Box-Cox and Yeo-Johnson transformation models were utilized in this paper to use density function to improve multivariate time series forecasting. The K-Nearest Neighbor function is used in our model, with automatic bandwidth selection using a cross-validation approach and semi-metrics used to measure the proximity of functional data. Then, to decorrelate multivariate response variables, we use principal component analysis. The methodology was applied on two time series data examples with multiple responses. The first example includes three time series datasets of the monthly average of Humidity (H), Rainfall (R) and Temperature (T). The simulation studies are provided in the second example. Mean square errors of predicted values were calculated to show forecast efficiency. The results have proved that applying multivariate nonparametric time series transformed stationary datasets using the Yeo-Johnson model more efficient than applying the univariate nonparametric analysis to each response independently.

This article introduces a novel algorithm for incorporating power transformation into the estimation process of a Holt-Winters Seasonal model. The algorithm outlines a series of steps aimed at selecting the most appropriate power parameter estimate. This selection is achieved using the conventional Maximum Likelihood Estimation method in combination with various criteria for enhancing statistical modeling efficiency. Supplementary decision rules include assessing Mean Square Error, Mean Absolute Error, and conducting a p-value test for the normality of errors. The algorithm's effectiveness is demonstrated through its application to real-world data. Ultimately, the article affirms the feasibility of obtaining viable solutions for selecting the optimal power parameter.

This article introduces an algorithm designed for utilizing power transformations in the estimation of nonlinear regression models. The algorithm outlines a series of steps for selecting the most suitable power parameter estimate through a combination of the conventional Maximum Likelihood Estimation technique and specific criteria for enhancing statistical modeling effectiveness. Supplementary decision guidelines involve the utilization of the determination coefficient and the p-value from the errors normality test. The algorithm's application was demonstrated using actual data. The article's conclusion highlighted the ability to identify a range of feasible solutions for selecting the optimal power parameter. However, it was acknowledging the challenge of identifying a single optimal value that satisfies the requirements of all estimation and decision methodologies.

In this article, a simulated study is introduced, focusing on the use of power transformation to estimate a nonlinear regression model in the presence of residuals following an exponential distribution. Four criteria were employed to estimate the power parameter: the p-value of Shapiro-Wilk test statistics for both the transformed and back-transformed data's normality, maximum likelihood estimation, and coefficient of determination. The findings of the study indicate that while it is possible to identify a range of viable solutions to select the optimal power parameter, finding a single optimal value that satisfies all estimation and decision methods is not feasible.

Abstract This study assesses the new approach of the Box-Cox Transformation to estimate power parameters using five criteria: the traditional Maximum Likelihood Estimation; coefficient of determination; p-value of Shapiro-Wilk test statistics for the residual’s normality of the estimated linear regression of the transformed response vector; pvalue related to residual’s normality; and the Mean Square Errors of the estimated nonlinear regression of the original response vector. The efficiency of these criteria is studied to determine the optimal transformation parameter in the presence of an outlier within a response variable in simple linear regression. The computational algorithm has been developed and applied to medical data. The authors concluded that it is difficult to obtain a feasible solution for all criteria from which an optimal power parameter can be selected. Therefore, the researcher's experience can be considered a decisive factor in choosing according to the priorities of the comparison between the criteria.

In this paper, we perform a transformation-based statistical analysis with an eye to designing a robust and efficient localisation scheme. To this end, we evaluate the coefficient of determination (COD) also denoted as R2 on simulated electromagnetic wave propagation models in an urban environment. Our transformation-based statistical models show that two measurable network parameters, namely the received power (RP) and the time of arrival (ToA), present a strong correlation with a mobile user's given location. By transforming the network parameters we were able to achieve COD of 0.577 for the RP and 0.549 for ToA using the Modulus transformation. We believe that by exploiting the high correlation of parameter transformation, there is potential to design fast and robust machine learning localisation schemes through which the future location of a mobile user can be accurately and reliably predicted.

Institutional internationalization arose as a globalized cross-cultural organizational practice in all social sectors. Popularity of the concept in the higher education sector urged authors to present numerous descriptions of internationalization approaches, which differed according to the philosophical references, circumstances of applying in the academic environments, and means–ends analysis. In the Third World countries, universities used to confront enormous challenges hindering internationalization ambitions in the context of structural and functional obstacles facing development in general. Perhaps it can be said that their internationalization is almost impossible in light of the complex socio- economic environments in those countries, political crises, obsolete bureaucracy, institutional inertia, and restricted academic freedoms. In any case, universities around the world are currently dealing with internationalization as an undebatable issue and must be working on it as a competitive requirement and substantive standard for quality assurance in higher education. In this article, the authors argue the success potential of an organizational transformation-based strategy to internationalization established on institutional adaptation and capacity building. This strategy has been adopted as a transitional stage between localization and internationalization in Nawroz University (NZU) which is presented here as a case study. NZU, as one of the private universities that following the local academic administration system in Kurdistan region of Iraq, has been faced complications resulted from the responsibility of tracking balance between fostering international values and policies, on the one hand, as well as maintaining institutional stability and human resources' positive response to the change, on the other hand. The article discusses how this university dealt with the idea of harmonization as a result of awareness of the difficulties implied in the task of internationalization. Therefore, it adopted what it had considered as a consistent, sustainable, and gradual institutional transformation during the period (2016-2019). The article also highlights plans taken up to support skill up-gradation to consolidate implicit transformation without affecting everyday workflow. In addition, it refers to some simulated international approaches and rankings standards which were used as a guide to direct the transformational process. Finally, the article illustrates some preferable outcomes that the university has been achieved.

The Dirac delta function δ(x)is one of the most important mathematical functions that is used successfully in physics and mathematics. Several authors have used this function to derive distributions of functions of random variables. This article consists of two main axes, namely, the use of change-of-variables technique and δ(x) in deriving the ratio distribution of the two variables of bivariate Beta-Gamma distribution. The aim is to prove that δ(x) is a successful alternative to the change-of-variables technique especially for complex density functions. An appropriate mathematical body of δ(x) is proposed to achieve an adequate representation of the transformation task required for the target distribution. It has been proven that the two methods lead to the same result, which is that the ratio of the two variables follows a beta distribution and that δ(x)is an easy and straightforward alternative to the traditional method.

The aim of this paper is to select an appropriate ARIMA model for the time series after transforming the original responses. Box-Cox and Yeo-Johnson power transformation models were used on the response variables of two time series datasets of average temperatures and then diagnosed and built the appropriate ARIMA models for each time-series. The authors treat the results of the model fitting as a package in an attempt to decide and choose the best model by diagnosing the effect of the data transformation on the response normality, significant of estimated model parameters, forecastability and the behavior of the residuals. The authors conclude that the Yeo-Johnson model was more flexible in smoothing the data and contributed to accessing a simple model with good forecastability.

Response Surface Methodology (RSM) has succeeded in different scientific areas, such as engineering, pharmaceutics, agriculture, and living and chemical experiments, where linear or quadratic models describe one or more explanatory variables that influence the response variable. When linear and quadratic models fail to represent data using RSM adequately, an alternative technique must be used, which involves choosing the appropriate transformations applied to either the response variable or the explanatory variables. A Tukey transformation and Box-Cox method are applied to the response variable in this article to improve the model's adequacy. A previously performed biological experiment is presented, and RSM is applied with power transformations without iterating the experiment. A parameter in the transformed response surface models is also estimated using the maximum likelihood and Draper and Smith methods.

In this article, Box-Cox and Yeo-Johnson transformation models are applied to two time series datasets of monthly temperature averages to improve the forecast ability. An application algorithm was proposed to transform the positive original responses using the first model and the stationary responses using the second model to improve the nonparametric estimation of the functional time series. The Box-Cox model contributed to improving the results of the nonparametric estimation of the original data, but the results become somewhat confusing after attempting to make the transformed response variable stationary in the mean, while the functional time series predictions were more accurate using the transformed stationary datasets using the Yeo- Johnson model.

High-dimensionality is one of the major problems which affect the quality of the classification and prediction modeling. Support vector regression has been applied in several real problems. However, it is usually needed to tune manually the hyperparameters.In addition, SVR cannot perform feature selection. Nature-inspired algorithms have been used as a feature selection and as hyperparameters estimation procedure. In this paper, an improving grasshopper optimization algorithm (GOA) is proposed by adapting a new function of the main controlling parameter of GOA to enhance the exploration and exploitation capability of GOA. This improving is utilized to optimize the hyperparameters of the SVR with embedding the feature selection simultaneously. Experimental results, obtained by running on four datasets, show that our proposed algorithm performs better than cross-validation method, in terms of prediction, number of selected features, and running time. Besides, the experimental results of the proposed improving confirm the efficiency of the proposed algorithm in improving the prediction performance and computational time compared to other nature-inspired algorithms, which proves the ability of GOA in searching for the best hyperparameters values and selecting the most informative features for prediction tasks.

The article proposed a methodology of applying density function to improve the multivariate time series forecasting. Nadaraya-Watson estimator or so-called k-nearest neighbor (KNN) was used in two types of semi-metrics. The methodology was applied to two time series data examples with multiple responses. It has been proven that the applying multivariate nonparametric time series more efficiency than the applying of the univariate nonparametric analysis to each response separately. The mean square errors of predicted values have been estimated to evaluate the performance of the analysis.

For the last ten years, Webometrics Ranking WR has become one of the most reliable and firmly established academic ranking systems in the world. In accordance with the international direction, like any emerging higher education institution looking forward to building eligibility and competence, Nawroz University NZU has perceived the vital need of extracting and tracking indicators with modern dimensions in order to evaluate performance effectively. In addition, the university could benefit from these indicators in charting the features of its scientific and academic plans. Therefore, since the end of 2016, the university has started reviewing and developing plans and programs to reshape its institutional environment according to international standards. Consequently, WR indicators have been used as one of the main resources for reviewing and assessing achievement. This article is an approach to study and analyse the university position of WR during the period from January 2016 to January 2020. Also, it aims to measure the growth rate and the level of cumulative growth in university ranks during the study period. Moreover, the study tries to clarify the role of Scientific Affairs Sector Plan SASP for the period 2017-2019 and NZU's retrofitting steps to improve the University’s ranking in WR at a growth rate of 4% for each six months. In conclusion, the article has found that this rate decreased in the last edition, which requires the university to develop its plans for the purpose of achieving sustainable growth.

n this paper, we propose new Bayesian hierarchical representations of lasso, adaptive lasso and elastic net quantile regression models. We explore these representations by observing that the lasso penalty function corresponds to a scale mixture of truncated normal distribution (with exponential mixing densities). We consider fully Bayesian treatments that lead to new Gibbs sampler methods with tractable full conditional posteriors. The new methods are then illustrated with both simulated and real data. Results show that the new methods perform very well under a variety of simulations, such as the presence of a moderately large number of predictors, collinearity and heterogeneity.

Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter, μ, is added in the original transfer function as a time-varying variable. The TVBGSA-based model was internally and externally validated based on Qint2, QLGO2, QBoot2, MSEtrain, Qext2, MSEtest, Y-randomization test, and applicability domain evaluation. The validation results indicate that the proposed TVBGSA model is robust and not due to chance correlation. The descriptor selection and prediction performance of TVBGSA outperform BGSA method. TVBGSA shows higher Qint2 of 0.957, QLGO2 of 0.951, QBoot2 of 0.954, Qext2 of 0.938, and lower MSEtrain and MSEtest compared to obtained results by BGSA, indicating the best prediction performance of the proposed TVBGSA model. The results clearly reveal that the proposed TVBGSA method is useful for constructing reliable and robust QSARs for predicting antidiabetic activity of DPP-IV inhibitors prior to designing and experimental synthesizing of new DPP-IV inhibitors.

An improved binary differential search (improved BDS) algorithm is proposed for QSAR classification of diverse series of antimicrobial compounds against Candida albicans inhibitors. The transfer functions is the most important component of the BDS algorithm, and converts continuous values of the donor into discrete values. In this paper, the eight types of transfer functions are investigated to verify their efficiency in improving BDS algorithm performance in QSAR classification. The performance was evaluated using three metrics: classification accuracy (CA), geometric mean of sensitivity and specificity (G-mean), and area under the curve. The Kruskal–Wallis test was also applied to show the statistical differences between the functions. Two functions, S1 and V4, show the best classification achievement, with a slightly better performance of V4 than S1. The V4 function takes the lowest iterations and selects the fewest descriptors. In addition, the V4 function yields the best CA and G-mean of 98.07% and 0.977%, respectively. The results prove that the V4 transfer function significantly improves the performance of the original BDS.

Lasso regression, a special case of Bridge regression of a penalty function ∑∣∣βj∣∣q with q = 1, is considered from a Bayesian perspective. Park and Casella (2008) introduced the Bayesian lasso regression, using a conditional Laplace prior distribution represented as a scale mixture of normals with an exponential mixing distribution. Recently, Mallick and Yi (2014) provided a new version of Bayesian lasso regression approach by using a scale mixture of uniform representation of the Laplace distribution with a particular gamma mixing density. In this paper, we propose a new Bayesian lasso regression method by using a scale mixture of truncated normal representation of the Laplace density with exponential mixing densities. The method is illustrated via simulation examples and two real data sets. Results show that the proposed method performs very well. An extension to general models is also discussed.

A Bayesian single-index quantile estimation approach for ordinal data is proposed. A simple and efficient MCMC algorithm was developed for posterior computation using a normal-exponential mixture representation of the skewed Laplace distribution. The proposed method is then demonstrated via simulated studies and two real data sets. Results show that the proposed method performs well under the simulated studies and real data analysis.

Classical adaptive lasso regression is known to possess the oracle properties; namely, it performs as well as if the correct submodel were known in advance. However, it requires consistent initial estimates of the regression coefficients, which are generally not available in high dimensional settings. In addition, none of the algorithms used to obtain the adaptive lasso estimators provide a valid measure of standard error. To overcome these drawbacks, some Bayesian approaches have been proposed to obtain the adaptive lasso and related estimators. In this paper, we consider a fully Bayesian treatment for the adaptive lasso that leads to a new Gibbs sampler with tractable full conditional posteriors. Through simulations and real data analyses, we compare the performance of the new Gibbs sampler with some of the existing Bayesian and non-Bayesian methods. Results show that the new approach performs well in comparison to the existing Bayesian and non-Bayesian approaches.

Quantitative structure–activity relationship (QSAR) classification modelling with descriptor selection has become increasingly important because of the existence of large datasets in terms of either the number of compounds or the number of descriptors. Descriptor selection can improve the accuracy of QSAR classification studies and reduce their computation complexity by removing the irrelevant and redundant descriptors. In this paper, a two-stage classification approach is proposed by combining the minimum redundancy maximum relevancy criterion with the sparse support vector machine. The experimental results of classifying the neuraminidase inhibitors of influenza A (H1N1) viruses show that the proposed method is able to effectively outperform other sparse alternatives methods in terms of classification performance and the number of selected descriptors.

Gene selection has been proven to be an effective way to improve the results of many classification methods. However, existing gene selection techniques in binary classification regression are sensitive to outliers of the data, heteroskedasticity or other anomalies of the latent response. In this paper, we propose a new Bayesian hierarchical model to overcome these problems in a relatively straightforward way. In particular, we propose a new Bayesian Lasso method that employs a skewed Laplace distribution for the errors and a scaled mixture of uniform distribution for the regression parameters, together with Bayesian MCMC estimation. Comprehensive comparisons between our proposed gene selection method and other competitor methods are performed experimentally, depending on four benchmark gene expression datasets. The experimental results prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.

Tobit quantile regression (QReg) model provides an efficient way of coping with left-censored data and can be viewed as a linear QReg model, where only the data on the dependent variable is incompletely observed. This article considers the regularizer in tobit QReg from a Bayesian perspective. By proposing a hierarchical model framework, we consider a fully Bayesian treatment that leads to a Gibbs sampling algorithm with fully tractable conditional posterior densities. The proposed method is illustrated via simulation studies and a real dataset. Results show that the proposed method performs very well in comparison to other approaches.

Since the pioneering work by Koenker and Bassett [27], quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location-scale mixture representation of the skewed double-exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.

A Bayesian elastic net approach is presented for variable selection and coefficient estimation in linear regression models. A simple Gibbs sampling algorithm was developed for posterior inference using a location-scale mixture representation of the Bayesian elastic net prior for the regression coefficients. The penalty parameters are chosen through an empirical method that maximizes the data marginal likelihood. Both simulated and real data examples show that the proposed method performs well in comparison to the other approaches.

In this paper a method has been suggested to describe the conditional expectation of Bayesian probability distribution (Gamma-Gamma) by nonlinear regression model and using power transformation for the observations of the predictor variables in the observable distribution to get the best possible fitting to the model of the posterior conditional expectation. The parameters of the described model have been estimated by depending on experimental data which has been generated using different values for the parameters of conditional probability distribution. The best estimation of the power parameter of the described model was found by using Draper & Smith method which gave best fitting of the suggested model and best estimate for the conditional expectation of the Bayesian Probability Distribution (Gamma–Gamma).

This research dealt with studying whatever is possible to get from data concerning the life of people in Iraq, weather economic, social, educational, and hygienic and the level of the required infrastructure for a better life. The research hypothesizes that there are huge differences in the life standards quality between Iraqi governorates. Consequently, this research asses the significance of these differences and to limit what ever is possible to arrange the governorates according to the efficiency of life quality. The researcher used the quantitative style in investigating the research hypotheses. The research concludes with dividing the governorates to three main groups which are governorates of the better sort of life, governorates of the medium sort of life and the governorates of the poorest quality of life.

The aim of the current research is to build a model for consumption function, which corresponds with the economic theory through a series of statistical treatment to smooth data which do not contrast with the statistical theory. The research hypothesis uses dummy variables and power transformation satisfies good fitting for the estimation a set of consumption functions by using ordinary least squares method, besides the data about national consumption, national income, general consumption and general expenditure for the period between 1970-1995 by current and fixed prices in Iraq. The best conclusions, by using dummy variable has succeeded to review again and explain independent variables, but the use of dummy variables and power transformation has achieved a better fitting.

In this research, we consider the study of conditional expectation and it's relationship with regression model. The conditional expectation has a linear form which is specified as a simple linear regression model. The power transformation was used on the predictor variable which gave the best possible fit for the model which was derived from the binomial distribution conditioned with varying sample size. The parameters of specified models were estimated by depending on empirical data which were simulated with different values for the parameter of conditional probability distribution. The best estimator for the power parameter was found in two specified models by the maximum likelihood and Draper & Smith methods. These estimators gave the best fit to the suggested model and best estimator to the conditional expectations of conditional probability distribution and it was concluded that the suggested method was better than the ordinary method. The increments in the probability of success p had a great effect on the best fitted model also the estimated conditional expectation of conditional binomial distribution was affected. This result was clear because of decreasing the coefficient of determination R2 in Draper & Smith and the mean square of residuals in maximum likelihood method with increase in p.

The study suggested a new procedure, depending on which is to estimate the power transformation parameter of the independent variable in the logistic regression model using the error index in kernel estimate method that is called (Mean Integrated Square Error). The application is done by three biological experiment and comparing proposal procedure with MLE method as a common method.