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广义线性模型回归系数的影响诊断AI 解读

Suggested diagnostics for influence on the estimated regression coefficients in a generalized linear model have generally approximated the effect of deleting a single case. We apply the local influence method of Cook (1986) to assess the effect of small perturbations of the data, including the vector of responses, case weights, explanatory variables, and the components of one case. The resulting diagnostics allow one to check for different kinds of influence, and may give…

广义线性模型回归系数的影响诊断AI 解读

Suggested diagnostics for influence on the estimated regression coefficients in a generalized linear model have generally approximated the effect of deleting a single case. We apply the local influence method of Cook (1986) to assess the effect of small perturbations of the data, including the vector of responses, case weights, explanatory variables, and the components of one case. The resulting diagnostics allow one to check for different kinds of influence, and may give…

摘要暂缺

摘要暂缺

We propose graphical methods for displaying relevant information on a selected parameter from a normal nonlinear regression model. It is shown that the usual extension of added variable plots from linear to nonlinear regression can fail to reveal important diagnostic information, and that this information can be recovered by using a parameter plot that depends on selected elements of the parameter-effects curvature array.

非线性回归中的参数图AI 解读

We propose graphical methods for displaying relevant information on a selected parameter from a normal nonlinear regression model. It is shown that the usual extension of added variable plots from linear to nonlinear regression can fail to reveal important diagnostic information, and that this information can be recovered by using a parameter plot that depends on selected elements of the parameter-effects curvature array.

非线性回归中参数子集的曲率AI 解读

The relative curvature measures of nonlinearity proposed by Bates and Watts (1980) are extended to an arbitrary subset of the parameters in a normal, nonlinear regression model. In particular, the subset curvatures proposed indicate the validity of linearization-based approximate confidence intervals for single parameters. The derivation produces the original Bates-Watts measures directly from the likelihood function. When the intrinsic curvature is negligible, the…

回归中的残差与影响AI 解读

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…

回归模型的交叉验证AI 解读

A methodolgy for assessment of the predictive ability of regression models is presented. Attention is given to models obtained via subset selection procedures, which are extremely difficult to evaluate by standard techniques. Cross-validatory assessments of predictive ability are obtained and their use illustrated in examples.

回归模型中的异方差诊断AI 解读

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.

用于非椭圆对称多元数据建模的分布族AI 解读

SUMMARY The paucity of distributions that can be used as an aid to modelling the structure of multivariate data is a recognized limitation. We present a relatively simple family of distributions for use in describing data which are not elliptically symmetric. Properties of the family are discussed and it is shown that multivariate Pareto, Burr and Logistic distributions are special cases. In addition, simulation methods for applications in Monte Carlo robustness studies are…

线性回归中的影响观测AI 解读

Characteristics of observations which cause them to be influential in a least squares analysis are investigated and related to residual variances, residual correlations, and the convex hull of the observed values of the independent variables. It is shown how deleting an observation can substantially alter an analysis by changing the partial F-tests, the studentized residuals, the residual variances, the convex hull of the independent variables, and the estimated parameter…