators with data set that has a positive skewness through treating the top-rankings.

OBJECTIVES The aim of this study was to compare the diagnostic accuracy of apparent diffusion coefficient (ADC) ratios as a monoparametric magnetic resonance imaging (MRI) protocol for the detection of prostate cancer (PCa) with the established multiparametric (mp) MRI at 3.0 T. MATERIALS AND METHODS According to power analysis, 52 male patients were included in this monocenter study with prospective data collection and retrospective, blinded multireader image analysis. The study was approved by the local ethics committee. Patients were recruited from January to December 2020. Based on mpMRI findings, patients underwent in-bore MR biopsy or prostatectomy for histopathologic correlation of suspicious lesions. Three readers, blinded to the histopathologic results and images of mpMRI, independently evaluated ADC maps for the detection of PCa. The ADC ratio was defined as the lowest signal intensity (SI) of lesions divided by the SI of normal tissue in the zone of origin. Predictive accuracy of multiparametric and monoparametric MRI were compared using logistic regression analysis. Moreover, both protocols were compared applying goodness-of-fit analysis with the Hosmer-Lemeshow test for continuous ADC ratios and Pearson χ2 test for binary decision calls, correlation analysis with Spearman ρ and intraclass correlation coefficients, as well as noninferiority assessment with a TOST ("two one-sided test"). RESULTS Eighty-one histopathologically proven, unique PCa lesions (Gleason score [GS] ≥ 3 + 3) in 52 patients could be unequivocally correlated, with 57 clinically significant (cs) PCa lesions (GS ≥ 3 + 4). Multiparametric MRI detected 95%, and monoparametric ADC detected ratios 91% to 93% of csPCa. Noninferiority of monoparametric MRI was confirmed by TOST (P < 0.05 for all comparisons). Logistic regression analysis revealed comparable predictive diagnostic accuracy of ADC ratios (73.7%-87.8%) versus mpMRI (72.2%-84.7%). Spearman rank correlation coefficient for PCa aggressiveness revealed satisfactory correlation of ADC ratios (P < 0.013 for all correlations). The Hosmer-Lemeshow test for the logistic regression analysis for continuous ADC ratios indicated adequate predictive accuracy (P = 0.55-0.87), and the Pearson χ2 test showed satisfactory goodness of fit (P = 0.35-0.69, χ2 = 0.16-0.87). CONCLUSIONS Normalized ADC ratios based on advanced DWI are noninferior to mpMRI at 3.0 T for the detection of csPCa in a preselected patient cohort and proved a fast and accurate assessment tool, thus showing a potential prospect of easing the development of future screening methods for PCa.

Advanced satellite geodetic systems have contributed to improving knowledge on changes in global gravity fields in recent years. These systems offer profound opportunities in the determination of a high-precision, high-resolution gravimetric geoid models in data deficient regions. However, due to the absence of research competence in some regions, these conventional datasets have not been implemented to update the obsolete reference frames which have been in use in these regions. This study introduces the well-known Remove-Compute-Restore (RCR) technique in modeling a gravimetric geoid model for a large data deficient region in West Africa (Nigeria) using two sets of long and short wavelength data (a) EGM2008 (long) + Airborne gravimetric observation (AGO) dataset (short) (b) EGM2008 (long) + Terrestrial gravimetric undulation (TGU) dataset (short). This therefore resulted in obtaining two sets of resultant RCR-gravimetric details whose statistics comparative assessments are produced in the body of this work. The RCR-determined gravimetric geoid model showed a strong relationship with the observed terrestrial data. The goodness of fit for the orthometric height correlation between the computed gravimetric geoid and the terrestrial data is 97.87 %. Strong relationships between the computed model and the other height models determined from the primary data are also observed. The difference between the Flury and rummel geoid-based model and the Heiskanen and moritz model in the geoid-quasi geoid separation phase shows the importance of implementing the ‘rigorous modeling technique’ for geoid quasi-geoid separation instead of the approximation method. However, the discrepancy between both models can be overlooked in a case of low accuracy geoidal operations. The RCR-based Fast Fourier Transform (FFT), and the least squares modified stokes (LSMS) was used in the quasi-geoid determination of the study region. Using the 1-parameter fit, the modified Stokes integral (FFT(b)) outperformed the LSMS model with a root mean square (rms) difference of 0.6 cm, while the LSMS outperformed both the unmodified (FFT(a)) and the modified (FFT(b)) Stokes integral using the 4-parameter fit with a rms difference of 0.5 cm. The use of both models in this study created a better perception of the quasi-geoid differential analysis, and a more refined understanding of their interaction with the satellite based quasi geoid.

The purpose of this research was to measure the acceptance of Google Classroom use by junior high school students using variables such as habits, satisfaction, knowledge, and skills. To achieve the set objective, it adopted the TAM, which measures technology usability and ease of use. TAM also provides a theoretical basis for measuring students’ perceptions concerning the use of Google Classroom in online learning during the COVID-19 pandemic. Based on this, a questionnaire was used to collect data through an online survey involving a sample of 306 students at the junior high school level. The data obtained was analyzed using exploratory and confirmatory factor analyses to obtain valid and reliable measuring instruments. AMOS was used to measure, assess the structural model based on the goodness of fit standard and test hypotheses. as many as 84.3% of students had never used Google Classroom until the COVID-19 pandemic struck. Keywords: TAM, Habit, Satisfaction, Knowledge, and skill

This study aims to determine the effect of leadership style, work environment and performance benefits on employee performance at the Service Office for Placement and Protection of Indonesian Migrant Workers (BP3TKI) Medan. The effect of these variables on employee performance was studied using statistical analysis. The sampling technique used in this study is saturated sampling. Scaled surveys has been done by 116 employees of Service Office for Placement and Protection of Indonesian Migrant Workers (BP3TKI) Medan. The statistical tool used is the Structural Equation Modelling (SEM), and analysed using LISREL 8.80 statistic software. The model developed fulfil Goodness of Fit (GoF) requirements from LISREL 8.80 to which shows that it is good enough. The most significant sub-variables are no strong grip and low confidence from leader, legislations related to employee’s performance benefits, and good air circulations in the workspace, which strongly affect work quality, time efficiency, and cooperation from employees in Medan BP3TKI office. For further research, other researchers, institutions, or parties, could use this research conclusion and suggestions as foundation or additional information for similar problems, that is to improve employee performance in an office.

This study aimed to assess the structural relationships between metacognition, emotional flexibility, affective styles, and worry in a nonclinical Persian sample. Two hundred seventy-seven undergraduate students were selected by convenience sampling and then completed the Pennsylvania State Worry Questionnaire (PSWQ), Metacognitive Questionnaire-30 (MCQ-30), Affective Styles Questionnaire (ASQ), and Emotional Flexibility Questionnaire (EFQ). Data were analyzed using structural equation modeling (SEM) based on Linear Structural relationships (LISREL). The model examination indicated that the proposed theoretical model had the goodness of fit with the measurement model (SMSEA = 0.04; SRMR = 0.04; NFI = 0.97; GFI = 0.97; CFI = 0.99; IFI = 0.99). Also, the results of SEM presented the significant direct impact of metacognition on worry (β = 0.40; P

Author(s): Zheng, Bang Quan | Advisor(s): Bentler, Peter M | Abstract: Does Reweighted Least Squares (RLS) perform better in small samples than maximum likelihood (ML) for mean and covariance structure? ML statistics in covariance structure analysis are based on the asymptotic normality assumption; however, actual applications of structural equation modeling (SEM) in social and behavioral science research usually involve small samples. It has been found that chi-square tests often incorrectly over-reject the null hypothesis: Σ=Σ(θ), because when sample is small the sample covariance matrix becomes ill-conditioned and entails unstable estimates. In certain SEM models, the vector of parameter must contain both means, variances and covariances. Yet, whether RLS also works in mean and covariance structure remains unexamined. This research is an extended examination of reweighted least squares in mean and covariance structure. Specifically, we replace biased covariance matrix in traditional GLS function (Browne, 1974) with the unbiased sample covariance matrix that derives from ML estimation. Moreover, under the assumption of multivariate normality, a Monte Carlo simulation study was carried out to examine the statistical performance as compared with ML methods in different sample sizes. Based on empirical rejection frequencies and empirical averages of test statistic, this study shows that RLS performs much better than ML in mean and covariance structure models when sample sizes are small.

The literature review highlighted the impacts of drivers’ behavior on passengers’ attitudes in the choice of seatbelt usage. However, limited studies have been done to determine those impacts. Studying the passengers’ seatbelt use is especially needed to find out why passengers choose not to buckle up, and consequently it helps decision makers to target appropriate groups. So, this study was conducted to find drivers’ characteristics that might impact the passenger’s seatbelt use, in addition to other passengers’ characteristics themselves. While performing any analysis, it is important to use a right statistical model to achieve a less biased point estimate of the model parameters. The latent class multinomial logit model (LC-MNL) can be seen as an alternative to the mixed logit model, replacing the continuous with a discrete distribution, by capturing possible heterogeneity through membership in various clusters. In this study, instead of a response to the survey or crash observations, we employed a real-life observational data for the analysis. Results derived from the analysis reveal a clear indication of heterogeneity across individuals for almost all parameters. Various socio-demographic variables for class allocation and models with different latent numbers were considered and checked in terms of goodness of fit. The results indicated that a class membership with three factors based on vehicle type would result in a best fit. The results also highlighted the significant impacts of driver seatbelt status, time of a day, distance of traveling, vehicle type, and driver gender, instead of passenger gender, as some of the factors impacting the passengers’ choice of seatbelt usage. In addition, it was found that the belting status of passengers is positively associated with the belting condition of drivers, highlighting the psychological behavioral impact of drivers on passengers. Extensive discussion has been made regarding the implications of the findings.

Objetivo: Realizar la traducción, adaptación cultural y lingüística y la valoración de las cualidades psicométricas de la Escala de Evaluación de Estrés Parental en la Unidad de Neonatología - versión modificada.Método: Estudio metodológico con enfoque cuantitativo, para validar la Escala de Evaluación de Estrés Parental en la Unidad de Neonatología. Los padres fueron seleccionados por muestreo no aleatorio (n = 406 padres). En la estimación de la sensibilidad, validez y confiabilidad de la Escala de Evaluación de Estrés Parental en Neonatología se utilizó estadística descriptiva, alfa de Cronbach estandarizado y análisis factorial confirmatorio (índices de calidad del ajuste del modelo χ2 / df, CFI, TLI, RMSEA, P (RMSEA).Resultados: Los índices empíricos de la calidad del ajuste del modelo factorial EASPUN-VM, presentan una calidad aceptable (χ2 / gl = 3.3; CFI = 0.8; GFI = 0.8; TLI = 0.8 y PRMSEA = 0,07).Conclusión: La Escala de Evaluación de Estrés Parental en la Unidad de Neonatología - versión modificada es un instrumento psicométricamente sensible, válido y confiable que permitirá la evaluación sistemática del estrés parental, cuando los niños ingresan en las unidades de Neonatología, facilita la toma de decisiones de enfermeras en el seguimiento de los padres y derivación a otros profesionales de la salud. Objective: To translate the Neonatal Unit Parental Stress scale into Portuguese and culturally and linguistically adapt it for Portugal, and to evaluate the psychometric qualities of the Neonatal Unit Parental Stress Assessment Scale - Modified Version (EASPUN-MV). Method: This was a quantitative methodological study to validate the Neonatal Unit Parental Stress Assessment Scale. Parents were selected by non-probabilistic sampling (n = 406 parents). Descriptive statistics, standardized Cronbach's alpha, and confirmatory factor analysis were used to calculate the sensitivity, validity, and reliability of the Neonatology Unit Parental Stress Assessment Scale (indexes of the model's goodness of fit χ2/df, CFI, TLI, RMSEA, P(RMSEA). Results: The empirical indexes of the EASPUN-MV’s factorial model showed acceptable goodness of fit (χ2/df=3.3; CFI=0.8; GFI=0.8; TLI=0.8 and P(RMSEA)=0.07). Conclusion: The Neonatology Unit Parental Stress Assessment Scale - Modified Version is a psychometrically sensitive, valid and reliable instrument that can systematically assess parental stress and facilitate the decision-making of nurses, the follow-up care of parents, and their referral to other healthcare professionals. Objetivo: Fazer a tradução, adaptação cultural e linguística e a avaliação das qualidades psicométricas da Escala de Avaliação do Stress Parental na Unidade de Neonatologia-versão modificada.Método: Estudo metodológico com abordagem quantitativa, para validação da Escala de Avaliação do Stress Parental na Unidade de Neonatologia. Os pais foram selecionados por amostragem não aleatória (n=406 pais). Na estimação da sensibilidade, validade e fiabilidade da Escala de Avaliação do Stress Parental na Neonatologia recorreu-se à estatística descritiva, alfa de Cronbach estandardizado e análise fatorial confirmatória (índices de qualidade do ajustamento do modelo χ2/df, CFI, TLI, RMSEA, P(RMSEA). Resultados: Os índices empíricos da qualidade do ajustamento do modelo fatorial da EASPUN-VM, apresentam uma qualidade aceitável (χ2/df=3,3; CFI=0,8; GFI=0,8; TLI=0,8 e PRMSEA=0,07). Conclusão: A Escala de Avaliação do Stress Parental na Unidade de Neonatologia-versão modificada é um instrumento psicometricamente sensível, válido e fiável que permitirá a avaliação sistematizada do stress parental, aquando do internamento dos filhos nas unidades de Neonatologia, facilita a tomada de decisão dos enfermeiros no acompanhamento dos pais e no encaminhamento para outros profissionais de saúde.

The traditional online education resource integration technology ignores the calculation of the characteristic variance contribution rate of online educational resources, and the evaluation of educational resources is biased, which leads to the low ability of resource integration. Therefore, the online education resource integration technology based on remote scheduling of teaching information is proposed. Based on the theory of remote scheduling of teaching information, the quantitative value of online educational resources integration is obtained. By calculating the integration probability of online educational resources, the influencing factors of online educational resources in the process of integration are determined. The principle of teaching information remote scheduling is integrated into the feature vector extraction of online educational resources, and the contribution rate of feature variance of online educational resources is calculated. By extracting the number of main factors of the online educational resources feature vector, the feature vector of online educational resources is decomposed. Taking the wavelet entropy of online educational resources as the fusion weight of online educational resources, the integration result of online educational resources is obtained through wavelet transform. The experimental results show that the online education resource integration technology based on remote scheduling of teaching information has ideal application performance in integration probability, the goodness of fit, and robustness.

In a multiple regression model setup, we examine the effects of trimming and winsorization of regressors on the regression estimates and their efficiency. The exact results developed here to enable...

AIM: This study aims to clarify the construct and criterion-related validity of the Questionnaire for Older Senior Citizens (QO) during the COVID-19 pandemic. METHODS: This cross-sectional study was conducted in Japan between November 11, 2020 and January 10, 2021. Of the 1645 (63.5%) older adults who responded, data from 900 participants were analyzed. First, we conducted an exploratory factor analysis (EFA) among older-older adults (aged ≥75 years) and extracted the factors. Next, we conducted a confirmatory factor analysis (CFA) using structural equation modeling. We also conducted this analysis among younger-older adults (aged ≥65 and

Background The universal Surgical Risk Preoperative Assessment System (SURPAS) prediction models for postoperative adverse outcomes have good accuracy for estimating risk in broad surgical populations and for surgical specialties. The accuracy in individual operations has not yet been assessed. The objective of this study was to evaluate the Surgical Risk Preoperative Assessment System in predicting adverse outcomes for selected individual operations. Methods The SURPAS models were applied to the top 2 most frequent common procedural terminology codes in 9 surgical specialties and 5 additional common general surgical operations in the 2009 to 2018 database of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). Goodness of fit statistics were estimated, including c-indices for discrimination, Hosmer-Lemeshow graphs and P values for calibration, overall observed versus expected event rates, and Brier scores. Results The total sample size was 2,020,172, which represented 29% of the 6.9 million operations in the ACS NSQIP database. Average c-indices across 12 outcomes were acceptable (≥0.70) for 13 (56.5%) of the 23 operations. Overall observed-to-expected rates were similar for mortality and overall morbidity across the 23 operations. Hosmer-Lemeshow graphs over quintiles of risk comparing observed-to-expected rates of mortality and overall morbidity were similar for 52% and 70% of operations, respectively. Model performance was better in less complex operations and those done in patients with lower preoperative risk. Conclusion SURPAS displayed accuracy in estimating postoperative adverse events for some of the 23 operations studied, but not all. In the procedures where SURPAS was not accurate, developing disease or operation-specific risk models might be appropriate.

Purpose – The aim of the research is to determine the effect of psychosocial welfare measures such as impulsive use of the internet, which is an indispensable element of life today, and the fear of missing out, on impulsive buying and its effect on fatigue and personal anxiety caused by internet use. Design/methodology/approach – The main mass of the research consists of consumers who live in any province of Turkey, are 18 years of age and older, and shop online. Survey technique was used to collect data and surveys were created via Google Forms. Snowball sampling method was used. 5 scales used in the survey; They are “Impulsive Use”, “Fear of Missing Out”, “Impulsive Buying”, “Internet-Induced Fatigue” and “Anxiety”. The research data were analyzed with IBM SPSS 24 and IBM AMOS 27 statistical package programs. Structural equation modeling was used to test the hypotheses. Previously, frequency analysis of demographic variables, compatibility of data to normal distribution, exploratory and confirmatory factor analysis, reliability analysis were performed. Findings – According to the results of the research, when the observation values ​​of the variables were examined, the skewness and kurtosis values ​​of the variables were found to be between -2 and +2, and it was seen that they met the normal distribution condition. After the exploratory factor analysis, the factor loading of a question of the “Fatigue due to Internet” variable was less than 0.50 and the total explained. variancewas low, this 1 question was removed and confirmatory factor analysis was performed. It was determined that the goodness of fit values ​​of the confirmatory factor analysis were valid. As a result of the structural equation modeling, all four hypotheses were accepted and it was found that there was a significant and positive interaction between all tested relations. Impulsive use positively affects impulsive buying at a rate of 0.479, fear of missing out positively affects impulsive buying at a rate of 0.250, impulsive buying positively affects fatigue due to internet with a positive rate of 0.395 and impulsive buying anxiety positively with a rate of 0.323. Discussion – It has been observed that most of the data obtained as a result of the analysis support the relevant literature. However, some findings specific to this study, which had not been investigated before, were also reached. Various suggestions have been developed in line with the analysis results of the research.

Bennett (Statistics in Medicine 4 (1985), 535-539) proposed a goodness-of-fit statistic to compare two predictive values in an attempt to improve upon its earlier version he derived in 1976. Howeve...

This study, conducted between October 2018 and May 2019, aimed to develop an Exercise Attitude Scale in Turkish (EAS-Turkish) for pregnant women and to determine its validity and reliability. The scale was prepared in Turkish to comprehensively measure pregnant women's exercise attitudes. Then, it administered in 253 pregnant women, who were native speakers of Turkish and older than 18 years, in the Obstetrics Polyclinic at Ataturk Training and Research Hospital, Ankara, Turkey. Validity with exploratory and confirmatory factor analysis and reliability with test-retest and internal consistency methods were tested. The 37-item scale was found to be 2-dimensional (knowledge and benefit; barrier). All indexes of the goodness of fit (χ2/df = 2.0, Comparative Fit Index = 0.90, Goodness-of-fit Index = 0.85, Root Mean Square Error of Approximation = 0.06) indicated that the fit between the model and the sample data was acceptable. The item-total score correlations varied between r = 0.22 and 0.60. The Cronbach alpha coefficients were found as 0.90 for the whole scale, 0.91 for the knowledge and benefit sub-dimension, and 0.87 for the barrier sub-dimension. In the test-retest analysis conducted with 41 pregnant women, the reliability coefficients were detected as 0.93 for the whole scale and 0.84 for each sub-dimension. The EAS-Turkish for pregnant women was found to be a valid and reliable tool.

Recently, the characterization-based approach for the construction of goodness of fit tests has become popular. Most of the proposed tests have been designed for complete i.i.d. samples. Here, we present the adaptation of the recently proposed exponentiality tests based on equidistribution-type characterizations for the case of randomly censored data. Their asymptotic properties are provided. Besides, we present the results of wide empirical power study including the powers of several recent competitors. This study can be used as a benchmark for future tests proposed for this kind of data.

Right now, more than ever in our lives, we need to remain optimistic. The present study aimed to examine the validity and reliability of The Optimism Scale, developed by Balci & Yilmaz (2002), among Iranian university students and to adapt to Iranian culture. The scale is a single-factor scale with 24 items. The participants were selected using an appropriate sampling method, and the sample consisted of 600 Iranian university students (360 females and 240 males) from Tehran and Tabriz cities. Participants’ age ranged from 18 to 24 years (M = 21.58, SD = 1.75). The study findings suggested similarities between Iran and Turkey culture (e.g., religion, language, neighborhood relations). The optimism scale revealed a good construct validity in the Goodness of fit index indicators. The Cronbach alpha internal consistency coefficient for the scale was 0.76 and Convergent validity with happiness was 0.38. In future studies, the scale’s factor structure can be investigated in different samples, such as adolescents and adults.

The calculation of DFT (density functional theory) chemical shifts have become an important technique for the verification of a proposed structure. An easily calculated metric developed for proton and carbon chemical shifts of natural products and organic compounds, the calculated chemical shift index (CCSI), has been developed, which uses the deviation of each pair of calculated and experimental chemical shifts. The mean absolute deviation (MAD), which is commonly used as the goodness of fit metric for DFT calculated chemical shifts, can conceal large deviations in the calculated data. A classification strategy is also proposed for the CCSI to highlight when further assessment of the NMR data is required.

In recent years various probability models have been proposed for describing lifetime data. Increasing model flexibility is often sought as a means to better describe asymmetric and heavy tail distributions. Such extensions were pioneered by the beta-G family. However, efficient goodness-of-fit (GoF) measures for the beta-G distributions are sought. In this paper, we combine probability weighted moments (PWMs) and the Mellin transform (MT) in order to furnish new qualitative and quantitative GoF tools for model selection within the beta-G class. We derive PWMs for the Fr\’{e}chet and Kumaraswamy distributions; and we provide expressions for the MT, and for the log-cumulants (LC) of the beta-Weibull, beta-Fr\’{e}chet, beta-Kumaraswamy, and beta-log-logistic distributions. Subsequently, we construct LC diagrams and, based on the Hotelling’s $T^2$ statistic, we derive confidence ellipses for the LCs. Finally, the proposed GoF measures are applied on five real data sets in order to demonstrate their applicability.

The 60-item HEXACO (HEXACO-60) Personality Trait Inventory may be strategically more important for researchers compared to the 100-item HEXACO. The aim of this study was to assess the psychometric properties of the 60-item HEXACO questionnaire in the Serbian language. This cross-sectional study, conducted at three of five Faculties of Medicine during the summer semester of the 2016/2017 academic year, included 617 students. Relevant data were collected by an anonymous questionnaire. Cronbach's α and McDonald's ω coefficients for the total scale were .71 and .72, respectively. Apart from Agreeableness, all other domains showed an adequate level of internal consistency (both Cronbach's α and McDonald's ω coefficients were >.70). Random data eigenvalues obtained on the parallel analysis had higher values than initial exploratory factor analysis eigenvalues up to the sixth factor. Accordingly, it was concluded that six factors should be extracted from the Serbian HEXACO-60. These factors corresponded in item distribution with the original ones. Based on the confirmatory factor analysis, using both 60 items and 24 facets, overall goodness of fit of the Serbian HEXACO-60 was acceptable. Moreover, there were no domains with completely invalid indices, as there were at least two indices that were acceptable for each domain. The Serbian version of the HEXACO-60 has acceptable psychometric properties. This shorter version with 60 items may be more suitable for brief surveys or when the time allocated to fill in a questionnaire is limited.

Disruption to airline schedules is a key issue for the industry. There are various causes for disruption, ranging from weather events through to technical problems grounding aircraft. Delays can quickly propagate through a schedule, leading to high financial and reputational costs. Mitigating the impact of a disruption by adjusting the schedule is a high priority for the airlines. The problem involves rearranging aircraft, crew and passengers, often with large fleets and many uncertain elements. The multiple objectives, cost, delay and minimising schedule alterations, create a trade-off. In addition, the new schedule should be achievable without over-promising. This thesis considers the rescheduling of aircraft, the Aircraft Recovery Problem. The Aircraft Recovery Problem is well studied, though the literature mostly focusses on deterministic approaches, capable of modelling the complexity of the industry but with limited ability to capture the inherent uncertainty. Simulation offers a natural modelling framework, handling both the complexity and variability. However, the combinatorial aircraft allocation constraints are difficult for many simulation optimisation approaches, suggesting that a more tailored approach is required. This thesis proposes a two-stage multi-fidelity modelling approach, combining a low-fidelity Integer Program and a simulation. The deterministic Integer Program allocates aircraft to flights and gives an initial estimate of the delay of each flight. By solving in a multi-objective manner, it can quickly produce a set of promising solutions representing different trade-offs between disruption costs, total delay and the number of schedule alterations. The simulation is used to evaluate the candidate solutions and look for further local improvement. The aircraft allocation is fixed whilst a local search is performed over the flight delays, a continuous valued problem, aiming reduce costs. This is done by developing an adapted version of STRONG, a stochastic trust-region approach. The extension incorporates experimental design principles and projected gradient steps into STRONG to enable it to handle bound constraints. This method is demonstrated and evaluated with computational experiments on a set of disruptions with different fleet sizes and different numbers of disrupted aircraft. The results suggest that this multi-fidelity combination can produce good solutions to the Aircraft Recovery Problem. A more theoretical treatment of the extended trust-region simulation optimisation is also presented. The conditions under which a guarantee of the algorithm's asymptotic performance may be possible and a framework for proving these guarantees is presented. Some of the work towards this is discussed and we highlight where further work is required. This multi-fidelity approach could be used to implement a simulation-based decision support system for real-time disruption handling. The use of simulation for operational decisions raises the issue of how to evaluate a simulation-based tool and its predictions. It is argued that this is not a straightforward question of the real-world result being good or bad, as natural system variability can mask the results. This problem is formalised and a method is proposed for detecting systematic errors that could lead to poor decision making. The method is based on the Probability Integral Transformation using the simulation Empirical Cumulative Distribution Function and goodness of fit hypothesis tests for uniformity. This method is tested by applying it to the airline disruption problem previously discussed. Another simulation acts as a proxy real world, which deviates from the simulation in the runway service times. The results suggest that the method has high power when the deviations have a high impact on the performance measure of interest (more than 20%), but low power when the impact is less than 5%.

This paper proposes an effective unsupervised Bayesian framework for learning a finite mixture of asymmetric generalized Gaussian distributions (AGGD). The parameters are estimated by a hybrid Markov Chain Monte Carlo (MCMC) approach, which is a combination of Metropolis-Hastings and Gibbs sampling techniques. The motivation of this research is to add modeling flexibility to Gaussian distribution and enhance its ability to fit asymmetric and non-Gaussian data by introducing additional parameters. In order to examine the performance of the proposed model, several experiments are conducted, and the model is evaluated on various real world applications. The performance of our model is measured using a wide range of evaluation techniques and compared with the performance of benchmark models to investigate the goodness of fit. From the results, it is observed that proposed model has performed better in clustering applications as compared to Bayesian Gaussian and Bayesian asymmetric Gaussian mixture models.

Growth is a fundamental aspect of a living organism. Growth curves play an important role in explaining the complex dynamics of growth trajectories. The development of a large class of growth models provides more choices to explain complex growth dynamics. However, identifying a suitable growth curve from a broad class of growth models becomes a challenging task. Relative Growth Rate (RGR) is the most popular measure in the growth-related study. It serves many purposes in growth curve literature, including constructing any goodness-of-fit index of some growth dynamics. However, the goodness-of-fit test based on RGR is restricted to only simple growth models. This study aims to develop a new growth rate function, instantaneous maturity rate (IMR), which can play an important role in identifying growth models. We have explored that the measure has synergy in mathematical form with IMR. However, unlike the hazard rate, IMR is a random variable when the size/RGR variable is stochastic. We have derived the exact and asymptotic distribution of this measure under the Gaussian setup of both the size and RGR variables. We have constructed a goodness-of-fit test for the extended Gompertz growth model based on the instantaneous maturity rate. We have checked the performance of the test through simulation studies as well as real data. AMS 2010 subject classifications: 62Mxx, 92Bxx, 62P10