Nor Azrita Mohd Amin
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Nor Azrita Mohd Amin
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Nor Azrita, Mohd Amin
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Amin, N. A.M.
Mohd Amin, Nor Azrita
Amin, Nor Azrita Mohd
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56288444700

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Assessing the effect of different covariates distributions on parameter estimates for Multinomial Logistic Regression (MLR)

2020 , Hamzah Abdul Hamid , Siti Raudhah Ismail , Sahimel Azwal Sulaiman , Nor Azrita Mohd Amin

n fitting a multinomial logistic regression model, one of the most important part is estimating the parameter. In Multinomial Logistic Regression (MLR), Maximum Likelihood Estimation (MLE) method is used to estimate the parameters. MLE is the suitable method to be applied to the problems associated with categorical response variables since it has several benefits such as sufficiency, consistency, efficiency and parameterization invariance. This study investigates the different type of continuous distributions (normal, negatively skewed, positively skewed) on parameter estimation via Monte Carlo simulation. From the simulation result, it shows that as the sample size increases, the effect of covariate distribution reduces. The estimated parameter also less affected for model with normal covariate distribution. At sample size 300 and above, the estimated parameter with normal covariate distribution is considered as close to the true parameter value. Interestingly, for the positively skewed, the estimated parameter also obtained unbiased parameter at sample size 300 and above. However, for negatively skewed, it requires a larger sample size to get closer to the true parameter value. The estimated parameters deviate too far from the true parameter at small sample size. As expected, as sample size increases the parameter estimates for all distributions are getting close to the true parameter value. Lastly, the distribution for MLR with more than one covariate give the same effect as the MLR model with only one covariate on parameter estimations.

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The comparison of extreme rainfall prediction for Northern region of Peninsular Malaysia based on GEV and GPD models

2024 , Mohd Khaidir Mohamed Salleh , Nor Azrita Mohd Amin , Noor Fadhilah Ahmad Radi , Siti Aisyah Zakaria , Muhamad Hafiz Masran , Wan Nor Munirah Ariffin

Extreme rainfall prediction is a critical aspect in hydrological and climate research fields to estimate the probability of extreme events, such as heavy rainfall or floods. These extreme events occur all over the world and have a tremendous impact on human health, injury and illness, and the imbalance of the ecosystem. This paper aims to compare the prediction of extreme rainfall between generalized extreme value distribution (GEV) and generalized Pareto distribution (GPD) for 10 years return period. The daily rainfall data of northern region in Peninsular Malaysia were obtained from Department of Irrigation and Drainage Malaysia (DID) for 29 stations for the period 1999 to 2019 is used. The findings will be beneficial for hydrologists to improve understanding of the difference between the analysis of the standard data modeling with extreme data modeling as well as to understand the difference between two main approaches in extreme data analysis. Both models show Klinik Bkt. Bendera station will encounter the highest 10 years return level compared to the other stations. The maximum corresponding 10-years return value for GPD is 147.26mm while for GEV is 142.39mm. These values are reaching the very heavy category of rainfall intensity index in Malaysia.

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