Theresearch took the spatial autoregressive model: SAR and spatial error model: SEM in an attempt to provide a practical evident that proves the importance of spatial analysis, with a particular focus on the importance of using regression models spatial andthat includes all of them spatial dependence, which we can test its presence or not by using Moran test. While ignoring this dependency may lead to the loss of important information about the phenomenon under research is reflected in the end on the strength of the statistical estimation power, as these models are the link between the usual regression models with time-series models. Spatial analysis had been applied on IraqHousehold Socio-Economic Survey: IHSES 2012. To measure the preference models used in the research was the use of such standards compared: Root Mean Squares Error: RMSE,Mean Absolute Percentage Error: MAPEand , and Adjusted determinant coefficient: with different weight matrices (binary and modified) take into account the effect of neighborhoods of districts.
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This research deals with the risks of non-compliance and its impact on the profitability of Islamic banks. Research variables were measured and analyzed as the risk of non-compliance as an independent variableand profitability as a dependent variable. The profitability was measured by three indicators ((rate of return on assets, rate of return on equity and rate of return on Total deposits)) The results of the research showed a significant relationship between the risk of non-compliance and the rate of return on assets and rate of return on total deposits, while there was no relationship between the risk of non-compliance and rate of return on ownership. The research recommended that the senior management of the Islamic Investment Bank s
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Cox regression model have been used to estimate proportion hazard model for patients with hepatitis disease recorded in Gastrointestinal and Hepatic diseases Hospital in Iraq for (2002 -2005). Data consists of (age, gender, survival time terminal stat). A Kaplan-Meier method has been applied to estimate survival function and hazerd function.
Breast cancer has got much attention in the recent years as it is a one of the complex diseases that can threaten people lives. It can be determined from the levels of secreted proteins in the blood. In this project, we developed a method of finding a threshold to classify the probability of being affected by it in a population based on the levels of the related proteins in relatively small case-control samples. We applied our method to simulated and real data. The results showed that the method we used was accurate in estimating the probability of being diseased in both simulation and real data. Moreover, we were able to calculate the sensitivity and specificity under the null hypothesis of our research question of being diseased o
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ABSTRACT:
Interest rates are one of the important aspects that affect the banking business directly, which is characterized by unstable dynamic dynamics, which must be viewed on a daily and continuous basis through the macroeconomic view, which directly affects the bank’s income realized from loans as interest received or interest paid on its deposits as an expense. Hence the earnings per share. The relationship between interest rates and between net income and earnings per share was measured and a correlation was found between them, and then the effect between them was measured using regression equations and they were applied and th
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Characterized by the Ordinary Least Squares (OLS) on Maximum Likelihood for the greatest possible way that the exact moments are known , which means that it can be found, while the other method they are unknown, but approximations to their biases correct to 0(n-1) can be obtained by standard methods. In our research expressions for approximations to the biases of the ML estimators (the regression coefficients and scale parameter) for linear (type 1) Extreme Value Regression Model for Largest Values are presented by using the advanced approach depends on finding the first derivative, second and third.
Abstract
In this study, we compare between the autoregressive approximations (Yule-Walker equations, Least Squares , Least Squares ( forward- backword ) and Burg’s (Geometric and Harmonic ) methods, to determine the optimal approximation to the time series generated from the first - order moving Average non-invertible process, and fractionally - integrated noise process, with several values for d (d=0.15,0.25,0.35,0.45) for different sample sizes (small,median,large)for two processes . We depend on figure of merit function which proposed by author Shibata in 1980, to determine the theoretical optimal order according to min
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In general, researchers and statisticians in particular have been usually used non-parametric regression models when the parametric methods failed to fulfillment their aim to analyze the models precisely. In this case the parametic methods are useless so they turn to non-parametric methods for its easiness in programming. Non-parametric methods can also used to assume the parametric regression model for subsequent use. Moreover, as an advantage of using non-parametric methods is to solve the problem of Multi-Colinearity between explanatory variables combined with nonlinear data. This problem can be solved by using kernel ridge regression which depend o
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