Abstract

 The problem of missing data represents a major obstacle before researchers in the process of data analysis in different fields since , this problem is a recurrent one in all fields of study including social , medical , astronomical and clinical experiments .

The presence of such a problem within the data to be studied may influence negatively on the analysis and it may lead to misleading conclusions , together with the fact that these conclusions that result from a great bias caused by that problem in spite of the efficiency of wavelet methods but they are also affected by the missing of data , in addition to the impact of the problem of miss of accuracy estimation

This research takes up address the practical side by taking case studies for construction projects that include the various Iraqi governorates, as it includes conducting a field survey to identify the impact of parametric costs on construction projects and compare them with what was reached during the analysis and the extent of their validity and accuracy, as well as adopting the approach of personal interviews to know the reality of the state of construction projects. The results showed, after comparing field data and its measurement in construction projects for the sectors (public and private), the correlation between the expected and actual cost change was (97.8%), and this means that the data can be adopted in the re

Introduction: The therapeutic efficacy of erythropoietin (EPO) may be diminished by the formation of anti-erythropoietin antibodies (AEAs), potentially resulting in therapy failure. Objectives: This retrospective study included 84 hemodialysis patients with end-stage renal disease (ESRD) treated with recombinant EPO and aimed to evaluate the association between serum AEA and treatment response. Patients and Methods: This cross-sectional study included eighty-four Iraqi patients with ESRD who had been on recombinant human EPO for more than 6 months and had hemoglobin levels below 11 g/dL. Serum AEA levels were measured in all patients to evaluate their association with treatment response and to investigate correlations with demograph

As the process of  estimate for model and variable selection significant is a crucial process in the semi-parametric modeling At the beginning of the modeling process often At there are many explanatory variables to Avoid the loss of any explanatory elements may be important as a result , the selection of significant variables become necessary , so the process of variable selection is not intended to simplifying  model complexity explanation , and also predicting. In this research was to use some of the semi-parametric methods (LASSO-MAVE , MAVE and The proposal method (Adaptive LASSO-MAVE) for variable selection and estimate semi-parametric single index model (SSIM) at the same time .