Asian Journal of Probability and Statistics

In this paper, measure of slope rotatability for second order response surface designs using pairwise balanced designs under intra-class correlated structure of errors is suggested and illustrated with examples.

The main concept of this research was forecasting a group of variables simultaneously, thus making use of correlations among the variables. This research aims to check forecasting performance among different VAR and ARIMA models applying some economic indicators of Bangladesh. Data sets were collected from secondary sources of Bangladesh such as Bangladesh bank bulletin, Bangladesh economic review, Monthly economic trends of Bangladesh Bank, and Statistical yearbook of Bangladesh. The stationary VAR and ARIMA models were applied for predicting these financial variables and then checked the accuracy by comparing ME, RMSE, MAE, MPE, MAPE, and MASE of respected the variables. This research found that the VAR model presented a better forecast than ARIMA models for the highly correlated variables such as GDP vs. GNP, Export vs. Import, etc. But ARIMA and VAR models performed almost the same for comparatively low correlated variables. That's means the variables were comparatively low correlated couldn't give a better forecast in the multivariate time series model rather than the univariate time series model. Finally, researchers concluded that before forecasting the authority should check correlations among the variables, and for high correlated variables, the VAR model should be used for forecasting, and otherwise, they can consider any models for both of these correlated and uncorrelated variables.

For many years scientists studied the piosphere concept- a grazing gradient around a natural/artificial watering point. As is the case for other kinds of ecological studies, the method of statistical analyses applied in many publications is not always appropriate. We note there are many statistical errors and misapplication of data analysis techniques. We reviewed 875 piosphere-related publications between 1915-2018 to find the common statistical methods and common statistical errors in the design of the study, data analyses, presentation of results, and interpretation of study findings. One-way ANOVA, multiple linear regression, Pearson correlation coefficient, permutational multivariate analysis of variance, canonical correspondence analysis, and mean were the most frequent statistical methods applied. Seventy-one common statistical errors in piosphere publications were found. The most common errors were not choosing the proper or appropriate statistical techniques, not checking the assumptions and diagnostics of statistical methods, partial and wrong interpretation of results, and not using informative figures and tables to help readers. Negligence to the proper application of statistics by researchers results in inaccurate interpretation and spurious conclusions. It is recommended researchers seek advice from statisticians at the early stages of research to save resources, time, and labor and to provide increased trust in recommendations and findings.

This paper introduces and investigates the notions of GN -disconnected sets, GN -connected, GN -precompact sets, and separation axioms via GN -preopen sets. They will be introduced in grill topological spaces by using the class of GN -preopen sets.

An innovative standard scheme was established aimed at developing inferences and interpretations statistically relative to clinical neuroimaging facts and figures. It involves as particular instances, SPMs, a standard methodology to clinical neuroimaging anatomy. Our developed model contributes and provides various educational and statistical benefits which begin from the anatomy of facts at group level before the level of the voxel, commencing by direct modelling of the location and shape of the modules. We set out a new general framework for making inferences from neuroimaging data, which includes a standard approach to neuroimaging analysis, statistical parametric mapping (SPM), as a particular case. The model offers numerous conceptual and statistical advantages that begin from analysis of the collected data at the group level somewhat than the voxel level, from explicit modelling of the shape and position of clusters of activation. It provides a natural and moral way to pool data from nearby voxels for parameter and variance-component estimation. The model can also be viewed as performing Spatio-temporal cluster analysis. The parameters of the model are estimated using an expectation-maximization (EM) algorithm.