新型冠状病毒肺炎疫情预测预报的非线性回归方法

本文介绍几种累计新型冠状病毒肺炎(COVID-19)疫情预测预报中的非线性增长曲线,并说明Richards增长曲线在这次COVID-19疫情预测预报中的合理性和可行性;在此基础上,建立累计COVID-19疫情预测预报中的非线性回归点模型,并给出参数估计方法;对全国COVID-19疫情进行即时跟踪预测预报,包括数据校准、整体和分时间段的预测预报,同时获得全国COVID-19疫情随时间的预测预报结果,为进一步的疫情防控打下良好基础.

EMPIRICAL LIKELIHOOD CONFIDENCE REGION FOR PARAMETERS IN LINEAR ERRORS-IN-VARIABLES MODELS WITH MISSING DATA

multivariate 线性 errors-in-variables 模型 regressors 什么时候在在 Rubin (1976 ) 的意义的随机是失踪的，在这份报纸被考虑。为 0 在这建模的一个参数的一个抑制实验可能性的信心区域被建议，它被基于反的概率把相应于加权的摆平的直角的距离的 20 个函数与 0 的一个抑制区域相结合构造。在真参数的实验木头可能性的比率收敛到标准 chi 平方分发，这被显示出。模拟证明建议信心区域的范围率接近名字的水平，信心间隔的长度是比反的概率的正常近似的那些狭窄的在大多数情况中的加权的调整最不方形的评估者。一个真实例子被学习，结果支持理论和模拟结论。

RCV-based error density estimation in the ultrahigh dimensional additive model

In this paper,we mainly study how to estimate the error density in the ultrahigh dimensional sparse additive model,where the number of variables is larger than the sample size.First,a smoothing method based on B-splines is applied to the estimation of regression functions.Second,an improved two-stage refitted crossvalidation(RCV)procedure by random splitting technique is used to obtain the residuals of the model,and then the residual-based kernel method is applied to estimate the error density function.Under suitable sparse conditions,the large sample properties of the estimator,including the weak and strong consistency,as well as normality and the law of the iterated logarithm,are obtained.Especially,the relationship between the sparsity and the convergence rate of the kernel density estimator is given.The methodology is illustrated by simulations and a real data example,which suggests that the proposed method performs well.

The abstract of doctoral dissertation‘Some research on hypothesis testing and nonparametric variable screening problems for high dimensional data’

In this thesis,we construct test statistic for association test and independence test in high dimension,respectively,and study the corresponding theoretical properties under some regularity conditions.Meanwhile,we propose a nonparametric variable screening procedure for sparse additive model with multivariate response in untra-high dimension and established some screening properties.

ON WEIGHTED RANDOMLY TRIMMED MEANS

称为介绍的加权的随机整修的 meansare 和不仅他们的一致性和 asymptotic 规度的柔韧的地点评估者的一个班被证明，但是 theirinfluence 工作 asymptotic 变化和故障点也被导出。他们拥有是的 thesame 故障点中部，并且他们中的一些拥有更高的 asymptotic 亲戚效率 atthe 重尾巴的分布比一些另外的著名地点评估者；而 trimmedmeans， Winsorized 工具和 Huber “ s M 评估者拥有更高的 asymptotic 亲戚效率 atthe 跟踪光的分布，在哪个 Huber ” s M 评估者是最柔韧的。