This research sought to present a concept of cross-sectional data models,  A crucial double data to take the impact of the change in time and obtained from the measured phenomenon of repeated observations in different time periods, Where the models of the panel  data were defined by different types of fixed , random and mixed, and Comparing them by studying and analyzing the mathematical relationship between the influence of time with a set of basic variables Which are the main axes on which the research is based and is represented by the monthly revenue of the working individual and the profits it generates, which represents the variable response And its relationship to a set of explanatory variables represented by the

Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using BoxJenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2)

 Accuracy in multiple objects segmentation using geometric deformable models sometimes is not achieved for reasons relating to a number of parameters. In this research, we will study the effect of changing the parameters values on the work of the geometric deformable model and define their efficient values, as well as finding out the relations that link these parameters with each other, by depending on different case studies including multiple objects different in spacing, colors, and illumination. For specific ranges of parameters values the segmentation results are found good, where the success of the work of geometric deformable models has been limited within certain limits to the values of these parameters.

Abstract

Due to the lack of previous statistical study of the behavior of payments, specifically health insurance, which represents the largest proportion of payments in the general insurance companies in Iraq, this study was selected and applied in the Iraqi insurance company.

In order to find the convenient model representing the health insurance payments, we initially detected two probability models by using (Easy Fit) software:

First, a single Lognormal for the whole sample and the other is a Compound Weibull  for the two Sub samples (small payments and large payments), and we focused on the compoun

Many of the dynamic processes in different sciences are described by models of differential equations. These models explain the change in the behavior of the studied process over time by linking the behavior of the process under study with its derivatives. These models often contain constant and time-varying parameters that vary according to the nature of the process under study in this We will estimate the constant and time-varying parameters in a sequential method in several stages. In the first stage, the state variables and their derivatives are estimated in the method of penalized splines(p- splines) . In the second stage we use pseudo lest square to estimate constant parameters, For the third stage, the rem

Massive multiple-input multiple-output (massive-MIMO) is considered as the key technology to meet the huge demands of data rates in the future wireless communications networks. However, for massive-MIMO systems to realize their maximum potential gain, sufficiently accurate downlink (DL) channel state information (CSI) with low overhead to meet the short coherence time (CT) is required. Therefore, this article aims to overcome the technical challenge of DL CSI estimation in a frequency-division-duplex (FDD) massive-MIMO with short CT considering five different physical correlation models. To this end, the statistical structure of the massive-MIMO channel, which is captured by the physical correlation is exploited to find sufficiently

Abstract

The Non - Homogeneous Poisson  process is considered  as one of the statistical subjects which had an importance in other sciences and a large application in different areas as waiting raws and rectifiable systems method , computer and communication systems and the theory of reliability and many other, also it used in modeling the phenomenon that occurred by unfixed way over time (all events that changed by time).

This research deals with some of the basic concepts that are related to the Non - Homogeneous Poisson process , This research carried out two models of the Non - Homogeneous Poisson process which are the power law model , and Musa –okumto ,   to estimate th