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Search Results: 1 - 10 of 242 matches for " Kayode Ayinde "
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Performances of Some Estimators of Linear Model with Autocorrelated Error Terms in the Presence of Multicollinearity
Kayode Ayinde
Research Journal of Applied Sciences , 2012,
Abstract: Assumptions in classical linear regression model that regressors are assumed to be independent and non-stochastic in repeated sampling are often violated by economist and other social scientists. This is because their regressors are generated by stochastic process beyond their control. Consequently, in this study we examine the performances of the Ordinary Least Square (OLS) and four Generalized Least Square (GLS) estimators of linear model with autocorrelated error terms when normally distributed stochastic regressors exhibit multicollinearity. These estimators are compared by examing their finite sampling properties at various levels of autocorrelation and non-validity of the multicollinearity assumption through Monte-Carlo studies. Results show that the Maximum Likelihood (ML) and the Hildreth and LU (HILU) estimators are generally preferable in estimating all the parameters of the model at all the levels of autocorrelation and multicollinearity. Consequently, when the these two forms of correlations can not be ascertained in a data set, it is more preferable to use either the ML or HILU estimator to estimate all parameters of the model.
Robust Regression Diagnostics of Influential Observations in Linear Regression Model  [PDF]
Kayode Ayinde, Adewale F. Lukman, Olatunji Arowolo
Open Journal of Statistics (OJS) , 2015, DOI: 10.4236/ojs.2015.54029
Abstract: In regression analysis, data sets often contain unusual observations called outliers. Detecting these unusual observations is an important aspect of model building in that they have to be diagnosed so as to ascertain whether they are influential or not. Different influential statistics including Cook’s Distance, Welsch-Kuh distance and DFBETAS have been proposed. Based on these influential statistics, the use of some robust estimators MM, Least trimmed square (LTS) and S is proposed and considered as alternative to influential statistics based on the robust estimator M and the ordinary least square (OLS). The statistics based on these estimators were applied into three set of data and the root mean square error (RMSE) was used as a criterion to compare the estimators. Generally, influential measures are mostly efficient with M or MM robust estimators.
Estimators of Linear Regression Model and Prediction under Some Assumptions Violation  [PDF]
Kayode Ayinde, Emmanuel O. Apata, Oluwayemisi O. Alaba
Open Journal of Statistics (OJS) , 2012, DOI: 10.4236/ojs.2012.25069
Abstract: The development of many estimators of parameters of linear regression model is traceable to non-validity of the assumptions under which the model is formulated, especially when applied to real life situation. This notwithstanding, regression analysis may aim at prediction. Consequently, this paper examines the performances of the Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis in prediction of linear regression model under the joint violations of the assumption of non-stochastic regressors, independent regressors and error terms. With correlated stochastic normal variables as regressors and autocorrelated error terms, Monte-Carlo experiments were conducted and the study further identifies the best estimator that can be used for prediction purpose by adopting the goodness of fit statistics of the estimators. From the results, it is observed that the performances of COR at each level of correlation (multicollinearity) and that of ML, especially when the sample size is large, over the levels of autocorrelation have a convex-like pattern while that of OLS and PC are concave-like. Also, as the levels of multicollinearity increase, the estimators, except the PC estimators when multicollinearity is negative, rapidly perform better over the levels autocorrelation. The COR and ML estimators are generally best for prediction in the presence of multicollinearity and autocorrelated error terms. However, at low levels of autocorrelation, the OLS estimator is either best or competes consistently with the best estimator, while the PC estimator is either best or competes with the best when multicollinearity level is high(λ>0.8 or λ<-0.49).
A Comparative Study of the Performances of the OLS and Some GLS Estimators When Stochastic Regressors are Correlated with the Error Terms
Kayode Ayinde,B.A. Oyejola
Research Journal of Applied Sciences , 2012,
Abstract: The estimates of the OLS estimator of the Classical Linear Regression Model are known to be inconsistent when regressors are correlated with the error terms. However, this does not imply that inference is impossible. In this study, we compare the performances of the OLS and some Feasible GLS estimators when stochastic regressors are correlated with the error terms through Monte Carlo studies at both low and high replications. The performances of the estimators are compared using the following small sampling properties of estimators at various levels of correlation: bias, absolute bias, variance and more importantly the mean squared error of the model parameters. Results show that the OLS and GLS estimators considered in the study are equally good in estimating the model parameters when replication is low. However with increased replication, the OLS estimator is most efficient even though the performances of all the estimators exhibit no significant difference when the correlation between regressor and error terms tends to ±1.
Empirical Investigation of Effect of Multicollinearity on Type 1 Error Rates of the Ordinary Least Squares Estimators
O.O. Alabi,Kayode Ayinde,B.A. Oyejola
Journal of Modern Mathematics and Statistics , 2012,
Abstract: The effect of multicollinearity on the parameters of regression model using the Ordinary Least Squares (OLS) estimator is not only on estimation but also on inference. Large standard errors of the regression coefficients result in very low values of the t-statistic. Consequently, this study attempts to investigate empirically the effect of multicollinearity on the type 1 error rates of the OLS estimator. A regression model with constant term ( 0) and two independent variables (with 1 and 2 as their respective regression coefficients) that exhibit multicollinearity was considered. A Monte Carlo study of 1000 trials was conducted at 8 levels of multicollinearity (0, 0.25, 0.5, 0.7, 0.75, 0.8, 0.9 and 0.99) and sample sizes (10, 20, 40, 80, 100, 150, 250 and 500). At each specification, the true regression coefficients were set at unity. Results show that multicollinearity effect on the OLS estimator is not serious in that the type 1 error rates of 0 is not significantly different from the preselected level of significance (0.05), in all the levels of multicollinearity and samples sizes and that that of 1 and 2 only exhibits significant difference from 0.05 in very few levels of multicollinearity and sample sizes. Even at these levels the significant level different from 0.06.
International Journal of Engineering Science and Technology , 2012,
Abstract: Performances of estimators of linear regression model with autocorrelated error term have been attributed to the nature and specification of the explanatory variables. The violation of assumption of the independence of the explanatory variables is not uncommon especially in business, economic and social sciences, leading to the development of many estimators. Moreover, prediction is one of the main essences of regression analysis. This work, therefore, attempts to examine the parameter estimates of the Ordinary Least Square estimator (OLS), Cochrane-Orcutt estimator (COR), Maximum Likelihood estimator (ML) and the estimators based on Principal Component analysis (PC) in prediction of linear regression model with autocorrelated error terms under the violations of assumption of independent regressors (multicollinearity) using Monte-Carlo experiment approach. With uniform variables as regressors, it further identifies the best estimator that can be used for prediction purpose by averaging the adjusted co-efficient of determination of each estimator over the number of trials. Results reveal that the performances of COR and ML estimators at each level of multicollinearity over the levels of autocorrelation are convex – like while that of the OLS and PC estimators are concave; and that asthe level of multicollinearity increases, the estimators perform much better at all the levels of autocorrelation. Except when the sample size is small (n=10), the performances of the COR and ML estimators are generally best and asymptotically the same. When the sample size is small, the COR estimator is still best except when the autocorrelation level is low. At these instances, the PC estimator is either best or competes with the best estimator. Moreover, at low level of autocorrelation in all the sample sizes, the OLS estimator competes with the best estimator in all the levels of multicollinearity.
Effect of correlations and equation identification status on estimators of a system of simultaneous equation model
Tinuke L. Johnson,Kayode Ayinde,Benjamin A. Oyejola
Electronic Journal of Applied Statistical Analysis , 2010, DOI: 10.1285/i20705948v3n2p115
Abstract: In simultaneous equations model, multicollinearity and status of identification of the equations have been observed to influence estimation of the model parameters. The error terms of each equation in the model are also expected to be correlated with each other. This study therefore examined the effect of multicollinearity, correlation between error terms and status of identification of equations on six methods of parameter estimation in a simultaneous equations model using Monte Carlo approach. A two equation model, with one equation exactly identified and the other over identified, was considered. The levels of multicollinearity among the exogeneous variables were specified as r = 0.3, 0.6, 0.8, 0.9 and 0.99 and that of correlation between error terms as l = 0.3, 0.6, and 0.9. A Monte Carlo experiment of 250 trials was carried out at three sample sizes (20, 50 and 100). The six estimation methods; Ordinary Least Squares (OLS), Indirect Least Squares (ILS), Limited Information Maximum Likelihood (LIML), Two Stage Least Squares (2SLS), Full Information Maximum Likelihood (FIML) and Three Stage Least Squares (3SLS); were ranked according to their performances. Finite properties of estimators’ criteria namely bias, absolute bias, variance and mean squared error were used for comparing the methods. An estimator is best at a specified level of multicollinearity, correlation between error terms and sample size if it has minimum total rank over the model parameters and the criteria. Results show that the OLS estimator is best in estimating the parameters of the exactly identified equation at severe level of multicollinearity (r 1) at all
Ife Origin Influence in the History of Ijebu People of South- Western Nigeria
A Ayinde
African Research Review , 2011,
Abstract: This paper examined the lfe origin influence on the history of the Ijebu people. Though the Ijebu people are part of the Yoruba people living in the South-western part of Nigeria, who mostly traced their origin to Ife as the source of mankind, there are doubts in some quarters whether the people are Yoruba. The primary data for this paper were drawn from oral performance of oriki from selected Ijebu towns namely, Ijebu–Ode, Omu, Ikorodu, Aiyepe, Idowa, Imodi, Ijebu-Igbo; selected obas; some warlords and some notable personalities of Ijebuland. The secondary data were the gramophone records, waxed by Ijebu speakers and non-Ijebu speakers, written records, as well as library and archival documentations from the Universities in the South-western part of Nigeria. The study throws more light on the history and origin of the Ijebu people. It also proves that the Ijebu people are connected to the larger Yoruba race and that the Ijebu are still one despite their presence in more than one state and in many towns. The Ijebu people also have their own warlords like other Yoruba people. The study reveals that there is connection between the Ijebu people and the remaining Yoruba society from the Ijebu praise poetry studied. Through the oriki of Ijebupeople, we can locate their source, origin and points of migration to their present location. The study contributes to the development of Yoruba literature in that it has added to our corpus of oriki. This work would be of immense value to scholars of not only literary study but also other fieldssuch as history, linguistics, sociology, anthropology, ethnography and cultural study
The Sphere In-between: Najib Mahfuz on Power, Status and Authority in Africa’s Public Sphere
OA Ayinde
Africa Development , 2009,
Abstract: This paper is hinged on the following propositions: in no other region in Africa are the arguments about the role of the artist in the ‘public’ sphere more intense as in North Africa. The problematic of what constitutes the ‘public’ sphere in North Africa is circumscribed by the struggle for and the contest over ‘power’, ‘status’ and ‘authority’; the attempt by North African writers, particularly Najib Mahfuz (d. 2006), to mirror the socio-political and cultural fissures and contradictions in the public sphere usually leads to conflict not only over what constitutes the ‘public sphere’ and who governs it, but equally on how the ‘private’ and the sphere ‘in-between’ could be reclaimed for the ‘public good’. In grappling with the foregoing, the paper rereads Najib Mahfuz’s Awld Hratin 1959). In reading for ‘meaning’ and the ‘meaning of meaning’ in Awld Hratin, the paper pays attention to the socio-political and cultural codes provided by Najib Mahfuz, even as it searches for possible theoretical insights that the works of Arab-African and Euro-American writers including Ibn Qayyim, Abdul Qhir al-Jurjn, Edward Said, Michel Foucault and Benhabib could yield in an excursus which probes into how the trialectic of power, status and authority continues to shape the ‘public’, the ‘private’ and the sphere ‘in-between’ of Egyptian society.
Psychological Techniques In Helping Rape Victims
OA Ayinde
Edo Journal of Counselling , 2008,
Abstract: Counselling psychology is concerned with assisting people who have personal, social, educational and vocational problems to better understand themselves and solve their problems. Rape is seen as a problem that militates against the psychological well being of victims, because of the devastating aftermath of the incident. It is seen as a crisis that precipitates the individual into a state of disequilibrium. The paper reviews the symptoms and traumatic experience of rape victims. It also lays emphasis on the psychological techniques that could be used in helping the rape victims live a better and rational life.
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