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广义部分线性模型中用于假设检验的偏差分析AI 解读

In this study, we develop nonparametric analysis of deviance tools for generalized partially linear models based on local polynomial fitting. Assuming a canonical link, we propose expressions for both local and global analysis of deviance, which admit an additivity property that reduces to analysis of variance decompositions in the Gaussian case. Chi-square tests based on integrated likelihood functions are proposed to formally test whether the nonparametric term is…

部分线性模型的方差分析与 F 检验：环境健康数据应用AI 解读

Fish consumption during pregnancy exposes the fetus to both the neurotoxicant methylmercury and nutrients known to be beneficial for brain development. When nutrient status is not measured, maternal methylmercury levels may be a partial biomarker for both toxic and nutrient exposures. It is therefore necessary to employ a flexible model—such as the partial linear model—that will allow for possible nonlinear trends of methylmercury. To enhance interpretations of fitting a…

局部多项式回归的方差分析、决定系数与 F 检验AI 解读

This paper provides ANOVA inference for nonparametric local polynomial regression (LPR) in analogy with ANOVA tools for the classical linear regression model. A surprisingly simple and exact local ANOVA decomposition is established, and a local R-squared quantity is defined to measure the proportion of local variation explained by fitting LPR. A global ANOVA decomposition is obtained by integrating local counterparts, and a global R-squared and a symmetric projection matrix…

施加单调性约束的非参数核回归AI 解读

We suggest a method for monotonizing general kernel-type estimators, for example local linear estimators and Nadaraya .Watson estimators. Attributes of our approach include the fact that it produces smooth estimates, indeed with the same smoothness as the unconstrained estimate. The method is applicable to a particularly wide range of estimator types, it can be trivially modified to render an estimator strictly monotone and it can be employed after the smoothing step has…

参数回归模型的拟合优度检验AI 解读

Several new tests are proposed for examining the adequacy of a family of parametric models against large nonparametric alternatives. These tests formally check if the bias vector of residuals from parametric fits is negligible by using the adaptive Neyman test and other methods. The testing procedures formalize the traditional model diagnostic tools based on residual plots. We examine the rates of contiguous alternatives that can be detected consistently by the adaptive…

基于粗糙度度量的拟合优度检验AI 解读

A test for the one-sample goodness-of-fit problem is proposed. The test is based on a distance that measures the difference, in terms of roughness, between the underlying density function and the hypothesized density function. One advantage of using a roughness measure is high power in detecting high-frequency alternatives and densities with sharp features. The test statistic that estimates the distance is derived from the viewpoint of kernel density estimation, and a…

基于粗糙度度量的拟合优度检验AI 解读

A test for the one-sample goodness-of-fit problem is proposed. The test is based on a distance that measures the difference, in terms of roughness, between the underlying density function and the hypothesized density function. One advantage of using a roughness measure is high power in detecting high-frequency alternatives and densities with sharp features. The test statistic that estimates the distance is derived from the viewpoint of kernel density estimation, and a…