This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter.We propose a nonparametric imputation method for the missing values, which then leads to imputed estimating equations for the finite dimensional parameter of interest. The asymptotic normality of the parameter estimator is proved in a general setting, and is investigated in detail for a number of specific semiparametric models. Finally, we study the small sample performance of the proposed estimator via simulations
We discuss efficient estimation in regression models that are de- fined by a finite-dimensional para...
Missing data imputation is an important issue in machine learning and data mining. In this paper, we...
© 2021 Ruoxu TanThe thesis mainly studies three different topics on missing data, where we intend to...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
A partially linear model is considered when the responses are missing at random. Imputation, semipar...
Summary. We consider an empirical likelihood inference for parameters defined by general estimating ...
Summary. We consider an empirical likelihood inference for parameters defined by general estimating ...
AbstractA partially linear model is considered when the responses are missing at random. Imputation,...
We develop inference tools in a semiparametric regression model with missing response data. A semipa...
We consider an empirical likelihood inference for parameters defined by general estimating equations...
We discuss efficient estimation in regression models that are de- fined by a finite-dimensional para...
Missing data imputation is an important issue in machine learning and data mining. In this paper, we...
© 2021 Ruoxu TanThe thesis mainly studies three different topics on missing data, where we intend to...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
This paper considers the problem of parameter estimation in a general class of semiparametric models...
A partially linear model is considered when the responses are missing at random. Imputation, semipar...
Summary. We consider an empirical likelihood inference for parameters defined by general estimating ...
Summary. We consider an empirical likelihood inference for parameters defined by general estimating ...
AbstractA partially linear model is considered when the responses are missing at random. Imputation,...
We develop inference tools in a semiparametric regression model with missing response data. A semipa...
We consider an empirical likelihood inference for parameters defined by general estimating equations...
We discuss efficient estimation in regression models that are de- fined by a finite-dimensional para...
Missing data imputation is an important issue in machine learning and data mining. In this paper, we...
© 2021 Ruoxu TanThe thesis mainly studies three different topics on missing data, where we intend to...