In light of the enquiry raised by the Economist Mary Finn in 1995 concluding that high utilization in absorptive capacity of the economy is of inflationary tendency for industrial countries due to the equality between high rates of utilization of absorptive capacity and resource – shortage conditions leading to price inflation, the same idea was used to prove that budget utilization of operational costs and elevating absorptive capacity at the expense of investment budget leads to inflationary tendency that becomes a burden on financing the step- in policy of the Central bank to control prices through its foreign currency reserves at a time when the economy turned into an importer of non- tradable goods and being subject to Balassa-Samuelson effect based on intensifying non- traded goods price increase in industrial countries in coordination with its growth acceleration to be exported to Iraq as an inflationary force increasing the level of economic imbalances depleting the foreign currency needs of the Central Bank through the increase in the cost of financial or monetary step –in policy which is considered a disease of high consumption societies living on rental resources receiving as a result, price shocks from industrial countries due to the transition towards importing non- tradable goods to become tradable goods.
In this paper, we will provide a proposed method to estimate missing values for the Explanatory variables for Non-Parametric Multiple Regression Model and compare it with the Imputation Arithmetic mean Method, The basis of the idea of this method was based on how to employ the causal relationship between the variables in finding an efficient estimate of the missing value, we rely on the use of the Kernel estimate by Nadaraya – Watson Estimator , and on Least Squared Cross Validation (LSCV) to estimate the Bandwidth, and we use the simulation study to compare between the two methods.
The survival analysis is one of the modern methods of analysis that is based on the fact that the dependent variable represents time until the event concerned in the study. There are many survival models that deal with the impact of explanatory factors on the likelihood of survival, including the models proposed by the world, David Cox, one of the most important and common models of survival, where it consists of two functions, one of which is a parametric function that does not depend on the survival time and the other a nonparametric function that depends on times of survival, which the Cox model is defined as a semi parametric model, The set of parametric models that depend on the time-to-event distribution parameters such as
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