Students’ Survey Evaluation: A New Paradigm

The lynchpin of any educational setup is the duo of student and teacher; the third vital component which regulates the activities of the duo is educational management of the setup. The present study focuses on eliciting the opinions of students from three diplomas organized by Deanship of Community Services and Continuing Education, King Abdulaziz University, Jeddah to study the effectiveness of diplomas. The instrument diploma evaluation questionnaire (DEQ) used to collect data was a modified version of the course evaluation questionnaire (CEQ) developed by the Saudi National commission of Assessment and Academic Accreditation (NCAAA). A sample of 240 diploma students both male and female participated in the study. Statistical evaluation was carried out using SPSS ver 21 and some relevant figures were drawn using AMOS software. Findings of this study coupled with other inputs can simultaneously be used by pedagogical staff and administrators to frame future policies for improving the quality of educational diplomas in an institution or program. Results of the study pinpointed some areas which need to be focused on in future diplomas: for instance, orientation about the diplomas needs more elaboration, provision of training material and linkage between the theory and practice be established. The relationship between the three subscales and Overall Evaluation (OE) is significant with ‘Diploma evaluation’ subscale as the most effective predictor for OE followed by ‘During the diploma’ subscale.  The study also demonstrated the robust evidence of objectivity and data authenticity. The easy-to-follow approach has been adopted so that pedagogical and administrative staff can effectively use the techniques proposed in the current study. The evidence thus extracted can be used to structure efficient prospective policies than can surely enhance student experiences during their educational discourses

Assessing Volatility Modelling using three Error Distributions

The current study focuses on estimating the volatility of stock returns in the presence of flat tails error distribution (i.e. asymmetry of the distribution) which a traditional generalized auto-regressive conditional heteroscedasticity GARCH model usually fails to explain. The study, unlike the previous studies, compares three sets of error distributions for GARCH (1, 1) model of stock returns.  The three sets of error distributions used for comparing the predictive ability of GARCH (1, 1) model are –Gaussian (normal distribution), student’s t and generalized error distribution (GED). Eviews software was used for analyzing a time series data of Flying cement stock shares consisting of 245 days of in sample and 15 days of out-of-sample data. To compare the forecasting capability of three models root mean square (RMSE) and Theil’s Inequality Coefficient (TIC) were used. Akaike information criterion (AIC), the Schwarz information criterion (SIC), Hannan, and Quin (HQ) information criteria were examined for selection of a suitable model for capturing volatility of stock returns in the presence of symmetrical and asymmetrical distributions. Results of the study revealed that GARCH (1, 1) with GED is the best model for capturing the volatility of stock returns of Flying Cement Industry. Results of the present study will provide a stimulus to academia and practitioners for incorporating asymmetry aspect of the distribution in future prediction and capturing volatility of stock returns

Assessing Volatility Modelling using three Error Distributions

The current study focuses on estimating the volatility of stock returns in the presence of flat tails error distribution (i.e. asymmetry of the distribution) which a traditional generalized auto-regressive conditional heteroscedasticity GARCH model usually fails to explain. The study, unlike the previous studies, compares three sets of error distributions for GARCH (1, 1) model of stock returns.  The three sets of error distributions used for comparing the predictive ability of GARCH (1, 1) model are –Gaussian (normal distribution), student’s t and generalized error distribution (GED). Eviews software was used for analyzing a time series data of Flying cement stock shares consisting of 245 days of in sample and 15 days of out-of-sample data. To compare the forecasting capability of three models root mean square (RMSE) and Theil’s Inequality Coefficient (TIC) were used. Akaike information criterion (AIC), the Schwarz information criterion (SIC), Hannan, and Quin (HQ) information criteria were examined for selection of a suitable model for capturing volatility of stock returns in the presence of symmetrical and asymmetrical distributions. Results of the study revealed that GARCH (1, 1) with GED is the best model for capturing the volatility of stock returns of Flying Cement Industry. Results of the present study will provide a stimulus to academia and practitioners for incorporating asymmetry aspect of the distribution in future prediction and capturing volatility of stock returns

Inferences of generalized inverted exponential distribution based on partially constant-stress accelerated life testing under progressive Type-II censoring

The problem of testing the product units under stress higher than normal stress conditions is widely used in reliability analysis specially, in a high reliable products to saving time and cost which is known by accelerated life tests (ALTs) model. In this paper, we are adopted partially constant-stress ALTs model for product units have generalized inverted exponential (GIE) lifetime. And, the test is running under progressive Type-II censoring scheme. The model parameters are estimated with maximum likelihood and Bayes methods. The corresponding asymptotic confidence intervals as well as credible intervals are also constructed. The theoretical results are assessed and compared through Monte Carlo simulation study. The numerical data set is analyzed under the proposed model for illustrative purpose. Finally, we reported some comments about numerical computation

Process Monitoring for Gamma Distributed Product under Neutrosophic Statistics Using Resampling Scheme

In this article, a repetitive sampling control chart for the gamma distribution under the indeterminate environment has been presented. The control chart coefficients, probability of in-control, probability of out-of-control, and average run lengths have been determined under the assumption of the symmetrical property of the normal distribution using the neutrosophic interval method. The performance of the designed chart has been evaluated using the average run length measurements under different process settings for an indeterminate environment. In-control and out-of-control nature of the proposed chart under different levels of shifts have been described. The comparison of the proposed chart has been made with the existing chart. A real-world example from the healthcare department has been included for the practical application of the proposed chart. It has been observed from the simulation study and real example that the proposed control chart is efficient in quick monitoring of the out-of-control process. It can be concluded that the proposed control chart can be applied effectively in uncertainty

On estimation procedures of stress-strength reliability for Weibull distribution with application.

For the first time, ten frequentist estimation methods are considered on stress-strength reliability R = P(Y < X) when X and Y are two independent Weibull distributions with the same shape parameter. The start point to estimate the parameter R is the maximum likelihood method. Other than the maximum likelihood method, a nine frequentist estimation methods are used to estimate R, namely: least square, weighted least square, percentile, maximum product of spacing, minimum spacing absolute distance, minimum spacing absolute-log distance, method of Cramér-von Mises, Anderson-Darling and Right-tail Anderson-Darling. We also consider two parametric bootstrap confidence intervals of R. We compare the efficiency of the different proposed estimators by conducting an extensive Mont Carlo simulation study. The performance and the finite sample properties of the different estimators are compared in terms of relative biases and relative mean squared errors. The Mont Carlo simulation study revels that the percentile and maximum product of spacing methods are highly competitive with the other methods for small and large sample sizes. To show the applicability and the importance of the proposed estimators, we analyze one real data set

A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance

Abstract Background Prediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player's future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches. Methodology To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series). Results The study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts. Conclusion In conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance

Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models.

BackgroundGross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country.MethodologyStochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoothing, TBATS, and NNAR are applied. Subsequently, hybrid models combining these individual time series approaches are utilized. Model diagnostics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), are employed as criteria for model selection to identify the best-performing model.ResultsThe findings demonstrated that the neural network autoregressive (NNAR) model, as a non-linear approach, outperformed all other models, exhibiting the lowest values of MAE, RMSE and MAPE. The NNAR(5,3) projected the GDP of 1.3% which is close to the projection of IMF benchmark (1.9) for the year 2023.ConclusionThe selected model can be employed by economists and policymakers to formulate appropriate policies and plans. This quantitative study provides policymakers with a basis for monitoring fluctuations in GDP growth from 2022 to 2029 and ensuring the sustained progression of GDP beyond 2029. Additionally, this study serves as a guide for researchers to test these approaches in different economic dynamics

The Exponentiated Truncated Inverse Weibull-Generated Family of Distributions with Applications

In this paper, we propose a generalization of the so-called truncated inverse Weibull-generated family of distributions by the use of the power transform, adding a new shape parameter. We motivate this generalization by presenting theoretical and practical gains, both consequences of new flexible symmetric/asymmetric properties in a wide sense. Our main mathematical results are about stochastic ordering, uni/multimodality analysis, series expansions of crucial probability functions, probability weighted moments, raw and central moments, order statistics, and the maximum likelihood method. The special member of the family defined with the inverse Weibull distribution as baseline is highlighted. It constitutes a new four-parameter lifetime distribution which brightensby the multitude of different shapes of the corresponding probability density and hazard rate functions. Then, we use it for modelling purposes. In particular, a complete numerical study is performed, showing the efficiency of the corresponding maximum likelihood estimates by simulation work, and fitting three practical data sets, with fair comparison to six notable models of the literature