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几何模型平均AI 解读

This paper proposes a geometric model averaging (GMA) method in the framework of general likelihood estimation. Instead of using the classical weighted arithmetic mean, we consider a weighted geometric mean of the conditional probability density functions estimated from different candidate models, allowing both parameter uncertainty and model misspecification. The optimal weight is the minimizer of the Kullback-Leibler (KL) divergence of the true density to the weighted…

嵌套模型下最小二乘模型平均的渐近理论AI 解读

Theoretical results of frequentist model averaging mainly focus on asymptotic optimality and asymptotic distribution of the model averaging estimator. However, even for basic least squares model averaging, many theoretical problems have not been well addressed yet. This article discusses asymptotic properties of a class of least squares model averaging methods with nested candidate models that includes the Mallows model averaging (MMA) of Hansen (2007, Econometrica 75,…

二元响应的半参数模型平均预测AI 解读

Model averaging has attracted abundant attentions in the past decades as it emerges as an impressive forecasting device in econometrics, social sciences and medicine. So far most developed model averaging methods focus only on either parametric models or nonparametric models with a continuous response. In this paper, we propose a semiparametric model averaging prediction (SMAP) method for a dichotomous response. The idea is to approximate the unknown score function by a…

利用碎片化数据进行预测的模型平均方法AI 解读

One main challenge for statistical prediction with data from multiple sources is that not all the associated covariate data are available for many sampled subjects. Consequently, we need new statistical methodology to handle this type of “fragmentary data” that has become more and more popular in recent years. In this article, we propose a novel method based on the frequentist model averaging that fits some candidate models using all available covariate data. The weights in…

不可忽略无应答下的模型选择AI 解读

Existing methods for handling nonignorable missing data rely on the correct specification of parametric models, which is difficult to check. By utilizing the information carried in an instrument, we propose a novel model selection criterion, called the penalized validation criterion, in the presence of nonignorable nonresponse with unspecified propensity. The proposed method can consistently select the most compact correct parametric model from a group of candidate models if…

存在无应答的分层样本：伪经验似然方法AI 解读

Nonresponse is common in surveys. When the response probability of a survey variable Y depends on Y through an observed auxiliary categorical variable Z (i.e., the response probability of Y is conditionally independent of Y given Z), a simple method often used in practice is to use Z categories as imputation cells and construct estimators by imputing nonrespondents or reweighting respondents within each imputation cell. This simple method, however, is inefficient when some Z…