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Articles by Sohel Rana
Total Records ( 3 ) for Sohel Rana
  Ehab A. Mahmood , Habshah Midi , Sohel Rana and Abdul Ghapor Hussin
  Background and Objective: The existence of outliers in any type of data influences the efficiency of an estimator. Few methods for detecting outliers in a simple circular regression model have been proposed in the study but it suspected that they are not very successful in the presence of multiple outliers in a data set. This study aimed to investigate new statistic to identify multiple outliers in the response variable in a simple circular regression model. Materials and Methods: The proposed statistic is based on calculating robust circular distance between circular residuals and circular location parameter. The performance of the proposed statistic is evaluated by the proportion of detected outliers and the rate of masking and swamping. The simulation study is applied for different sample sizes at 10 and 20% ratios of contamination. Results: The results from simulated data showed that the proposed statistic has the highest proportion of outliers and the lowest rate of masking comparing with some existing methods. Conclusion: The proposed statistic is very successful in detecting outliers with negligible amount of masking and swamping rates.
  S.K. Sarkar , Habshah Midi and Sohel Rana
  Logistic regression is one of the most frequently used statistical methods as a standard method of data analysis in many fields over the last decade. However, analysis of residuals and identification of influential outliers are not studied so frequently to check the adequacy of the fitted logistic regression model. Detection of outliers and influential cases and corresponding treatment is very crucial task of any modeling exercise. A failure to detect influential cases can have severe distortion on the validity of the inferences drawn from such modeling. The aim of this study is to evaluate different measures of standardized residuals and diagnostic statistics by graphical methods to identify potential outliers. Evaluation of diagnostic statistics and their graphical display detected 25 cases as outliers but they did not play notable effect on parameter estimates and summary measures of fits. It is recommended to use residual analysis and note outlying cases that can frequently lead to valuable insights for strengthening the model.
  Habshah Midi , S.K. Sarkar and Sohel Rana
  The aim of this study was to fit a multinomial logit model and check whether any gain achieved by this complicated model over binary logit model. It is quite common in practice, the categorical response have more than two levels. Multinomial logit model is a straightforward extension of binary logit model. When response variable is nominal with more than two levels and the explanatory variables are mixed of interval and nominal scale, multinomial logit analysis is appropriate than binary logit model. The maximum likelihood method of estimation is employed to obtain the estimates and consequently Wald test and likelihood ratio test have been used. The findings suggest that parameter estimates under two logits were similar since neither Wald statistic was significant. Thus, it can be concluded that complicated multinomial logit model was no better than the simpler binary logit model. In case of response variable having more than two levels in categorical data analysis, it is strongly recommended that the adequacy of the multinomial logit model over binary logit model should be justified in its fitting process.
 
 
 
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