Parameter estimation of the mixture normal distribution for hydro-meteorological variables using Meta-heuristic maximum likelihood

• 영문명: Parameter estimation of the mixture normal distribution for hydro-meteorological variables using Meta-heuristic maximum likelihood
• 발행기관: 한국방재학회
• 저자명: Lee, Taesam Shin, Juyoung Heo, Junhaeng Jeong, Changsam
• 간행물 정보: 『3. 한국방재학회 학술대회논문집』2014년, 483~483쪽, 전체 1쪽
• 주제분류: 공학 > 기타공학
• 파일형태: PDF
• 발행일자: 2014.02.19

영문 초록

This study proposes a new parameter estimation approach for the mixture normal distribution. The developed model estimates parameters of the mixture normal distribution by maximizing the log likelihood function using a meta-heuristic algorithm-genetic algorithm (GA). To verify the performance of the developed model, simulation experiments and practical applications are implemented. From the results of experiments and practical applications, the developed model presents some advantages, such as (1) the proposed model more accurately estimates the parameters even with small sample sizes compared to the expectation maximization (EM) algorithm; (2) not diverging in all application; and (3) showing smaller root mean squared error and larger log likelihood than those of the EM algorithm. We conclude that the proposed model is a good alternative in estimating the parameters of the mixture normal distribution for kutotic and bimodal hydrometeorological data.

키워드

Genetic algorithm EM algorithm Maximum likelihood Streamflow