But the methods of estimation of parameter of the GARCH models, BHHH and GMM, may become invalid because they usually meet the situation that the middle data fluctuate greatly.
而GARCH模型参数估计的主要方法BHHH算法和广义矩方法在实际运算中常遇到中间数据震荡从而导致算法整体失效等。
So firstly to get a better estimation of parameter using iterate inversion on wide scale, then using this estimation as initial value on mini scale till to get global optimum of original problem.
因此可先在粗尺度上迭代反演，得到一个较好的参数估计，再将这个估计作为较精细尺度的初值进行反演，直至原问题的全局最优解。
In this dissertation, we mainly focused on the restricted biased estimation and preliminary test estimation of parameter in linear models with equality restrictions and stochastic linear restrictions.
本文主要是研究线性模型参数在等式约束和随机线性约束下的约束有偏估计以及预检验估计等相关的一些问题。
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