Diagnosis of The Most Important Factors Affecting The Annual Performance Evaluation of The Teaching Cadres Using The Technique of Binary -Response Logistic Regression

Dec 20, 2022

Journal NTU Journal of Pure Sciences

publisher Northern Technical University

DOI http://dx.doi.org/10.56286/ntujps.v1i4.332

Issue 4

Volume 1

The aim of the research is to identify the main factors affecting the evaluation of the annual performance of the teaching staff using the Binary Logistic Regression technique by applying to a random sample of size(217) teaching and teaching staff from four different disciplines (technology, administrative, agricultural, The results of the analysis showed that the completion and publication of scientific research was the most important factor affecting the evaluation of the performance of the teaching staff, followed by the teaching use of modern teaching methods and its incorporation of technology into education.

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A Liu estimator in inverse Gaussian regression model with application in chemometrics

Jul 21, 2022

Journal Mathematical Statistician and Engineering Applications

Issue 3s2

Volume 71

The presence of multicollinearity among the explanatory variables has undesirable effects on the maximum likelihood estimator (MLE). Liu estimator (LE) is a wide used estimator in overcoming this issue. The LE enjoys the advantage that its mean squared error (MSE) is less than MLE. The inverse Gaussian regression (IGR) model is a well-known model in application when the response variable positively skewed. The purpose of this paper is to derive the LE of the IGR under multicollinearity problem. In addition, the performance of this estimator is investigated under numerous methods for estimating the Liu parameter. Monte Carlo simulation results indicate that the suggested estimator performs better than the MLE estimator in terms of MSE. Furthermore, a real chemometrics dataset application is utilized and the results demonstrate the excellent performance of the suggested estimator when the multicollinearity is present in IGR model.

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Jackknifed liu type estimator in the negative binomial regression model

Mar 1, 2022

Journal Journal of Nonlinear Analysis and Application

publisher Semnan University

DOI http://dx.doi.org/10.22075/ijnaa.2022.5990

Issue 1

Volume 13

The Liu estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of Inter-correlated (multicollinearity). The negative binomial regression model is a well-known model in the application when the response variable is non-negative integers or counts. However, it is known that multicollinearity negatively affects the variance of the maximum likelihood estimator of the negative binomial coefficients. To overcome this problem, a negative binomial Liu estimator has been proposed by numerous researchers. In this paper, a Jackknifed Liu-type negative binomial estimator (JNBLTE) is proposed and derived. The idea behind the JNBLTE is to decrease the shrinkage parameter and, therefore, the resultant estimator can be better with a small amount of bias. Our Monte Carlo simulation results suggest that the JNBLTE estimator can bring significant improvement relative to other existing estimators. In addition, the real application results demonstrate that the JNBLTE estimator outperforms both the negative binomial Liu estimator and maximum likelihood estimators in terms of predictive performance.

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