Department of Statistics

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Browsing Department of Statistics by Author "Anono, Zuabir Mohammed"

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    COMPARISON OF ESTIMATORS EFFICIENCY FOR LINEAR REGRESSIONS WITH JOINT PRESENCE OF AUTOCORRELATION AND MULTICOLLINEARITY
    (Department of Statistics, Nasarawa Sate University Keffi., 2021-08-06) Anono, Zuabir Mohammed; Adenomon, Monday Osagie
    This paper proposes a new estimator called Two stage K-L estimator by combining these two estimators previously proposed by Prais Winsten (1958) and Kibra with Lukman (2020) for autocorrelation and multicollinearity respectively and to derived the necessary and sufficient condition for its superiority over other competing estimators. Simulation study was used to ascertain the dominance of this new estimator using the finite sample properties of estimators in terms of the estimated mean squared error. The study findings shows that under severe autocorrelation and collinearity condition, the proposed Two stage K-L estimator appears to be having a similar performance with RMLE and MLE. Also, under severe autocorrelation and moderate collinearity condition, regardless of the sample size, the proposed Two stage K-L estimator is seen to outperform all other estimators and lastly, the Two stage K-L estimator appears to have an improved performance as the large sample sizes. The study recommends that when autocorrelation and multicollinearity level is at moderate to severe, the proposed Two stage K-L estimator will perform better regardless of the size of the data, and the degree of autocorrelation and multicollinearity should be considered while estimating parameters and thus applying an efficient estimator to avoid erroneous inferences.
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    ItemOpen Access
    Robustness Test of the Two Stage K-L Estimator in Models with Multicollinear Regressors and Autocorrelated Error Term
    (Department of Statistics, Nasarawa Sate University Keffi., 2021-08-26) Anono, Zuabir Mohammed; Adenomon, Monday Osagie
    In a classical multiple linear regression analysis, multicollinearity and autocorrelation are two main basic assumption violation problems. When multicollinearity exists, biased estimation techniques such as Maximum Likelihood, Restricted Maximum Likelihood and most recent the K-L estimator by Kibria and Lukman [1] are preferable to Ordinary Least Square. On the other hand, when autocorrelation exist in the data, robust estimators like Cochran Orcutt and Prais-Winsten [2] estimators are preferred. To handle these two problems jointly, the study combines the K-L with the Prais-Winsten’s two-stage estimator producing the Two-Stage K-L estimator proposed by Zubair & Adenomon [3]. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) criterion was used to compare the performance of the estimators. Application of the estimators to two (2) real life data set with multicollinearity and autocorrelation problems reveals that the Two Stage K-L estimator is generally the most efficient.