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Effects of different type of covariates and sample size on parameter estimation for multinomial logistic regression model
Hamzah Abdul Hamid1, Wah, Yap Bee2, Xie, Xian-Jin3.
The sample size and distributions of covariate may affect many statistical modeling
techniques. This paper investigates the effects of sample size and data distribution on
parameter estimates for multinomial logistic regression. A simulation study was conducted
for different distributions (symmetric normal, positively skewed, negatively skewed) for the
continuous covariates. In addition, we simulate categorical covariates to investigate their
effects on parameter estimation for the multinomial logistic regression model. The
simulation results show that the effect of skewed and categorical covariate reduces as
sample size increases. The parameter estimates for normal distribution covariate apparently
are less affected by sample size. For multinomial logistic regression model with a single
covariate study, a sample size of at least 300 is required to obtain unbiased estimates when
the covariate is positively skewed or is a categorical covariate. A much larger sample size is
required when covariates are negatively skewed.
Affiliation:
- Universiti Teknologi MARA, Malaysia
- Universiti Teknologi MARA, Malaysia
- University of Texas Southwestern Medical Center, United States
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Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
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0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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