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评估回归模型适切性的图形方法
Graphical paradigms for assessing the adequacy of nearly any regression model are discussed. The fundamental idea is to examine the fit of a model using a sequence of marginal model plots. On each plot, nonparametric estimates of fit derived from the model are compared to nonparametric fits based on the observed data. Several examples are given.
回归图形分析导论
This article, which is based on an Interface tutorial, presents an overview of regression graphics, along with an annotated bibliography.The intent is to discuss basic ideas and issues without delving into methodological or theoretical details, and to provide a guide to the literature.
通过响应变量变换实现回归线性化
We consider the problem of choosing a transformation t(y) of a univariate response variable y so that the regression function E{t(y)| x}is linear in the predictor vector x. Let y^ be the fitted values from a linear regression of y on x. Given a few simple assumptions, we show that the plot with y on the horizontal axis and y^ on the vertical axis can be used to visualise the needed transformation t(y). A smoother can then be used as an aid to assessing t(y) visually.
通过响应变量变换实现回归线性化
We consider the problem of choosing a transformation t(y) of a univariate response variable y so that the regression function E{t(y)| x}is linear in the predictor vector x. Let y^ be the fitted values from a linear regression of y on x. Given a few simple assumptions, we show that the plot with y on the horizontal axis and y^ on the vertical axis can be used to visualise the needed transformation t(y). A smoother can then be used as an aid to assessing t(y) visually.
非线性回归中的置信曲线
Standard Wald confidence regions for parameters in a normal nonlinear regression model often fail to capture accurately the uncertainty of estimation as reflected by the corresponding profile log-likelihood. We present a graphical method, along with a stable computational algorithm, for inference on scalar parameters in a nonlinear regression model.
回归中的残差与影响
Conlputat ions E.ratnple 2.3.4Jer Jiyhters no.I 2.3.3Partial residual plots Esantple 2.3.5 Jef fiyhrers no. 2 2.3.4Probability plots E.ratrtpIe 2.3.6 Cloud seeditty no. 4 2.4 Transformations 2.4.1 Fanlilies of fratlsjorr~ratiot~s 2.4.2Selectb~g a trat~s/'orrtlatiott Itr~.ariattceNor~rrality Choice c?/'trtodel i ~t t c i scalirlg the predictors It~f'erettce E.uan~ple 2.4.1 Tree data 110.I 2.4.3Diagnostic ntetttods A tkinson 's method Andrew' nterhod Application to the power…
回归模型中的异方差诊断
For the usual regression model without replication, we provide a diagnostic test for heteroscedasticity based on the score statistic. A graphical procedure to complement the score test is also presented.
使用估计权重组合独立估计量的方差
Consider k independent unbiased estimators of a common parameter τ. Let t* be the linear combination of the k estimators with minimum variance, and let t∩ be any linear combination whose weights sum to unity and are independent of the k estimators. Then t∩ is unbiased for τ and the variance of t∩ depends only on the variance of t* and the mean squared error of the weights as estimates of the optimum weights.
使用估计权重组合独立估计量时的方差
Consider k independent unbiased estimators of a common parameter τ. Let t* be the linear combination of the k estimators with minimum variance, and let t∩ be any linear combination whose weights sum to unity and are independent of the k estimators. Then t∩ is unbiased for τ and the variance of t∩ depends only on the variance of t* and the mean squared error of the weights as estimates of the optimum weights.