Semiparametric p-norm Maximum Likelihood Regression
PAN Xiong,1,2, SUN Hai-yan1
(1. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079,China; 2.Department of Mathematics and Physics, Wuhan Polytechnic University, Wuhan 430023,China)
Abstract£ºIn this paper, used the kernel weight function, we obtain the parameter estimation of p-norm distribution in semiparametric regression model ,which is effective to decide the distribution of random errors. Under the assumption that the distribution of observations is unimodal and symmetrical, this method can give the estimates of X£¬S and ¦Ò. Finally, two simulated adjustment problems 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.
Key words£ºp-norm distributions£» semiparametric regression£» kernel weight function£» maximum likelihood adjustment