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gnuplot 4.0：可移植的交互式绘图工具AI 解读

Abstract gnuplot , under active development since 1986, is an interactive plotting utility for UNIX, IBM OS/2, MS Windows, DOS, Apple Macintosh, VMS, Atari and many other platforms. It is free and open source, though it is not licensed under the GPL, nor is it GNU software. gnuplot supports a number of ‘terminal’ types including interactive screen terminals, pen plotters, printers, and pseudo‐devices like LATEX, metafont, pdf, svg, and bitmap png. As of version 4.0 it also…

交叉验证与条件概率密度估计AI 解读

Many practical problems, especially some connected with forecasting, require nonparametric estimation of conditional densities from mixed data. For example, given an explanatory data vector X for a prospective customer, with components that could include the customer's salary, occupation, age, sex, marital status, and address, a company might wish to estimate the density of the expenditure, Y, that could be made by that person, basing the inference on observations of (X, Y)…

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使用 R 教授计量经济学AI 解读

Abstract R, an open‐source programming environment for data analysis and graphics, has in only a decade grown to become a de‐facto standard for statistical analysis against which many popular commercial programs may be measured. The use of R for the teaching of econometric methods is appealing. It provides cutting‐edge statistical methods which are, by R's open‐source nature, available immediately. The software is stable, available at no cost, and exists for a number of…

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非参数回归的一致显著性检验AI 解读

This article presents a framework for individual and joint tests of significance employing nonparametric estimation procedures. The proposed test is based on nonparametric estimates of partial derivatives, is robust to functional misspecification for general classes of models, and employs nested pivotal bootstrapping procedures. Two simulations and one application are considered to examine size and power relative to misspecified parametric models and to test for the linear…

一般平稳过程的可行交叉验证模型选择AI 解读

Cross-validation is a method used to estimate the expected prediction error of a model. Such estimates may be of interest in themselves, but their use for model selection is more common. Unfortunately, cross-validation is viewed as being computationally expensive in many situations. In this paper it is shown that the h-block cross-validation function for least-squares based estimators can be expressed in a form which enormously impact on the amount of calculation required.…