Second order asymptotic efficiency in a partial linear model
This paper considers a model of the form \(Y=X\beta+g(T)+\varepsilon\), where \(X\in R^ 1\) is an explanatory variable that enters linearly, \(\beta\) is an unknown parameter, \(T\) is another explanatory variable that enters in a nonlinear fashion, \(g(\cdot)\) is an unknown smooth function of \(T\) in \(R^ 1\), \((X,T)\) and \(\varepsilon\) are independent and \(\varepsilon\) is the random error with mean 0 and variance \(\sigma^ 2\). The second order asymptotic efficiency of estimators of \(\beta\) based on the maximum likelihood estimator has been studied.
scientific article; zbMATH DE number 445291