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尾部概率近似的一般方法AI 解读

A new transformation referred to as the G (m) n -transformation is introduced to establish a general methodology for finding functions that are easy to evaluate and give very good approximations to tail probabilities. The transformation is based only on a general class of differential equations that include the specified density function in the solution set, at least asymptotically. As a result this new method applies to a broad class of distributions. Not only is the method…

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G谱估计量AI 解读

In this article a modified definition of the G-spectral estimator is given. It is shown that the resulting estimator is a method of moments autoregressive moving average (ARMA) spectral estimator that does not require an estimate of the moving average parameters. As a result, a new formula for the power spectrum of an ARMA process is given that does not explicitly involve the moving average (MA) parameters. This formula then leads to a closed-form expression for the MA…

S 阵列与 Box–Jenkins ARMA 模型识别方法的关系AI 解读

This paper investigates an extension of the partial autocorrelation function, which we call the generalized partial autocorrelation function. These generalized partial autocorrelations, which are not true correlations except when p = 0, are useful in examining the relationship between the S array method of Gray, Kelley, and McIntire (1978) and the Box-Jenkins approach to ARMA model identification. Also, the generalized partial autocorrelation is shown to be a useful model…

广义刀切法及其与统计微分的关系AI 解读

The flexibility of the definition of the first-order generalized jackknife is exploited so that its relation to the method of statistical differentials can be seen. The estimators presented have the same bias reduction and asymptotic distributional properties as the usual generalized jackknife. A limiting case produces an infinitesimal jackknife which represents a generalization of statistical differentials.

未知累积量下的 Edgeworth 展开AI 解读

In this paper a new method of approximating one distribution by another is introduced. The method is essentially a modification of the Edgeworth technique which eliminates the necessity of knowing the cumulants of the distributions involved.

广义 jackknife 与统计微分的关系AI 解读

The flexibility of the definition of the first-order generalized jackknife is exploited so that its relation to the method of statistical differentials can be seen. The estimators presented have the same bias reduction and asymptotic distributional properties as the usual generalized jackknife. A limiting case produces an infinitesimal jackknife which represents a generalization of statistical differentials.

估计中的偏差减少AI 解读

A general procedure for reducing the bias of point estimators is introduced. The technique includes the “jackknife” as a special case. The existing notion of reapplication is shown to lack a desirable bias removal property for which it was originally designed. Proper reapplication is proposed to conform to the general notion of higher order bias elimination and an interesting algorithm for the correct method is defined. Illustrative examples are drawn from ratio estimation,…