In this paper, using the kernel weight function, we obtain the parameter estimation of p-norm distribution in semi-parametric regression model, which is effective to decide the distribution of random errors. Under the...In this paper, using the kernel weight function, we obtain the parameter estimation of p-norm distribution in semi-parametric regression model, which is effective to decide the distribution of random errors. Under the assumption that the distribution of observations is unimodal and symmetry, this method can give the estimates of the parametric. Finally, two simulated adjustment problem are constructed to explain this method. The new method presented in this paper shows an effective way of solving the problem; the estimated values are nearer to their theoretical ones than those by least squares adjustment approach.展开更多
In this paper we study the practical procedure for getting the maximumlikelihood estimates in a semi-parametric regression model with interval censored data.On the basis of the on previous theoretical results, we give...In this paper we study the practical procedure for getting the maximumlikelihood estimates in a semi-parametric regression model with interval censored data.On the basis of the on previous theoretical results, we give the detailed algorithms when there are one or two covariates in the model.展开更多
文摘In this paper, using the kernel weight function, we obtain the parameter estimation of p-norm distribution in semi-parametric regression model, which is effective to decide the distribution of random errors. Under the assumption that the distribution of observations is unimodal and symmetry, this method can give the estimates of the parametric. Finally, two simulated adjustment problem are constructed to explain this method. The new method presented in this paper shows an effective way of solving the problem; the estimated values are nearer to their theoretical ones than those by least squares adjustment approach.
基金This research is supported by the National Natural Science Foundation of China(10071004) and RFDP, Liping Liu was supported in part by the National Basic Research Program of China under Grant 2003CB716101.
文摘In this paper we study the practical procedure for getting the maximumlikelihood estimates in a semi-parametric regression model with interval censored data.On the basis of the on previous theoretical results, we give the detailed algorithms when there are one or two covariates in the model.
