Asian Science Citation Index is committed to provide an authoritative, trusted and significant information by the coverage of the most important and influential journals to meet the needs of the global scientific community.  
ASCI Database
308-Lasani Town,
Sargodha Road,
Faisalabad, Pakistan
Fax: +92-41-8815544
Contact Via Web
Suggest a Journal
 
Articles by H. Midi
Total Records ( 4 ) for H. Midi
  H. Midi , S. Rana and A.H.M.R. Imon
  In this study, we propose a Leverage Based Near-Neighbor (LBNN) method where prior information on the structure of the heteroscedastic error is not required. In the proposed LBNN method, weights are determined not from the near-neighbor values of the explanatory variables, but from their corresponding leverage values so that it can be readily applied to a multiple regression model. Both the empirical and Monte Carlo simulation results show that the LBNN method offers substantial improvement over the existing methods. The LBNN has significantly reduced the standard errors of the estimates and also the standard errors of residuals for both simple and multiple linear regression models. Hence, the LBNN can be established as one reliable alternative approach to other existing methods that deal with heteroscedastic errors when the form of heteroscedasticity is unknown.
  H. Midi , A. Bagheri and A.H.M.R. Imon
  In this study, we proposed Robust Variance Inflation Factors (RVIFs) in the detection of multicollinearity due to the high leverage points or extreme outliers in the X-direction. The computation of RVIFs is based on robust coefficient determinations which we called RR2 (MM) and RR2 (GM (DRGP)). The RR2 (MM) is coefficient determination of high breakdown point and efficient MM-estimators whereas RR2 (GM (DRGP)) has been defined through an improved GM-estimators. The GM (DRGP) is a GM-estimator with the main aim as downweighting high leverage points with large residuals. It has been introduced by employing S-estimators as initial values, Diagnostic Robust Generalized Potential based on MVE (DRGP (MVE)) as initial weight function and an Iteratively Reweighted Least Squares (IRLS) has been utilized as a convergence method. The numerical results and Monte Carlo simulation study indicate that the proposed RVIFs are very resistant to the high leverage points and unable to detect the multicollinearity in the data especially RR2 (GM (DRGP)). Hence, this indicates that the high leverage points are the source of multicollinearity.
  H. Midi and Z.H. Zamzuri
  The commonly used Maximum Likelihood Estimator (MLE) to estimate the parameters of a time series model requires that the process is normally distributed. However, in real situations, many processes are not normal and have a heavy tail distribution. Hence, the aim of this study is to propose using a distribution free bootstrap method for parameter estimations, when the assumption of normality is not met. The performance of the Bootstrap Estimates (BE) and the MLE estimates of the AR (9) process were then investigated using the Malaysian Opening Price for Second Board data and simulation study. The empirical results indicate that the BE is reasonably close to the MLE estimates, hence, can be established as one reliable alternative approach to the MLE estimates.
  M. Mohammadi , H. Midi , J. Arasan and B. Al-Talib
  Control chart is a statistical process control tool that is used to monitor the changes in a process. Hotelling's T2 chart is one of the most popular control charts for monitoring independently and identically distributed random vectors. This chart detects many types of out-of-control signals, but it is not sensitive to small shifts in the mean vector. This study propose a more efficient T2 control charts based on the re-weigted robust estimators of location and dispersion. The proposed control charts are attained by substituting the classical estimators of the mean vector and covariance matrix in the Hotelling's T2 by the re-weighted MCD and re-weighted MVE estimators. In this study, Monte Carlo simulations were carried out to establish the proposed robust control limit. Following that, we suggested suitable estimators for each condition. Our advice in this study is replacing the classical mean vector and covariance matrix of the data in the Hotelling's T2 statistic by there weighted MCD and Re-weighted MVE estimators.
 
 
 
Copyright   |   Desclaimer   |    Privacy Policy   |   Browsers   |   Accessibility