THE STRONG CONSISTENCY OF THE ASYMMETRIC LEAST SQUARES ESTIMATORS IN NONLINEAR CENSORED REGRESSION MODELS
نویسندگان
چکیده
منابع مشابه
Strong consistency of least-squares estimates in regression models.
A general theorem on the limiting behavior of certain weighted sums of i.i.d. random variables is obtained. This theorem is then applied to prove the strong consistency of least-squares estimates in linear and nonlinear regression models with i.i.d. errors under minimal assumptions on the design and weak moment conditions on the errors.
متن کاملConsistency for Least Squares Regression Estimators with Infinite Variance Data
The least squares estimators are discussed for the linear regression model with random predictors. Both predictors and errors may have infinite variance. Under the condition that the predictors are in a stable domain of attraction, we determine necessary and sufficient conditions for weak consistency of the least squares estimators in the simple linear model. The conditions vary, depending on w...
متن کاملWeighted least squares estimators in possibly misspecified nonlinear regression
The behavior of estimators for misspecified parametric models has been well studied. We consider estimators for misspecified nonlinear regression models, with error and covariates possibly dependent. These models are described by specifying a parametric model for the conditional expectation of the response given the covariates. This is a parametric family of conditional constraints, which makes...
متن کاملOn least-squares regression with censored data
The semiparametric accelerated failure time model relates the logarithm of the failure time linearly to the covariates while leaving the error distribution unspecified. The present paper describes simple and reliable inference procedures based on the least-squares principle for this model with right-censored data. The proposed estimator of the vectorvalued regression parameter is an iterative s...
متن کاملConsistency for the Least Squares Estimator in Non-parametric Regression
We shall study the general regression model Y = g 0 (X) + ", where X and " are independent. The available information about g 0 can be expressed by g 0 2 G for some class G. As an estimator of g 0 we choose the least squares estimator. We shall give necessary and suucient conditions for consistency of this estimator in terms of (basically) geometric properties of G. Our main tool will be the th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Communications of the Korean Mathematical Society
سال: 2003
ISSN: 1225-1763
DOI: 10.4134/ckms.2003.18.4.703