| 1. | Moderate deviations for the kernel density estimators
核密度估计的中偏差 |
| 2. | Methods non - parameter kernel density estimation method was adopted
方法采用非参数核密度估计推断方法。 |
| 3. | The general class of kernel density estimates for positive associated samples
样本下一般形式的密度估计 |
| 4. | Kernel density estimation of species abundance distribution in rare and endangered castanopsis kawakamii natural forest
格氏栲天然林物种多度分布的核估计研究 |
| 5. | As not to know the stock prices obey what distribution , we accord to historical data to estimate the distribution of the ultimate stock prices by kernel density estimation , then develop the theorems for options pricing , and price the option
本文研究股票价格不服从几何布朗运动,即股票的对数收益率并不服从正态分布时的欧式期权价值评估的非参数估计 |
| 6. | In last chapter , a new conception and model for var , based on prediction are brought forward . finally , a kind of new kernel density estimating function , adapting to financial time series is employed to extend time series kernel density estimating model
文中最后一部分，从风险价值预测的角度出发，建立了基于var预测的概念和模型，提出了一种适合估计金融时间序列分布的核密度函数，并采用加权法推广了时间序列核密度估计模型 |
| 7. | The work in this thesis is based on three technologies of multivariable statistical process control ( mspc ) , the principal component analysis ( pca ) , the partial least squares ( pls ) and the kernel density estimate ( kde ) . the work involves the following contents
基于多元统计过程控制方法中的主元分析法，偏最小二乘法和核函数分析法这三种技术，本课题主要研究了以下内容： 1 )用面向对象的方法开发多元统计过程控制状态监测应用系统。 |
| 8. | This paper investigates the application of the multivariate statistical process monitoring and control technology , which employs both multiway principal component analysis ( mpca ) and kernel density estimation ( kde ) , to real time status monitoring and fault diagnosis of batch production processes
本文主要研究了运用多向主元分析法和核函数法概率密度估计相结合的多元统计过程监控技术对间歇生产过程进行实时的状态监测与故障诊断。 |
| 9. | One is the bss based on kernel density estimation ( kde ) and genetic algorithm ( ga ) , the other is the blind deconvolution based on high order cross cumulants and ga . without nlf , the performance of separation in both algorithms is independent with the kurtosis of the sources
两种算法的实现无需引入非线性函数,因此都与源信号的峭度性质无关;另外,选取全局搜索的遗传算法进行寻优,避免了梯度法搜索的局部性,使得算法均能收敛到问题的全局最优解。 |
