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非线性向量时间序列的多元函数系数回归模型AI 解读

Vector time series data are widely met in practice. In this paper we propose a multivariate functional-coefficient regression model with heteroscedasticity for modelling such data. A local linear smoother is employed to estimate the unknown coefficient matrices. Asymptotic normality of the proposed estimators is established, and bandwidth selection is considered. To deal with the co-integration commonly observed in financial markets, we propose an error-corrected…

利用辅助协变量的加性风险回归AI 解读

We consider the additive hazard model when some of the true covariates are measured only on a randomly selected validation set whereas auxiliary covariates are observed for all study subjects. An updated pseudoscore estimation approach is proposed for the parameters of the additive hazard model. It allows one to fit the model with auxiliary covariates, while leaving the baseline hazard unspecified. Asymptotic properties of the proposed estimators are established, and…

广义线性混合模型中的稳健估计AI 解读

We propose robust methods for estimation of parameters of interest in an extended generalised linear mixed model, in which only the conditional means of the responses given the random effects are specified. A first‐step estimator θ of the vector θ of parameters is obtained by solving a system of estimating equations. It is shown that θ is consistent. If, furthermore, the conditional variances are correctly specified, a second‐step estimator, θ, can be obtained by solving a…