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局部加权回归：以局部拟合开展回归分析AI 解读

Locally weighted regression, or loess, is a way of estimating a regression surface through a multivariate smoothing procedure, fitting a function of the independent variables locally and in a moving fashion analogous to how a moving average is computed for a time series. With local fitting we can estimate a much wider class of regression surfaces than with the usual classes of parametric functions, such as polynomials. The goal of this article is to show, through…

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月度时间序列的日历效应：建模与调整AI 解读

Most monthly time series that represent a total of some variable for each month contain calendar effects due to changing month length, day-of-the-week effects, and holidays. It is important to remove the calendar variation to allow an effective assessment of the variation due to important factors. New procedures for calendar adjustment are presented in this article. A plausible model for the daily data is used to derive a model for the monthly data in which the power…

月度时间序列的日历效应：建模与调整AI 解读

Most monthly time series that represent a total of some variable for each month contain calendar effects due to changing month length, day-of-the-week effects, and holidays. It is important to remove the calendar variation to allow an effective assessment of the variation due to important factors. New procedures for calendar adjustment are presented in this article. A plausible model for the daily data is used to derive a model for the monthly data in which the power…

月度时间序列中的日历效应：谱分析与图形检测AI 解读

Many time series, particularly national business and economic series, which are reported monthly and represent a total of the series for each month, contain calendar effects due to changing month length, weekly periodicities, and holidays. It is important to detect and remove this spurious calendar variation to allow a better appreciation of the variation in the series due to important factors. This article discusses detection. Two sets of diagnostic methods for detecting…

相关系数的稳健估计与异常值检测AI 解读

Two graphical methods are proposed for identifying bivariate observations that may unduly influence the sample correlation coefficient. Secondly, robust estimators of correlation are developed and a Monte Carlo comparative study is made of these and other well-known estimators. Also considered are methods for developing positive-definite estimates of correlation matrices and extensions of robustness to other problems such as regression are mentioned.