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极端尾部概率的估计AI 解读

Applications of extreme value theory to problems of statistical inference typically involve estimating tail probabilities well beyond the range of the data, without the benefit of a concise mathematical model for the sampling distribution. The available model is generally only an asymptotic one. That is, an approximation to probabilities of extreme deviation is supposed, which is assumed to become increasingly accurate as one moves further from the range of the data, but…

极值指数的估计AI 解读

SUMMARY The extremal index is an important parameter measuring the degree of clustering of extremes in a stationary process. If we consider the point process of exceedance times over a high threshold, then this can be shown to converge asymptotically to a clustered Poisson process. The extremal index, a parameter in the interval [0, 1], is the reciprocal of the mean cluster size. Apart from being of interest in its own right, it is a crucial parameter for determining the…

SUMMARY An extension of the threshold method for extreme values is developed, to consider the joint distribution of extremes of two variables. The methodology is based on the point process representation of bivariate extremes. Both parametric and nonparametric models are considered. The simplest case to handle is that in which both marginal distributions are known. For the more realistic case in which the marginal distributions are unknown, a mixed parametric-nonparametric…

概率分布下尾的最大似然估计AI 解读

SUMMARY We consider the estimation of the three parameters of the lower tail of a distribution function based on the k smallest out of n observations. The likelihood function has a singularity but it is argued that a local maximum, when it exists, should be taken as the m.l.e. Asymptotic results as k → ∞, n → ∞, k/n → 0 show that such a local maximum does exist, and provides consistent estimators whenever the shape parameter is greater than one; otherwise there is no local…

基于最大 k 个观测值的参数与极高分位数估计AI 解读

We consider an estimation problem when only the k largest observations of a sample of size n are available. It is assumed that the underlying distribution function F belongs to the domain of attraction of a known extreme-value distribution and that k remains fixed as n → ∞. We present estimators for the location and scale parameters and for p-quantiles of F, where p is of the form 1 — c/n (c fixed). These estimators are either asymptotically maximum likelihood or minimum…

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变量含测量误差的回归模型中斜率的一致估计AI 解读

Consistent estimators for the slope coefficient in a regression model, where both variables are subject to errors, do not exist without some restrictions on the model. When the serial correlation of (lag 1) of the regression is known to be nonzero, one can obtain extra information from the first differences and get a consistent estimator for the slope. It turns out that this estimator is (asymptotically) equivalent to an estimator which uses as instrumental variable the…

变量含测量误差回归模型中斜率的一致估计AI 解读

Consistent estimators for the slope coefficient in a regression model, where both variables are subject to errors, do not exist without some restrictions on the model. When the serial correlation of (lag 1) of the regression is known to be nonzero, one can obtain extra information from the first differences and get a consistent estimator for the slope. It turns out that this estimator is (asymptotically) equivalent to an estimator which uses as instrumental variable the…