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一般希尔伯特空间响应的部分线性加性回归AI 解读

In this article we develop semiparametric regression techniques for fitting partially linear additive models. The methods are for a general Hilbert-space-valued response. They use a powerful technique of additive regression in profiling out the additive nonparametric components of the models, which necessarily involves additive regression of the nonadditive effects of covariates. We show that the estimators of the parametric components are n-consistent and asymptotically…

二元选择固定效应面板中的非参数模型AI 解读

In this paper we extend the fixed effects approach to deal with non‐linear panel data with non‐parametric components. Specifically, we propose a non‐parametric procedure that generalizes the conditional logit approach. We develop an estimator based on non‐linear stochastic integral equations and provide the asymptotic property of the estimator and an iterative algorithm to implement the estimator. We analyse the small sample behaviour of the estimator through a Monte Carlo…

广义加性模型中的平滑回拟合AI 解读

Generalized additive models have been popular among statisticians and data analysts in multivariate nonparametric regression with non-Gaussian responses including binary and count data. In this paper, a new likelihood approach for fitting generalized additive models is proposed. It aims to maximize a smoothed likelihood. The additive functions are estimated by solving a system of nonlinear integral equations. An iterative algorithm based on smooth backfitting is developed…