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互联网流量数据AI 解读

Internet engineering and management depend on an understanding of the characteristics of network traffic. Statistical models are needed that can generate traffic that mimics closely the observed behavior on live Internet wires. Models can be used on their own for some tasks and combined with network simulators for others. But the challenge of model development is immense. Internet traffic data are ferocious. Their statistical properties are complex, databases are very large,…

ATS方法：非高斯数据的非参数回归AI 解读

ATS methods provide an approach to fitting curves and surfaces to data using nonparametric regression when distributions are not necessarily Gaussian. First, a small amount of local averaging (the “A” in ATS) is carried out, then a variance-stabilizing transformation is applied (“T”), and finally the result is smoothed (“S”) using a nonparametric regression procedure. ATS methods are quite broad in terms of applications; in this article we show how they can be used for…

ATS 方法：非高斯数据的非参数回归AI 解读

ATS methods provide an approach to fitting curves and surfaces to data using nonparametric regression when distributions are not necessarily Gaussian. First, a small amount of local averaging (the “A” in ATS) is carried out, then a variance-stabilizing transformation is applied (“T”), and finally the result is smoothed (“S”) using a nonparametric regression procedure. ATS methods are quite broad in terms of applications; in this article we show how they can be used for…

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…

摘要暂缺

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…

季节调整的图形方法AI 解读

Graphical displays deserve to be a routine part of seasonal adjustment, and their routine use is now feasible because of the current revolution in computer graphical hardware and software. We present and illustrate seven graphical displays that provide powerful tools for judging the adequacy of the seasonal adjustment of a series and for understanding the behavior of the trend, seasonal, and irregular components.

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…

稳健局部加权回归与散点图平滑AI 解读

The visual information on a scatterplot can be greatly enhanced, with little additional cost, by computing and plotting smoothed points. Robust locally weighted regression is a method for smoothing a scatterplot, (x i , y i ), i = 1, …, n, in which the fitted value at z k is the value of a polynomial fit to the data using weighted least squares, where the weight for (x i , y i ) is large if x i is close to x k and small if it is not. A robust fitting procedure is used that…

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以对象识别为准则的聚类及其在二维计数表中的应用AI 解读

The formation of a partition of objects, each with an associated random measurement X, is given operational meaning and a figure of merit. The information about X, given each object, is first reduced to information about X, given the cluster in the partition to which that object belongs. The figure of merit for a partition is then the probability of a correct object identification, on the basis of a realization of X, after the information loss. An algorithm is derived for…