Structure selection for bounded-parameter models: consistency conditions and selection criterion

Veres S.M.; Norton J.P.

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Structure selection for bounded-parameter models: consistency conditions and selection criterion

机译：有界参数模型的结构选择：一致性条件和选择准则

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Strong-consistency conditions for structure selection in bounded-parameter models are studied. A certain robust selection criterion, based on the volume of the exact parameter-bounding polytope, is proposed for linear regression models. The effectiveness of the polytope volume criterion is demonstrated on a model nonlinear in its variables but linear in its parameters. Its strong consistency is proved for a large class of noise distributions. The usual assumptions on the noise, namely independence, constant variance, or martingale difference properties, need not be made, but asymptotic independence is assumed.

机译：研究了有界参数模型中结构选择的强一致性条件。针对线性回归模型，提出了一个基于可靠参数边界多义词的数量的鲁棒选择准则。在变量变量为非线性但参数变量为线性的模型上证明了多位点体积标准的有效性。它的强一致性被证明适用于各种噪声分布。不需要对噪声进行通常的假设，即独立性，恒定方差或mar差异属性，但可以假定渐近独立性。

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《IEEE Transactions on Automatic Control》 |1991年第4期|P.474-481|共8页
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Veres S.M.; Norton J.P.;
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Structure selection for bounded-parameter models: consistency conditions and selection criterion

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