本文考虑具有正态误差假设下混合回归模型的参数估计问题.由于似然函数的无界性,混合回归模型普通的最大似然估计不存在.本文提出一种惩罚最大似然方法来估计混合回归模型的参数,证明惩罚最大似然估计量(penalized maximum likelihood e...本文考虑具有正态误差假设下混合回归模型的参数估计问题.由于似然函数的无界性,混合回归模型普通的最大似然估计不存在.本文提出一种惩罚最大似然方法来估计混合回归模型的参数,证明惩罚最大似然估计量(penalized maximum likelihood estimation, PMLE)具有强相合和渐近正态性.通过深入模拟研究,从估计精确性角度看,惩罚最大似然估计量有很好的表现.本文还给出一个音调感知的例子来说明理论结果的应用.展开更多
Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-r...Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.展开更多
文摘本文考虑具有正态误差假设下混合回归模型的参数估计问题.由于似然函数的无界性,混合回归模型普通的最大似然估计不存在.本文提出一种惩罚最大似然方法来估计混合回归模型的参数,证明惩罚最大似然估计量(penalized maximum likelihood estimation, PMLE)具有强相合和渐近正态性.通过深入模拟研究,从估计精确性角度看,惩罚最大似然估计量有很好的表现.本文还给出一个音调感知的例子来说明理论结果的应用.
基金Project supported by the National Natural Science Foundation of China (No. 10571093, No. 10371059)Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20050055038)+2 种基金the Natural Science Foundation of Shandong Province of China (No. 2006A13)the China Postdoctoral Science Foundation (No. 20060390169)the Tianjin Planning Programs of Philosophy and Social Science of China (No. TJ05-TJ002).
文摘Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.
