The response of a treatment (direct effect) applied on a given unit may be affected by the treatments applied to its neighboring units (neighbor effects). Neighbor designs are considered robust to neighbor effects. Minimal neighbor designs are economical, therefore, these are preferred by the experimenters. Method of cyclic shifts (Rule I) provides the minimal neighbor designs for odd v (number of treatments). Method of cyclic shifts (Rule II) provides the minimal circular Quasi Rees neighbor designs for v even which are considered to be the good alternate to the minimal neighbor designs. In this article, for every case of v even, minimal circular Quasi Rees neighbor designs are constructed in such a way that these designs can also be converted directly into minimal circular balanced and strongly balanced neighbor designs.

In this article, four new families named as Weibull extended exponentiated-X (WEE-X), Lomax extended exponentiated-X (LEE-X), Logistic extended exponentiated-X (LGCEE-X), and Burr III extended exponentiated-X (BIIIEE-X) with their quantile functions are proposed. The expressions for distribution function and density function of BIIIEE-X family are written in terms of linear combinations of the exponentiated densities based to parent model. New models, i.e., Weibul extended exponentiated Weibull (WEEW), Lomax extended exponentiated Weibull (LEEW), Logistic extended exponentiated Weibull (LGCEEW), and Burr III extended exponentiated-Weibull (BIIIEEW) distributions are derived, were plotted for functions of probability density and hazard rate at different levels of parameters. Some mathematical properties of the BIIIEEW model are disclosed. The maximum likelihood method for the BIIIEEW model are described. Numerical applications of the BIIIEEW model to disease of cancer datasets are provided.

Much of classical optimal design theory relies on specifying a model with only a small number of parameters. In many applications, such models will give reasonable approximations. However, they will often be found not to be entirely correct when enough data are at hand. A property of classical optimal design methodology is that the amount of data does not influence the design when a fixed model is used. However, it is reasonable that a low dimensional model is satisfactory only if limited data is available. With more data available, more aspects of the underlying relationship can be assessed. We consider a simple model that is not thought to be fully correct. The model misspecification, that is, the difference between the true mean and the simple model, is explicitly modeled with a stochastic process. This gives a unified approach to handle situations with both limited and rich data. Our objective is to estimate the combined model, which is the sum of the simple model and the assumed misspecification process. In our situation, the low-dimensional model can be viewed as a fixed effect and the misspecification term as a random effect in a mixed-effects model. Our aim is to predict within this model. We describe how we minimize the prediction error using an optimal design. We compute optimal designs for the full model in different cases. The results confirm that the optimal design depends strongly on the sample size. In low-information situations, traditional optimal designs for models with a small number of parameters are sufficient, while the inclusion of the misspecification term lead to very different designs in data-rich cases. 

The study of hydrological characteristics has a vital role in designing, planning, and managing water resources. The selection of appropriate probability distributions and methods of estimations are basic elements in hydrology analyses. In this article, a new family named the ‘exponentiated power alpha index generalized’ (EPAIG)-G is proposed to develop several new distributions. Using this proposed family, we developed a new model, called the EPAIG-exponential (EPAIG-E). A few structural properties of the EPAIG-G were obtained. The EPAIG-E parameters were estimated through the method of maximum likelihood (MML). The study of the Monte Carlo simulation (MCS) was produced for the EPAIG-E. The model performance is illustrated using real data.

The experiments where response of a treatment (direct effect) is affected by the treatment(s) applied in neighboring units, neighbor designs are used to balance the neighbor effects. Being the economical, minimal neighbor designs are preferred by the experimenters. Minimal circular neighbor designs could not be constructed for almost every case of v even, where v is number of treatments. For v even, minimal circular generalized neighbor designs are preferred. In this article, algorithms are developed to obtain minimal circular generalized neighbor designs in which (a) v/2 of the unordered pairs, and (b) 3v/2 of the unordered pairs, do not appear as neighbor whereas the remaining ones appear once. These algorithms are also coded with R-language. 

Achievement tests are used to characterize the proficiency of higher-education students. Item response theory (IRT) models are applied to these tests to estimate the ability of students (as latent variable in the model). In order for quality IRT parameters to be estimated, especially ability parameters, it is important that the appropriate number of dimensions is identified. Through a case study, based on a statistics exam for students in higher education, we show how dimensions and other model parameters can be chosen in a real situation. Our model choice is based both on empirical and on background knowledge of the test. We show that dimensionality influences the estimates of the item-parameters, especially the discrimination parameter which provides information about the quality of the item. We perform a simulation study to generalize our conclusions. Both the simulation study and the case study show that multidimensional models have the advantage to better discriminate between examinees. We conclude from the simulation study that it is safer to use a multidimensional model compared to a unidimensional if it is unknown which model is the correct one.

Balanced or strongly balanced repeated measurements designs (RMDs) are used to balance out residual effects Minimal strongly balanced RMD (SBRMDs) are important to estimate the direct effects and residual effects independently at low cost but there are many situations where SBRMDs cannot be constructed. In such situations, strongly partially balanced RMDs (SPBRMDs) are preferred. In literature, these designs are not available for odd p(1) (larger period size). In this paper, minimal circular SPBRMDs are constructed in periods of two different sizes for almost every case of p(1) (odd).

Residual effects in repeated measurements design (RMDs) leads to wrong estimation of direct treatment effects. Minimal strongly balanced RMDs are preferred to balance out the residual effects. The partially strongly balanced designs form an important family of RMDs which provide designs where minimal strongly balanced RMDs do not exist. In this article, a general construction of efficieint circular partially strongly-balanced RMDs is given in periods of k different sizes which produces these designs in periods of equal sizes, two different sizes, three different sizes, …, by putting k = 1, 2, 3, …, respectively. 

This study determined the influence of renewable energy and natural resources on carbon emissions by paying special attention to the transboundary trade relationship between South East Asian (SEA) economies. To highlight the importance of international trade relationships among the SEA economies, an interaction term is introduced. Moreover, to estimate the results, panel data from 1996 to 2019 is analyzed by applying Cross-Sectional Augmented Autoregressive Distributed Lag (CS-ARDL). The results show that renewable energy consumption is a significant factor that can reduce carbon emissions. The employment of interaction term shows that international trade improves the influence of renewable energy to control carbon emissions. The findings also depict that natural resources consumption is stimulating carbon emissions. While a strong trade bond is helping to reduce the influence of natural resources consumption on carbon emissions. The findings of this study highlight the importance of international trade at the regional level to mitigate carbon emissions. The study suggests that improvement in international trade may prove a helpful strategy to promote renewable energy sources and diminish the reliance on natural resources such as fossil fuels, this will ultimately help to mitigate carbon emissions.

Agricultural production is greatly influenced by environmental parameters such as temperature, rainfall, humidity, and wind speed. The accurate information about environmental parameters plays a vital and useful role when making policies for the agriculture sector as well as for other sectors. Pakistan meteorological department observed these environmental parameters at more than 90 stations. The allocation of these monitoring stations is not made systematically correct. This leads to inaccurate predictions for unobserved locations. The study aims to propose a monitoring network by which these prediction errors of the environmental parameters can be minimized. The well-known prediction techniques named, model-based ordinary kriging and model-based universal kriging (UK) with the known Matheron variogram model are used for prediction purposes. We investigate the monitoring network of Pakistan for rainfall and focus on both the optimal deletion/addition of monitoring stations from/to this network. The two stochastic search algorithms, spatial simulated annealing, and genetic algorithm are used for optimization purposes. Furthermore, the minimization of the Average Kriging Variance (AKV) is taken as the interpolation accuracy measure. The spatial simulated annealing exhibits a lower AKV as compared to the Genetic algorithm when adding/removing the optimal/redundant locations from the monitoring network.

Here, a new method is recommended to characterize a new continuous distribution class, named the generalized alpha exponent power family of distributions (GAEPFDs). A particular sub-model is presented for exemplifying the objective. The basic statistical properties, such as ordinary moments, the probability weighted moments, mode, quantile, order statistics, entropy measures, and moment generating functions, etc., were explored. To gauge the GAEPPRD parameters, the ML technique was utilized. The estimator behaviour was studied by a Monte Carlo simulation study (MCSS). The effectiveness of GAEPFDs was demonstrated observationally through lifetime data. The applications show that GAEPFDs can offer preferable results over other competitive models.

Here, we propose a new generalized exponential extended exponentiated (NGE3) family of distributions. Some statistical properties of proposed family are gained. The most extreme probability method, maximum likelihood (ML), is utilized for parameter estimation. We explore an exceptional model called NGE3-Exponential (NGE3E). NGE3E is estimated with ML, and the performance of estimators is demonstrated by utilizing a simulation. Moreover, two applications are given to show the significance and adaptability of the proposed model in comparison to some generalized models (GMs).

Workers exposed to gender harassment and illegitimate tasks may experience adverse mental health outcomessuch as depression and burnout. However, the longitudinal effects and the complex interrelationships betweenthese variables remain largely unexplored. We investigated the cross-lagged relationships between genderharassment, illegitimate tasks, and mental health outcomes among working adults in Sweden over a period oftwo years, as well as the gender differences in the cross-lagged effects. Additionally, the study examined whetherillegitimate tasks mediated the relationship between gender harassment and negative mental health outcomesover time. Data were drawn from the Swedish Longitudinal Occupational Survey of Health (SLOSH), covering2796 working men and 4110 working women in a two-wave analysis from 2018 to 2020. We employed astructural equation model to examine the cross-lagged effects and the mediating effect between genderharassment, illegitimate tasks, and mental health outcomes over time. Furthermore, we applied a multigroupanalysis to determine gender differences in the cross-lagged effects.The results showed statistically significant cross-lagged relationships (forward, reverse, and reciprocal) be-tween gender harassment, illegitimate tasks, and mental ill-health. There were statistically significant genderdifferences in these cross-lagged relationships (burnout: △χ2 (47) = 106.21, p < 0.01; depression: △χ2 (47) =80.5, p < 0.01). Initial illegitimate tasks mediated the relationship between gender harassment and mental ill-health outcomes over time. The gender differences in the interrelationships between gender harassment, ille-gitimate tasks, and mental ill-health outcomes among workers in Sweden indicate that policies, regulations, andinterventions that address these exposures in organisations must be tailored to benefit both men and women.

The importance of large scale achievement tests, like national tests in school, eligibility tests for university, or international assessments for evaluation of students, is increasing. Pretesting of questions for the above mentioned tests is done to determine characteristic properties of the questions by adding them to an ordinary achievement test. If computerized tests are used, it has been shown using optimal experimental design methods that it is efficient to assign pretest questions to examinees based on their abilities. The specific distribution of abilities of the available examinees are considered and restricted optimal designs are applied. A new algorithm is developed which builds on an equivalence theorem. It discretizes the design space with the possibility to change the grid adaptively during the run, makes use of an exchange idea and filters computed designs. It is illustrated how the algorithm works through some examples as well as how convergence can be checked. The new algorithm is flexible and can be used even if different models are assumed for different questions.

In recent years, the trigonometric families of continuous distributions have found a place of choice in the theory and practice of statistics, with the Sin-G family as leader. In this paper, we provide some contributions to the subject by introducing a flexible extension of the Sin-G family, called the transformed Sin-G family. It is constructed from a new polynomial-trigonometric function presenting a desirable “versatile concave/convex” property, among others. The modelling possibilities of the former Sin-G family are thus multiplied. This potential is also highlighted by a complete theoretical work, showing stochastic ordering results, studying the analytical properties of the main functions, deriving several kinds of moments, and discussing the reliability parameter as well. Then, the applied side of the proposed family is investigated, with numerical results and applications on the related models. In particular, the estimation of the unknown model parameters is performed through the use of the maximum likelihood method. Then, two real life data sets are analyzed by a new extended Weibull model derived to the considered trigonometric mechanism. We show that it performs the best among seven comparable models, illustrating the importance of the findings.

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data. 

Repeated measurements designs (RMDs) are very useful and economical but unfortunately, with the use of RMDs, a major source of bias is arisen, that is the carry over effect. Minimal designs which are strongly and nearly strongly balanced, are preferred to estimate the direct and carry over effects independently. In this article, some new classes of minimal circular nearly strongly balanced RMDs are constructed in periods of two and three different sizes which can be converted directly into minimal circular balanced and minimal strongly balanced which are highly efficient.

In recent years, the interest in measuring growth in student ability in various subjects between different grades in school has increased. Therefore, good precision in the estimated growth is of importance. This paper aims to compare estimation methods and test designs when it comes to precision and bias of the estimated growth of mean ability between two groups of students that differ substantially. This is performed by a simulation study. One- and two-parameter item response models are assumed and the estimated abilities are vertically scaled using the non-equivalent anchor test design by estimating the abilities in one single run, so-called concurrent calibration. The connection between the test design and the Fisher information is also discussed. The results indicate that the expected a posteriori estimation method is preferred when estimating differences in mean ability between groups. Results also indicate that a test design with common items of medium difficulty leads to better precision, which coincides with previous results from horizontal equating.

Dose-finding is an important part of the clinical development of a new drug. The purpose of dose-finding studies is to determine a suitable dose for future development based on both efficacy and safety. Optimal experimental designs have already been used to determine the design of this kind of studies, however, often that design is focused on efficacy only. We consider an efficacy-safety model, which is a simplified version of the bivariate Emax model. We use here the clinical utility index concept, which provides the desirable balance between efficacy and safety. By maximizing the utility of the patients, we get the estimated dose. This desire leads us to locally c-optimal designs. An algebraic solution for c-optimal designs is determined for arbitrary c vectors using a multivariate version of Elfving's method. The solution shows that the expected therapeutic index of the drug is a key quantity determining both the number of doses, the doses itself, and their weights in the optimal design. A sequential design is proposed to solve the complication of parameter dependency, and it is illustrated in a simulation study.