Random sieve likelihood and general regression models
Random sieve likelihood and general regression models
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DOI：
10.2307/2669998
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发表时间：
1999-09-01
影响因子：
3.7
通讯作者：
Wong, WH
中科院分区：
文献类型：
--
作者：
Shen, XT;Shi, J;Wong, WH
考虑半参数回归模型Y = f（theta，X，θ），其中f是已知函数，θ是未知向量，θ由随机误差和可能的一些未观察变量组成，分布F（.）（X，X）是不确定的。本文介绍了在一般情况下，新的方法来估计θ和F（.）。所提出的方法构造一个配置文件的可能性定义在随机水平集（随机筛）。所提出的方法与经验似然相关，但更普遍适用。四个例子进行了讨论，包括二次模型，高维半参数回归，非参数随机效应模型，线性回归与右删失数据。仿真结果和渐近分析支持所提出的方法的实用性和有效性。
Consider a semiparametric regression model Y = f (theta, X, epsilon), where f is a known function, theta is an unknown vector, epsilon consists of a random error and possibly of some unobserved variables, and the distribution F(.) of (epsilon, X) is unspecified. This article introduces, in a general setting, new methodology for estimating theta and F(.). The proposed method constructs a profile likelihood defined on random-level sets (a random sieve). The proposed method is related to empirical likelihood but is more generally applicable. Four examples are discussed, including a quadratic model, high-dimensional semiparametric regression, a nonparametric random-effects model, and linear regression with right-censored data. Simulation results and asymptotic analysis support the utility and effectiveness of the proposed method.