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a School of Economics and Management, Shanghai Maritime University, Shanghai, People's Republic of China;b Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
zouyuye@shmtu.edu.cn
a School of Economics and Management, Shanghai Maritime University, Shanghai, People's Republic of China
b Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
Abstract
In this thesis, we establish non-linear wavelet density estimators and studying the asymptotic properties of the estimators with data missing at random when covariates are present. The outstanding advantage of non-linear wavelet method is estimating the unsoothed functions, however, the classical kernel estimation cannot do this work. At the same time, we study the larger sample properties of the ISE for hazard rate estimator.
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