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1.
一种对称损失函数下正态总体刻度参数的估计   总被引:12,自引:0,他引:12  
本文研究正态分布中刻度参数在损失函数L(σ,δ)=[(σ-δ)^2]/σδ下的最小风险同变估计及Bayes估计,并讨论(cT(x) d)^1/2形式估计的可容许性与不可容许性,我们发现在这种损失下σ的极大似然估计是不可容许的.  相似文献
2.
序约束下ARCH(0,2)模型参数估计与检验   总被引:3,自引:0,他引:3  
本文研究了平稳ARCH(0,2)模型未知参数α的极大似然估计及有序约束时α的极大似然估计的渐近性质,给出了参数序关系(α1≥α2)的检验方法,并得出了似然比检验统计量的渐近分布。用二次规划的算法,给出求各种情况下参数α的极大似然估计的数值算法。  相似文献
3.
In this paper, an interpolation polynomial operator Fn (f; l, x ) is constructed based on the zeros of a kind of Jacobi polynomials as the interpolation nodes. For any continuous function. f(x)∈ C^b[-1,1] (0≤b≤1) Fn(f; l,x) converges to f(x) uniformly, where l is an odd number.  相似文献
4.
本文研究了在熵损失函数下,定数截尾时指数分布的参数估计,得出在熵损失函数下的最小风险同变(MRE)估计的精确形式.证明了(cT+d)~(-1)形式的一类估计的可容许性和不可容许性.  相似文献
5.
NA误差下部分线性模型的经验似然推断   总被引:2,自引:1,他引:1  
对于部分线性模型yi=βxi+g(ti)+ei,1≤i≤n,这里(xi,ti)是固定设计点,g是未知函数,ei是负相协(NA)随机误差,给出了回归系数的经验似然比统计量,并讨论了似然比统计量的极限分布,可构造参数的经验似然置信区间.  相似文献
6.
This paper is concerned with the distributional properties of a median unbiased estimator of ARCH(0,1)coefficient.The exact distribution of the estimator can be easily derived,however its practical calculations are too heavy to implement, even though the middle range of sample sizes.Since the estimator is shown to have asymptotic normality,asymptotic expansions for the distribution and the percentiles of the estimator are derived as the refinements.Accuracies of expansion formulas are evaluated numerically,and the results of which show that we can effectively use the expansion as a fine approximation of the distribution with rapid calculations.Derived expansion are applied to testing hypothesis of stationarity,and an implementation for a real data set is illustrated.  相似文献
7.
王德辉 《东北数学》2007,23(2):176-188
This paper is concerned with the distributional properties of a median unbiased estimator of ARCH(0,1) coefficient. The exact distribution of the estimator can be easily derived, however its practical calculations are too heavy to implement, even though the middle range of sample sizes. Since the estimator is shown to have asymptotic normality, asymptotic expansions for the distribution and the percentiles of the estimator are derived as the refinements. Accuracies of expansion formulas are evaluated numerically, and the results of which show that we can effectively use the expansion as a fine approximation of the distribution with rapid calculations. Derived expansion are applied to testing hypothesis of stationarity, and an implementation for a real data set is illustrated.  相似文献
8.
In this paper, we study a stationary AR(p)-ARCH(q) model with parameter vectors a and β. We propose a method for computing the maximum likelihood estimator (MLE) of parameters under the nonnegative restriction. A similar method is also proposed for the case that the parameters are restricted by a simple order: α1≥α2≥…≥αq, andβ1≥β2≥…βp. The strong consistency of the above two estimators is discussed. Furthermore, we consider the problem of testing homogeneity of parameters against the simple order restriction. We give the likelihood ratio (LR) test statistic for the testing problem and derive its asymptotic null distribution.  相似文献
9.
10.
As to the acronym NEAR(p), it means "New Exponential Autoregressive Process of order p". The NEAR(p) model is denned by where α0,α1,α2… are non-negative and sum to unity, and the residual sequence{εt} is defined as where q1, q2, … , qp+1 are non-negative and sum to unity, and {Et} is an independent and identically distributed (i.i.d.) sequence of standard exponential variants. Chan showed necessary and sufficient conditions for the existence of a stationary and ergodic NEAR(p) model.  相似文献
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