http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Rank method for partial functional linear regression models
Ruiyuan Cao,Tianfa Xie,Yu Ping 한국통계학회 2021 Journal of the Korean Statistical Society Vol.50 No.2
In this paper, we consider rank estimation for partial functional linear regression models based on functional principal component analysis. The proposed rank-based method is robust to outliers in the errors and highly efficient under a wide range of error distributions. The asymptotic properties of the resulting estimators are established under some regularity conditions. A simulation study conducted to investigate the finite sample performance of the proposed estimators shows that the proposed rank method performs well. Furthermore, the proposed methodology is illustrated by means of an analysis of the Berkeley growth data.
Model diagnostics of parametric Tobit model based on cumulative residuals
Sun Zhihua,Guo Yuanyuan,Xie Tianfa,Wang Miaomiao 한국통계학회 2021 Journal of the Korean Statistical Society Vol.50 No.1
In this paper, we investigate the adequate check of the parametric Tobit model. A Cramér–Von Mises type test statistic is constructed, and its asymptotic properties under the null and alternative hypotheses are rigorously studied. The method is efective for the adequacy check of parametric regression models with a scalar or multivariate covariate. Furthermore, it avoids the nonparametric smoothing of the regression function and the choice of the smoothing parameter. Simulation studies are conducted to compare the performance of the proposed test procedure and the existing methods in the literature. A real data set of income is analyzed by applying the proposed method.