We make analyses of the three hypotheses of regression. Adaptive adjustment of smoothing index parameters and parameter estimation of ARMA models.
对统计算法中回归模型中的假设条件、平滑指数的自适应调整、ARMA模型的参数估计作了一些分析。
In the problem of smoothing, filtering and predicting matching data in aerospace measurement, matching data refers that velocity parameter is differential of position parameter.
讨论了匹配测量数据的平滑、滤波和外推问题。所谓匹配测量数据是指位置参数和速度参数呈求导的匹配关系。
The desingularization smoothing parameter is introduced in calculating the induced velocity on trailing vortex surface.
在诱导速度计算中引入了非奇异光滑参数。
Parameter values in these models were estimated with Bayesian method. Realizations of different shocks were also obtained with Kalman smoothing algorithm.
利用贝叶斯方法估计了模型参数后，又通过卡尔曼平滑算法估计了模型中各个外生冲击的实现值。
For the defect that it is hard to determine the smoothing parameter in time-varying conditions, the adaptive optimizing strategy based on contributing ratio is proposed.
针对广义回归网络在时变环境下难以确定平滑因子，自适应能力弱的缺点，提出了一种基于贡献率的选择优化方案。
As the non-degeneracy condition holds and the smoothing parameter tends to zero, an S-stationary point of the MPCC problem is equivalent to a KKT point of the smoothing nonlinear programming.
当非退化条件成立和磨光参数趋于零时，证明了原问题的S -稳定点与磨光非线性规划的KKT点等价。
As the non-degeneracy condition holds and the smoothing parameter tends to zero, an S-stationary point of the MPCC problem is equivalent to a KKT point of the smoothing nonlinear programming.
当非退化条件成立和磨光参数趋于零时，证明了原问题的S -稳定点与磨光非线性规划的KKT点等价。
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