Adjusted least squares estimates for the scaled regression coefficients with censored data
學年 83
學期 1
出版(發表)日期 1994-12-01
作品名稱 Adjusted least squares estimates for the scaled regression coefficients with censored data
著者 Cheng, K. F.; 吳忠武; Wu, Jong-wuu
單位 淡江大學統計學系
出版者 American Statistical Association
著錄名稱、卷期、頁數 Journal of the American Statistical Association 89(428), pp.1483-1491
摘要 The ordinary least squares (OLS) method is popular for analyzing linear regression models because of its simplicity in computation. Suppose that the regressor variables are stochastic and the dependent observations are censored; we can prove that under very general design conditions, the least squares (LS) method can still be useful in estimating the scaled regression coefficients of the general regression model Y * = Q (α + βXi, ℰi), i = 1, 2, …, n, provided that the censored response observations are properly weighted. (Here α is a constant, β is a 1 + p row vector, X i are p + 1 column vectors of explanatory variables, ℰi are unobserved random errors, and Q is an arbitrary unknown function.) Particularly, we shall see that under stronger design conditions, such as assuming that the regressor variables have elliptically symmetric distribution, the OLS estimator consistently estimates the scaled β when the response observations are complete. The model discussed here is not the usual nonlinear regression model, because the functional form of Q is completely unknown. We shall show the proposed adjusted LS estimators are √n-consistent and asymptotically normal under very general censoring schemes. Consistent measurement of the precision for each point estimator is also given. Moreover, a limited Monte Carlo simulation is used to study the practical performance of the procedures.
關鍵字 Adjusted least squares estimate;Asymptotic normality;Density estimate;Limited dependent variable;Link-free
語言 en_US
ISSN 0162-1459
期刊性質 國外
國別 USA
出版型式 ,紙本

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