A Sieve maximum likelihood estimator(MLE) is proposed to estimate both the regression parameters and the nonparametric function,and a score test is provided for the presence of excess zeros.

Analysis of precision for maximum likelihood estimation in the Weibull distribution;

On Attributes of Maximum Likelihood Estimation;

Frequent-purchased Models and Maximum Likelihood Estimationfor Its Parameters;

Laws of the iterated logarithm for the maximum likelihood estimators under random censorship;

Moderate deviations for the maximum likelihood estimators under random censorship;

Order restricted maximum likelihood estimator of the parameters in Weibull distribution under randomly censored condition;

An Approximate MLE of Lognormal Model under General Type-ⅡProgressive Censoring;

The Estimation of MLE of Ⅱ Type Ending-Cutting Weibull DistributionParameter and Bayes Estimation Under the Uncomplete Information;

The Existence and Uniqueness of MLE for Weibull Distribution with Complete Data;

Optimal track fusion based on maximum likelihood estimate;

The maximum likelihood estimates on the parameters of multivariate degenerated normal distribution;

At last,optimal fusion value of multi-odometer data is obtained by maximum likelihood estimate.

In this paper,the comparison is qiven between the random simulation of the maximum likelihood estimators and the quasi-maximum likelihood estimators of the population-averaged treatment effects in the one-dimensional structural regression models with one covariate and the measurement errors.