| 1. | The weighted kernel estimators of nonparametricregression function with censored data
截尾数据非参数回归函数加权核估计 |
| 2. | Establishes of model of quasi - parabola regressive function model and parametric estimation
伪抛物线回归函数模型的建立与参数估计 |
| 3. | Exponential bounds of mean error for the kernel regreeion estimates with directional data
方向数据回归函数核估计平均偏差的指数界 |
| 4. | Weighted kernel estimator of nonparametric regression functions with censored data of sequences
相依截尾数据非参数回归函数加权核估计 |
| 5. | Convergence rate of the estimate of the regression function under martingale difference sequence
误差为鞅差序列的回归函数估计的收敛速度 |
| 6. | Estimators of penalized least square for parametric regression and vectors and spline function can be got by compiled program
模拟计算表明，该方法适合于回归函数模型误差与测量系统误差的估计。 |
| 7. | In this paper , we discuss the estimate of regression function in nonparametric regression model based on exponential integral martingale difference
摘要本文研究了误差项是鞅差序列，且满足某种指数矩条件的非参数回归函数的估计。 |
| 8. | The essence of the above estimating methods is local estimator or local smoothing technique . in general , the non - parametric regression function is . well estimated by the above methods when the covariable x is one dimension
这些方法本质上讲都是局部估计或局部光滑，当回归变量x为一维变量时，非参数回归函数用这些方法一般都能得到很好的估计。 |
| 9. | In the nonparametric regression the regression function is supposed to be from some function family , such as the smoothing functions . so the nonparametric regression needs few hypotheses and is very robust
非参数回归一般假定回归函数属于某一个函数类，如常常假定回归函数是一个光滑的函数，因此非参数回归对模型的假设很少，最主要的优点就是模型具有稳健性。 |
| 10. | Satisfactory results were obtained . by combining the traditional neural network method with the svr theory , this paper also addresses the problem of boundary value problems and replaces the neural network function with svr regression function
此外，在本课题中，我们把传统的神经网络解边值问题的方法和svr理论相结合，用svr中的回归函数代替神经网络函数，对边值问题的求解进行了研究。 |
