具有随机删失数据的部分线性模型的拟合优度检验的渐近性质

研究随机删失部分线性回归模型的假设检验问题. 提出了一个检验统计量来检验数据是否满足一个部分线性回归模型, 它是基于残差的cusum过程的平方形式. 研究了零假设下和局部对立假设下检验统计量的渐近分布. 数值模拟表明该检验方法有好的检验功效.     

信息右删失数据下比例风险模型的估计问题（英文）

在生存分析中,对右删失数据问题的研究常假设删失时间与失效时间相互独立.然而研究者经常要面对非独立删失的问题,即删失时间与失效时间可能相互关联并彼此影响,尤其表现在临床试验中.如果不考虑这种相关性,便无法得到生存函数的有效估计.针对这种相依结构已有很多处理方法,其中连接函数因结构简单而尤为受到关注.本文主要对信息右删失数据下比例风险模型的相关估计问题进行了研究.利用阿基米德连接函数对删失时间和失效时间的联合分布函数进行假定,在连接函数参数的可识别条件下,得到了连接函数的参数、比例风险模型参数以及基准累积风险函数的极大似然估计,并通过模拟计算的方法验证了估计方法的可行性以及估计量的有效性.     

相依删失下基于连接函数的参数模型的统计分析

在临床数据的收集中,由于竞争性风险或者病人的退出可能导致数据删失.删失数据的统计分析大多是基于独立删失的假定进行的.而实际情况中,数据的删失往往是非独立的,即删失变量和失效时间变量是相关的.相依删失使得原本复杂的删失数据处理变得更加困难.在本文中,假定删失变量和失效时间变量的联合分布可以用它们边际分布的连接函数函数表示,在给定连接函数下,得到了比例风险模型的极大似然估计.模拟计算显示,如果删失假定成立,本文所采用方法比独立删失假定下的估计方法更准确.     

长度偏差右删失数据下分位数回归的估计方程方法

本文首先建立左截断右删失数据下的一般分位数回归方法.当截断变量服从均匀分布时,左截断右删失数据变成长度偏差右删失数据.长度偏差数据因其特殊性,提供了更多的信息.当把适用于左截断右删失数据的一般方法用到长度偏差右删失数据时,得到的估计量并不有效,这是因为它们没有利用该数据的特殊结构.为了提高效率,本文提出复合估计方程方法来解决长度偏差右删失数据下的分位数回归问题,这种方法并不需要估计删失变量的分布.所提出的估计方程可以通过一个求L_1型凸函数最小值的简单算法来求解.本文用经验过程和随机积分的技巧建立了所提出估计量的一致相合性和弱收敛性.随机模拟验证了所提出方法在有限样本时的表现,并且给出了实例分析.     

超高维Ⅱ型区间删失数据的非参数变量筛选法北大核心CSCD

在定期随访的医学研究或临床实验中,人们经常会收集到高维区间删失数据,如何对这类数据进行降维是一个非常有意义的问题.本文基于Kolmogorov-Smirnov检验统计量,利用分割和融合的技巧,把独立特征筛选方法推广到区间删失数据中,提出了一种可以处理超高维Ⅱ型区间删失数据且不依赖于任何模型假设的变量筛选方法.此方法的适用范围很广,可以有效地处理各种生存模型下的超高维Ⅱ型区间删失数据,而且可以处理离散型,连续型等多种类型的协变量.在估计生存函数时,本文采用EM-ICM算法,极大地提高了计算效率.大量的数值模拟实验验证了此方法在有限样本下的有效性.     

随机删失下伽玛分布族参数的经验Bayes双边检验

利用经验贝叶斯方法研究了伽玛分布参数的双边检验问题,构造了一个在历史样本被随机右删失的条件下参数的经验Bayes检验函数,在适当的条件下证明了所提出的经验Bayes检验函数的渐近最优性,并获得了它的收敛速度可任意接近O(n~(-1/2)).     

长度偏差右删失数据下比例均值剩余寿命模型的估计方法

本文考虑了长度偏差右删失数据下均值剩余寿命模型的统计推断.当截断变量满足平稳性假设时,长度偏差右删失数据比左截断右删失数据具有更多的信息.为了提高参数估计的效率,我们在估计方程构造中添加了额外信息,通过组合方法获得了新的估计.模拟研究的结果也表明,组合估计方程的方法比仅考虑左截断右删失数据的方法更有效,结果表现更好.     

长度偏差右删失数据下[1mm]比例均值剩余寿命模型的估计方法

本文考虑了长度偏差右删失数据下均值剩余寿命模型的统计推断.当截断变量满足平稳性假设时,长度偏差右删失数据比左截断右删失数据具有更多的信息.为了提高参数估计的效率,我们在估计方程构造中添加了额外信息,通过组合方法获得了新的估计.模拟研究的结果也表明,组合估计方程的方法比仅考虑左截断右删失数据的方法更有效,结果表现更好.     

随机删失下刻度参数的单调经验贝叶斯检验

利用经验贝叶斯方法研究了刻度指数族的两行动问题, 提出了一个在历史样本被随机右删失的条件下收敛速度可以任意接近$O(n^{-1})$的单调经验贝叶斯检验.     

多重Ⅱ型删失数据的近似似然函数及应用

多重Ⅱ型删失数据是一种很常见的数据删失类型，处理起来也非常困难，本文获得了多重Ⅱ型删失数据的一种近似似然函数，并证明了在大样本场合下，这种近似与似然函数是等价的。基于该近似似然函数，求得了参数的近似极大似然估计与近似Bayes估计，并讨论似极大似然估计的性质。     

Tests of fit for the Rayleigh distribution based on the empirical Laplace transform

In this paper a class of goodness-of-fit tests for the Rayleigh distribution is proposed. The tests are based on a weighted integral involving the empirical Laplace transform. The consistency of the tests as well as their asymptotic distribution under the null hypothesis are investigated. As the decay of the weight function tends to infinity the test statistics approach limit values. In a particular case the resulting limit statistic is related to the first nonzero component of Neyman’s smooth test for this distribution. The new tests are compared with other omnibus tests for the Rayleigh distribution.     

Intermediate Approach to Comparison of Some Goodness-of-Fit Tests

In this paper we present the intermediate approach to investigating asymptotic power and measuring the efficiency of nonparametric goodness-of-fit tests for testing uniformity. Contrary to the classical Pitman approach, the intermediate approach allows the explicit quantitative comparison of powers and calculation of efficiencies. For standard tests, like the Cramér-von Mises test, an intermediate approach gives conclusions consistent with qualitative results obtained using the Pitman approach. For other more complicated cases the Pitman approach does not give the right picture of power behaviour. An example is the data driven Neyman test we present in this paper. In this case the intermediate approach gives results consistent with finite sample results. Moreover, using this setting, we prove that the data driven Neyman test is asymptotically the most powerful and efficient under any smooth departures from uniformity. This result shows that, contrary to classical tests being efficient and the most powerful under one particular type of departure from uniformity, the new test is an adaptive one.     

A Data Driven Smooth Test for Circular Uniformity

We propose a new omnibus test for uniformity on the circle. The new test is based upon the idea of data driven smooth tests as presented in Ledwina (1994, J. Amer. Statist. Assoc., 89, 1000–1005). Our simulations indicate that the test performs very well for multifarious alternatives. In particular, it seems to outperform other known omnibus tests when testing against multimodal alternatives. We also investigate asymptotic properties of our test and we prove that it is consistent against every departure from uniformity.     

非平稳过程的小波因果度量

该文对带有随机趋势的非平稳过程给出了一种新的因果度量定义.作者将证明新定义与Hosoya给出的因果度量定义等价, 但由于避免了以往文献中常见的对非平稳过程协整性的要求, 该定义有利于简化因果关系的假设检验.文中还对Wald检验和似然比检验进行了讨论.数值模拟和实证分析表明, 这两种检验方法都是有效的.     

Sign-based Test for Mean Vector in High-dimensional and Sparse Settings

In this article, we introduce a robust sparse test statistic which is based on the maximum type statistic. Both the limiting null distribution of the test statistic and the power of the test are analysed. It is shown that the test is particularly powerful against sparse alternatives. Numerical studies are carried out to examine the numerical performance of the test and to compare it with other tests available in the literature. The numerical results show that the test proposed significantly outperforms those tests in a range of settings, especially for sparse alternatives.     

Tests for normality based on density estimators of convolutions

Recent results show that densities of convolutions can be estimated by local U-statistics at the root-n rate in various norms. Motivated by this and the fact that convolutions of normal densities are normal, we introduce new tests for normality which use as test statistics weighted L₁-distances between the standard normal density and local U-statistics based on standardized observations. We show that such test statistics converge at the root-n rate and determine their limit distributions as functionals of Gaussian processes. We also address a choice of bandwidth. Simulations show that our tests are competitive with other tests of normality.     

TESTING THE ADEQUACY OF GARCH-TYPE MODELS IN TIME SERIES

In this article a new approach for checking the adequacy of GARCH-type models in time series was proposed. The resulted tests involve weight functions, which provide them with the flexibility in choosing scores to enhance power performance. The choice of weight functions and the power properties of the tests are studied. For a large number of alternatives, asymptotically distribution-free maximin test is constructed. The tests are asymptotically chi-squared under the null hypothesis and easy to implement. Simulation results indicate that the tests perform well.     

Some Simple Test Procedures for Normal Mean Vector with Incomplete Data

The problem of testing normal mean vector when the observations are missing from subsets of components is considered. For a data matrix with a monotone pattern, three simple exact tests are proposed as alternatives to the traditional likelihood ratio test. Numerical power comparisons between the proposed tests and the likelihood ratio test suggest that one of the proposed tests is indeed comparable to the likelihood ratio test and the other two tests perform better than the likelihood ratio test over a part of the parameter space. The results are extended to a nonmonotone pattern and illustrated using an example.     

一类相合的分位点型检验

广义非参数似然比检验统计量是一类很广的统计量,包含了众多重要的检验统计量,如Anderson-Darling(AD)等.利用Rubin的随机经验分布函数替代经验分布函数的方法,得到了广义非参数似然比检验统计量的新版本,构造了新的检验统计量.由于新的检验统计量在给定样本下仍然是随机变量,选择了它的分位点和期望作为检验统计量,分别称之为分位点型检验统计量和期望型检验统计量.在简单假设情况下,证明了分位点型检验统计量和期望型检验统计量在固定备择下的相合性.模拟结果显示,在某些备择下,新的检验的功效明显高于原有的基于经验分布函数的检验的功效.     

Nonlinearity tests for bilinear systems

The purpose of this paper is to develop nonlinearity tests for open-loop bilinear systems. Lagrange multiplier tests of linear systems against a bilinear alternative are proposed. A simulation study is performed to check the validity of the asymptotic null distributions of the test statistics and to investigate the power characteristics of the tests. Two recent nonlinearity tests in the time-series context are adapted to linear systems and compared with Lagrange multiplier tests. Simulation results show that the proposed Lagrange multiplier tests are more powerful than the other tests.