1 citations · 2 across the 4 of their papers we have counts for
5 papers
A new Bernstein-binomial model for fitting finite discrete data with over-dispersion: Likelihood-based and Bayesian approaches
Yuan-Fan ZHAO, Yikai GUO, Xun-Jian LI +2
Finite discrete count data are ubiquitous in fields such as biomedicine and social sciences and these data frequently exhibit over-dispersion, rendering the binomial distribution i…
The normalized expectation-maximization (N-EM) algorithm
Guo-Liang Tian, Xuanyu Liu, Yuanfan Zhao
Although the (EM) algorithm is a powerful optimization tool in statistics, it can only be applied to missing/incomplete data problems or to prob…
The upper-crossing/solution (US) algorithm for root-finding with strongly stable convergence
Xun-Jian Li, Hua Zhou, Kenneth Lange +1
In this paper, we propose a new and broadly applicable root-finding method, called as the upper-crossing/solution (US) algorithm, which belongs to the category of non-bracketing (o…
A generalized EMS algorithm for model selection with incomplete data
Ping-Feng Xu, Lai-Xu Shang, Man-Lai Tang +2
Recently, a so-called E-MS algorithm was developed for model selection in the presence of missing data. Specifically, it performs the Expectation step (E step) and Model Selection…
Eigenstructure of Maximum Likelihood from Counts Data
Fanghu Dong
The MLE (Maximum Likelihood Estimate) for a multinomial model is proportional to the data. We call such estimate an eigenestimate and the relationship of it to the data as the eige…