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本論文はモデル選択とその発展系であるモデル平均（model...

This paper proposed a model averaging method, which is called Generalized Mallows’ Cp model averaging (GC). It works well for heteroskedastic models. Under some regularity conditions, we show that our GC has asymptotic optimality as a model averaging method, and also has asymptotic optimality...

In this paper, we propose a method of averaging generalized least squares estimators for linear regression models with heteroskedastic errors. The averaging weights are chosen to minimize Mallows' Cp-like criterion. We show that the weight vector selected by our method is optimal. It is also...

This paper considers the problem of model averaging for regression models that can be nonlinear in their parameters and variables. We consider a nonlinear model averaging (NMA) framework and propose a weight-choosing criterion, the nonlinear information criterion (NIC). We show that up to a...

To avoid the risk of misspecification between homoscedastic and heteroscedastic models, we propose a combination method based on ordinary least-squares (OLS) and generalized least-squares (GLS) model-averaging estimators. To select optimal weights for the combination, we suggest two information...