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nonparametric model中文是什么意思

  • 非参数模型

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  • This paper is concerned with parametric component and nonparametric component estimators in a semiparametric regression model . this model is more flexible and explantory than traditional linear or nonparametric model
    本篇论文主要讨论关于半参数回归模型的参数和非参数的估计问题,半参数回归模型是近十几年发展起来的一种统计模型。
  • To the nonparametric model , we still obtain the rth - mean and completely consistency which generalize and improve the results in [ 4 ] ; to the linear model , we obtain the rth - mean and completely consistency which is a new result
    ; k )满足较一般性的条件下,仍获得了g 。 )的r阶平均相合性和一致平均相合性,推广和改进了tran在文献的结果,并在加上适当的条件,获得g 。
  • This paper consists of two parts : in the first part , we will discuss the prob - lem of the pth - mean , complete consistency for the estimators of a nonparamet - ric and linear model with l ~ p - mixingale errors ; in the second part , we will dis - cuss the problem of the rth - mean 、 complete consistency for the estimators of themodels above with weak stationary linear process errors and the uniformly mean consistency . to the nonparametric model y _ ni = g ( x _ ni ) + _ ni , 1 i n , let g _ n ( x ) = w _ ni ( x , w _ n1 , … ? xnn ) y _ ni estimate the unknown function g ( x ) . to the linear model y _ i - x _ i1 1 + … ? + x _ iq ? _ q , we use lse _ nj to estimate the unknown parametric _ j
    本篇论文主要是由两大部分内容构成:一是关于误差是l ~ p ?混合序列的线性回归模型参数的最小二乘估计与非参数回归模型未知函数的权函数估计的p ~ -阶平均相合性和完全收敛性问题;另一部分是关于误差是弱平稳线性过程的线性模型参数的最小二乘估计与非参数回归模型未知函数的权函数估计的r ?阶平均相合性和完全收敛性以及权函数估计的一致平均相合性问题。
  • Nonparametric model is widely used in the practical problems , the reason is that the form of the regression function in the nonparametric model is free , and the limits to the random variate ( x , y ) are fewer . in the past several decades , this model is studied carefully by the researchers of statistics , and many achievements are arrived in both theorial fields and in applicational fields
    非参数回归模型,由于其回归函数的形式可以任意,而且对随机变量( x , y )的分布限制较少,因而在实际中有着广泛的应用背景。几十年来,统计工作者对这一模型进行了深入细致的研究。无论在理论上还是应用上,都取得了许多优秀成果。
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Last modified time:Sat, 16 Aug 2025 00:29:56 GMT

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