缺失数据下EV模型的调整最小二乘估计

Abstract

Abstract:

The multivariate linear errors-in-variables model when the regressors are missing at random in the sense of
Rubin (1976) is considered in this paper. The weighted adjust LS parameter estimation based on inverse probability is proposed. It is shown that the estimators have consistency and asymptotic normality.
Simulations are shown that our estimators perform well.

Key words: Linear errors-in-variables model, Weighted adjust LSestimation, Inverse probability, Consistency, Asymptotic normality

CLC Number:

62F12

ZHANG Juan, CUI Heng-Jian. Weighted Adjust LS Estimation |in EV Model with Missing Data[J].Acta mathematica scientia,Series A, 2009, 29(6): 1465-1476.

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References

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Weighted Adjust LS Estimation |in EV Model with Missing Data

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