When SPATIAL DATA are realizations of a Gaussian model with parametric mean and covariance functions, then the function of observations that minimizes mean square prediction error depends on some unknown parameters. Usually, these parameters are replaced by their estimates to obtain the plug-in predictor. But, this method has some problems in estimation of the parameters and the optimality and mean square error of the SPATIAL predictor. In this paper, the problems related to plug-in method are discussed and to avoid them, the Bayesian approach for SPATIAL prediction is proposed. Then the Bayesian SPATIAL prediction for Gaussian and trans Gaussian models according to observations, that may contain noise, are derived. Next, in a simulation study, the adequacy of Bayesian prediction is compared with plug-in prediction. Finally, a numerical example illustrates the Bayesian SPATIAL prediction of rainfall in a region at the north of Iran.

The neoclassical growth model is tested by use of panel DATA procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel DATA method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

Many survival DATA analyses aim to assess the effect of different risk factors on survival time‎. ‎In some studies‎, ‎the survival times are correlated‎, ‎and the dependence between survival times is related to their SPATIAL locations‎. ‎Identifying and considering the dependence structure of DATA is essential in survival modeling‎. ‎The copula functions are helpful tools for incorporating DATA dependencies‎. ‎So‎, ‎one may use these functions for modelling SPATIAL survival DATA‎. ‎This paper presents a model for SPATIAL survival DATA by the Gumbel-Hougaard copula function‎. ‎A two-stage estimator using a composite likelihood function is used to estimate regression and dependence parameters‎. ‎A simulation study investigates the performance of the model‎. ‎Finally‎, ‎the proposed model is applied to model a set of COVID-19 DATA.

SPATIAL DATAsets may contain extreme values and exhibit heavy tails. So, the Gaussianity assumption for the corresponding random field is not reasonable. A sub-Gaussian α-stable (SGαS) random field may be more suitable as a model for heavy-tailed SPATIAL DATA. This paper focuses on geostatistical DATA and presents an algorithm for simulating SGαS random fields.

ICT has been introduced as the dominating technologies in the new millennium. These technologies have been transformed into tools for productivity, efficiency and growth in human communities through expediting the process of information exchange and they have also provided an easy access to the results of mass accumulation of prodused DATA. This research which has been conducted using systematic approach and with a descriptive-analytical method and in library manner, as well, is descriptive because information was gathered through information search and it is analytical because after the required information was obtained, it was recapitulated, classified and analyzed and assessed, too. The statistical society included the present local and foreign sources ranging from books, laws and regulations, theses, quarterlies, scientific articles, experts’ views, electronic sources and related websiste, as well. Methods and tools of information collection have been via documentary information note-taking and library studies, as well and the obtained concepts were systematized and analyzed. Cohesion and sustainability of the research are verified in regards to the provided documents and references. In case police is provided with the standard SPATIAL DATA infrastructures and transformation of CID present DATA bank (or other specialized police forces) and other agencies, as well, police can then obtain updated crime and delinquency-related DATA at the fastest possible time and take actions to identify and arrests criminals.