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抗异常值直线及相关回归方法AI 解读

In a bivariate (x, y) scatterplot the three-group resistant line is that line for which the median residual in each outer third of the data (ordered on x) is zero. It was proposed by Tukey as an exploratory method resistant to outliers in x and y and suited to hand calculation. A dual plot representation of the procedure yields a fast, convergent algorithm, nonparametric confidence intervals for the slope, consistency, influence function, and asymptotic normality results.…

有效得分、方差分解与蒙特卡洛方差缩减AI 解读

Monte Carlo “swindles” or variance reduction techniques exploit the experimenter's knowledge of the stochastic structure governing the simulated data to construct more precise estimates of unknown parameters. Alternatively, one can reduce the number of replications (and thus the cost) needed to gain a desired level of precision. This article reviews the common case of swindles based on variance decompositions for estimating efficiencies and variances of location and…

为多元一般线性形式生成特征可控的测试数据AI 解读

Experiments to assess the performance characteristics, both of statistical methods and of computer programs that implement them, are best performed using test data with controlled statistical and numerical properties. We describe an algorithm for constructing test data for any multivariate linear model that provides complete control (subject only to the requirement of mutual consistency) over the following factors: regression coefficients; regression and residual sums of…

为多元一般线性模型生成特征可独立控制的测试数据AI 解读

Experiments to assess the performance characteristics, both of statistical methods and of computer programs that implement them, are best performed using test data with controlled statistical and numerical properties. We describe an algorithm for constructing test data for any multivariate linear model that provides complete control (subject only to the requirement of mutual consistency) over the following factors: regression coefficients; regression and residual sums of…

构造具有可控特征的回归：探查回归表现的方法AI 解读

Studies of robust regression algorithms have generally relied on Monte Carlo trials and have concentrated on evaluating the algorithm's performance for long-tailed error distributions. This article identifies other aspects of data structure that can influence the performance of robust regression procedures and presents an algorithm for constructing regression problems in which each of these factors can be controlled separately. The method generalizes to multiple regression…

构造具有可控特征的回归问题：考察回归性能的方法AI 解读

Studies of robust regression algorithms have generally relied on Monte Carlo trials and have concentrated on evaluating the algorithm's performance for long-tailed error distributions. This article identifies other aspects of data structure that can influence the performance of robust regression procedures and presents an algorithm for constructing regression problems in which each of these factors can be controlled separately. The method generalizes to multiple regression…

稳健非线性数据平滑算法的定义与比较AI 解读

Nonlinear data smoothers provide a practical method of finding smooth traces for data confounded with possibly long-tailed or occasionally spikey noise. They are resistant to the effects of extreme observations that are not part of the local pattern, yet they are able to respond rapidly to well-supported patterns. This article defines a collection of nonlinear smoothers based upon running medians and presents methods for describing and comparing their performance. The…