Researcher Image
لقاء علي محمد - Lekaa Ali Mohammed
PhD - professor
College of Administration and Economics , Statistics
[email protected]
Summary

تدريسية في قسم الاحصاء- جامعة بغداد - كلية الادارة والاقتصاد

Qualifications

حاصلة على شهادة البكاللوريوس في الاحصاء 1989 1995حاصلة على شهادة الماجستير في الحصاء عام 2003حاصلة على شهادة الدكتوراه في الاحصاء عام

Teaching

قمت بتدريس مادة احتمالات 1996-1999 قمت بتدريس مادة مبادئ الاحصاء 2003-2007 قمت بتدريس مادة الاستدلال القياسي 1996-1998-2004-2011 قمت بتدريس قياس اقتصادي 1996-1998-2011 قمت بتدريس مادةالرياضيات1998-1996 ** =قمت بتدريس الاحصاء وتطبيقات الحاسوب ماجستير ودكتوراه نسائية واطفال 2005 قمت بتدريس احصاء وتطبيقات الحاسوب ماجستير احصاء وتطبيقات الحاسوب 2004-2006 قمت بتدريس احصاء وتطبيقات الحاسوب ماجستير صحافة 2004-2006 قمت بتدريس العينات 2013-2016

قمت

Supervision

اشرفت على 26 رسالة واطروحة لطلبة الدراسات العليا

Publication Date
Mon Sep 30 2024
Journal Name
Joiv : International Journal On Informatics Visualization
Evaluation of the Performance of Kernel Non-parametric Regression and Ordinary Least Squares Regression
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Researchers need to understand the differences between parametric and nonparametric regression models and how they work with available information about the relationship between response and explanatory variables and the distribution of random errors. This paper proposes a new nonparametric regression function for the kernel and employs it with the Nadaraya-Watson kernel estimator method and the Gaussian kernel function. The proposed kernel function (AMS) is then compared to the Gaussian kernel and the traditional parametric method, the ordinary least squares method (OLS). The objective of this study is to examine the effectiveness of nonparametric regression and identify the best-performing model when employing the Nadaraya-Watson

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Publication Date
Thu Nov 01 2018
Comparison of Estimates Nonparametric In Multiple Regression Analysis Function (Gamma ,Beta)
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The use of non-parametric models and subsequent estimation methods requires that many of the initial conditions that must be met to represent those models of society under study are appropriate, prompting researchers to look for more flexible models, which are represented by non-parametric models                  

          In this study, the most important and most widespread estimations of the estimation of the nonlinear regression function were investigated using Nadaraya-Watson and Regression Local Ploynomial, which are one of the types of non-linear

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Publication Date
Mon Jun 01 2009
Suggested method for modifying the site parameter
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     Estimating multivariate location and scatter with both affine equivariance and positive break down has always been difficult. Awell-known estimator which satisfies both properties is the Minimum volume Ellipsoid Estimator (MVE) Computing the exact (MVE) is often not feasible, so one usually resorts to an approximate Algorithm. In the regression setup, algorithm for positive-break down estimators like Least Median of squares typically recomputed the intercept at each step, to improve the result. This approach is called intercept adjustment. In this paper we show that a similar technique, called location adjustment, Can be applied to the (MVE). For this purpose we use the Minimum Volume Ball (MVB). In order

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Publication Date
Wed Jun 30 2021
A Comparison between robust methods in canonical correlation by using empirical influence function
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       Canonical correlation analysis is one of the common methods for analyzing data and know the relationship between two sets of variables under study, as it depends on the process of analyzing the variance matrix or the correlation matrix. Researchers resort to the use of many methods to estimate canonical correlation (CC); some are biased for outliers, and others are resistant to those values; in addition, there are standards that check the efficiency of estimation methods.

In our research, we dealt with robust estimation methods that depend on the correlation matrix in the analysis process to obtain a robust canonical correlation coefficient, which is the method of Biwe

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Publication Date
Fri Dec 01 2017
Multi – Linear in Multiple Nonparametric Regression , Detection and Treatment Using Simulation
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             It is the regression analysis is the foundation stone of knowledge of statistics , which mostly depends on the ordinary least square method , but as is well known that the way the above mentioned her several conditions to operate accurately and the results can be unreliable , add to that the lack of certain conditions make it impossible to complete the work and analysis method and among those conditions are the multi-co linearity problem , and we are in the process of detected that problem between the independent variables using farrar –glauber test , in addition to the requirement linearity data and the lack of the condition last has been resorting to the

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Publication Date
Sun Apr 01 2018
Estimate Kernel Ridge Regression Function in Multiple Regression
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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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Publication Date
Thu Sep 01 2011
Analysis of the indicators of the educational process and scientific levelUsing the analysis of variance of ordered data in repeated measurements
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In this research want to make analysis for some indicators and it's classifications that related with the teaching process and the            scientific level for graduate studies in the university by using analysis of variance for ranked data for repeated measurements instead of the ordinary analysis of variance . We reach many conclusions  for the                         

important classifications for each indicator that has affected on   the teaching process.         &nb

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