论文检索

L-矩：利用次序统计量的线性组合分析与估计分布AI 解读

SUMMARY L-moments are expectations of certain linear combinations of order statistics. They can be defined for any random variable whose mean exists and form the basis of a general theory which covers the summarization and description of theoretical probability distributions, the summarization and description of observed data samples, estimation of parameters and quantiles of probability distributions, and hypothesis tests for probability distributions. The theory involves…

摘要暂缺

检验广义极值分布的形状参数是否为零AI 解读

SUMMARY When a sequence of extreme values of a physical process has been observed it is often of interest to test whether the observations are distributed according to a type I Fisher-Tippett extreme-value distribution rather than one of types II or III. This is equivalent to testing whether the shape parameter is zero in the generalized extreme-value distribution. Thirteen tests of this hypothesis are here compared in terms of their empirical significance levels and power…

修订与勘误：ARMA过程样本逆自相关的渐近分布AI 解读

J. R. M. Hosking, Amendments and Corrections: The Asymptotic Distribution of the Sample Inverse Autocorrelations of an Autoregressive Moving-Average Process, Biometrika, Vol. 69, No. 2 (Aug., 1982), p. 491

多元时间序列模型的拉格朗日乘子检验AI 解读

SUMMARY Generalizing Hosking (1980a), the Lagrange-multiplier test procedure is applied to hypotheses concerning multivariate autoregressive moving-average time-series models. The portmanteau and Quenouille goodness-of-fit tests for multivariate processes are derived in this manner and three other tests are obtained for multivariate moving-average models.

分数阶差分AI 解读

The family of autoregressive integrated moving-average processes, widely used in time series analysis, is generalized by permitting the degree of differencing to take fractional values. The fractional differencing operator is defined as an infinite binomial series expansion in powers of the backward-shift operator. Fractionally differenced processes exhibit long-term persistence and antipersistence; the dependence between observations a long time span apart decays much more…

多变量 Portmanteau 综合检验统计量AI 解读

Box and Pierce have derived a goodness-of-fit test, the portmanteau test, for univariate autoregressive moving-average (ARMA) time series models. This test is here extended to multivariate ARMA models; the test statistic may be conveniently expressed as a function of the covariances between the residuals of the fitted model. A modified form of the statistic designed to have superior properties in small samples is derived, and the two forms of the statistic are compared via…

ARMA 过程样本逆自相关的渐近分布AI 解读

The inverse autocorrelations of a time series are useful at the identification stage of model building. In practice these quantities must be estimated from the data and so the distributional properties of the estimators are of great importance. In this paper the asymptotic distribution of the sample inverse autocorrelations is derived, making use of the duality between the autocorrelations and inverse autocorrelations of two autoregressive-moving average processes.

时间序列模型的拉格朗日乘子检验AI 解读

Summary The Lagrange-multiplier test procedure is applied to hypotheses concerning autoregressive moving-average time-series models. The portmanteau and Quenouille goodness-of fit tests are derived in this manner against specific alternative hypotheses and two other tests are obtained which have the nature of pure significance tests.

ARMA 过程样本逆自相关的渐近分布AI 解读

The inverse autocorrelations of a time series are useful at the identification stage of model building. In practice these quantities must be estimated from the data and so the distributional properties of the estimators are of great importance. In this paper the asymptotic distribution of the sample inverse autocorrelations is derived, making use of the duality between the autocorrelations and inverse autocorrelations of two autoregressive-moving average processes.

摘要暂缺

参数估计后 ARMA 时间序列模型中 R² 的渐近分布AI 解读

The properties of the estimator of the squared multiple correlation coefficient, R2, for an autoregressive-moving average process are considered. It is shown that the estimator has an asymptotic distribution which is normal with mean .R2 and variance obtainable from the autocorrelations of the process.

自回归时间序列拟合优度统计量渐近分布的统一推导AI 解读

Summary The portmanteau and Quenouille goodness-of-fit tests for an ar(p) model are derived as likelihood-ratio tests against particular alternative hypotheses, and their asymptotic distributions are thereby obtained.