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学者姓名：吴密霞
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Abstract ：
The Expectation Maximization (EM) algorithm is widely used in latent variable model inference. However, when data are distributed across various locations, directly applying the EM algorithm can often be impractical due to communication expenses and privacy considerations. To address these challenges, a communication-efficient distributed EM algorithm is proposed. Under mild conditions, the proposed estimator achieves the same mean squared error bound as the centralized estimator. Furthermore, the proposed method requires only one extra round of communication compared to the Average estimator. Numerical simulations and a real data example demonstrate that the proposed estimator significantly outperforms the Average estimator in terms of mean squared errors.
Keyword ：
Latent variable models Latent variable models EM algorithm EM algorithm Distributed inference Distributed inference Communication-efficient Communication-efficient
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| GB/T 7714 | Liu, Xirui , Wu, Mixia , Xu, Liwen . Communication-efficient distributed EM algorithm [J]. | STATISTICAL PAPERS , 2024 . |
| MLA | Liu, Xirui 等. "Communication-efficient distributed EM algorithm" . | STATISTICAL PAPERS (2024) . |
| APA | Liu, Xirui , Wu, Mixia , Xu, Liwen . Communication-efficient distributed EM algorithm . | STATISTICAL PAPERS , 2024 . |
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Abstract ：
The rank-tracking probability (RTP) is a useful statistical index for measuring the 'tracking ability' of longitudinal disease risk factors in biomedical studies. Two existing unstructured smoothing methods for estimating the RTP in literature require sufficient observations at any two design time points, which may be violated in practice. We consider the dynamic estimation methods based on semiparametric copula modeling and smoothing method and compare the corresponding estimators under three smoothing ways: parametric-smoothing (C-PAS), probability-smoothing (C-PRS) and RTP-smoothing (C-RS). The proposed estimators are consistent under some mild assumptions when the true copula model is known. Among them, the C-PAS, though performs best under the true copula model, is not robust for the copula model selection. The C-PRS and C-RS estimators are preferred under the selected copula model, which have much smaller mean squared errors than unstructured smoothing methods. Moreover, the C-RS estimator performs slightly better than C-PRS estimator. Some numerical simulations and an application of a longitudinal epidemiological study are carried out to illustrate the performances of the proposed methods.
Keyword ：
two-step smoothing two-step smoothing semiparametric copulal semiparametric copulal kernel smoothing kernel smoothing rank-tracking probability rank-tracking probability
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| GB/T 7714 | Zhang, Xiaoyu , Wu, Mixia , Wu, Colin O. . Dynamic copula-based methods for estimating rank-tracking probabilities with longitudinal data [J]. | STAT , 2023 , 12 (1) . |
| MLA | Zhang, Xiaoyu 等. "Dynamic copula-based methods for estimating rank-tracking probabilities with longitudinal data" . | STAT 12 . 1 (2023) . |
| APA | Zhang, Xiaoyu , Wu, Mixia , Wu, Colin O. . Dynamic copula-based methods for estimating rank-tracking probabilities with longitudinal data . | STAT , 2023 , 12 (1) . |
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Abstract ：
Receiver operating characteristic (ROC) curve is a popular tool to describe and compare the diagnostic accuracy of biomarkers when a binary-scale gold standard is available. However, there are many examples of diagnostic tests whose gold standards are continuous. Hence, Several extensions of receiver operating characteristic (ROC) curve are proposed to evaluate the diagnostic potential of biomarkers when the gold standard is continuous-scale. Moreover, in evaluating these biomarkers, it is often necessary to consider the effects of covariates on the diagnostic accuracy of the biomarker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. Applying the covariate adjustment to the case that the gold standard is continuous is challenging and has not been addressed in the literature. To fill the gap, we propose two general testing frameworks to account for the covariates effect on diagnostic accuracy. Simulation studies are conducted to compare the proposed tests. Data from a study that assessed three types of imaging modalities with the purpose of detecting neoplastic colon polyps and cancers are used to illustrate the proposed methods.
Keyword ：
diagnostic accuracy diagnostic accuracy biomarkers biomarkers covariate adjustment covariate adjustment regression analysis regression analysis
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| GB/T 7714 | Wu, Mixia , Sun, Xian , Liu, Aiyi et al. Significance tests for covariates in the diagnostic accuracy index of a biomarker against a continuous gold standard [J]. | STATISTICS IN MEDICINE , 2023 , 42 (22) : 4015-4027 . |
| MLA | Wu, Mixia et al. "Significance tests for covariates in the diagnostic accuracy index of a biomarker against a continuous gold standard" . | STATISTICS IN MEDICINE 42 . 22 (2023) : 4015-4027 . |
| APA | Wu, Mixia , Sun, Xian , Liu, Aiyi , Peng, Chenchen , Li, Zhaohai . Significance tests for covariates in the diagnostic accuracy index of a biomarker against a continuous gold standard . | STATISTICS IN MEDICINE , 2023 , 42 (22) , 4015-4027 . |
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Abstract ：
本文研究了响应变量随机缺失时部分线性空间自回归模型的估计问题.结合B样条方法,我们给出了该模型参数部分和非数部分的极大似然估计的EM算法、伪限制极大似然估计的EM算法、以及边际极大似然估计算法,并通过数值模拟比较了三种估计和相应算法在不同的样本容量、缺失比例及空间权重矩阵下数值表现.最后,通过一个实际例子进一步验证三种方法的优良性.
Keyword ：
EM算法 EM算法 空间自回归 空间自回归 样条逼近 样条逼近 缺失数据 缺失数据 似然方法 似然方法
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| GB/T 7714 | 马万霞 , 吴密霞 , 罗国旺 . 缺失数据下半参数空间自回归模型的估计 [J]. | 数学学报(中文版) , 2023 , 66 (06) : 1031-1044 . |
| MLA | 马万霞 et al. "缺失数据下半参数空间自回归模型的估计" . | 数学学报(中文版) 66 . 06 (2023) : 1031-1044 . |
| APA | 马万霞 , 吴密霞 , 罗国旺 . 缺失数据下半参数空间自回归模型的估计 . | 数学学报(中文版) , 2023 , 66 (06) , 1031-1044 . |
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Abstract ：
本文考虑多源异质大数据下线性模型的分布式统计推断问题.首先,提出针对模型参数的通信有效的分布式聚合估计及算法,并在一些正则条件下证明所得到的估计量的最优性和渐近正态性.其次,针对模型中的异质性检验问题,给出了分布式检验方法.最后,通过数值模拟研究,对本文所提出估计和检验方法的优良性进行验证.
Keyword ：
分布式统计推断 分布式统计推断 多源数据 多源数据 大数据 大数据 异质性 异质性 线性模型 线性模型
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| GB/T 7714 | 杨鑫 , 吴密霞 . 多源异质大数据线性模型的分布式统计推断 [J]. | 数学学报 , 2023 , 66 (2) : 263-276 . |
| MLA | 杨鑫 et al. "多源异质大数据线性模型的分布式统计推断" . | 数学学报 66 . 2 (2023) : 263-276 . |
| APA | 杨鑫 , 吴密霞 . 多源异质大数据线性模型的分布式统计推断 . | 数学学报 , 2023 , 66 (2) , 263-276 . |
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Abstract ：
本文研究了响应变量随机缺失时部分线性空间自回归模型的估计问题.结合B样条方法,我们给出了该模型参数部分和非数部分的极大似然估计的EM算法、伪限制极大似然估计的EM算法、以及边际极大似然估计算法,并通过数值模拟比较了三种估计和相应算法在不同的样本容量、缺失比例及空间权重矩阵下数值表现.最后,通过一个实际例子进一步验证三种方法的优良性.
Keyword ：
EM算法 EM算法 缺失数据 缺失数据 空间自回归 空间自回归 似然方法 似然方法 样条逼近 样条逼近
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| GB/T 7714 | 马万霞 , 吴密霞 , 罗国旺 . 缺失数据下半参数空间自回归模型的估计 [J]. | 数学学报 , 2023 , 66 (6) : 1031-1044 . |
| MLA | 马万霞 et al. "缺失数据下半参数空间自回归模型的估计" . | 数学学报 66 . 6 (2023) : 1031-1044 . |
| APA | 马万霞 , 吴密霞 , 罗国旺 . 缺失数据下半参数空间自回归模型的估计 . | 数学学报 , 2023 , 66 (6) , 1031-1044 . |
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Abstract ：
In this paper, linear spatial autoregressive (SAR) models with covariate measurement errors are studied. A three-stage least squares (3SLS) estimation method both with Berkson's and classical types of instrumental variables is proposed and asymptotic normality of the proposed estimator using each type of instrumental variables is derived under mild conditions. Simulation studies are conducted to investigate the finite sample performance of the proposed estimator. A real data example is used to illustrate the developed method. (c) 2022 Elsevier Inc. All rights reserved.
Keyword ：
Measurement error Measurement error Spatial data Spatial data Instrumental variable Instrumental variable Three-stage least squares (3SLS) Three-stage least squares (3SLS)
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| GB/T 7714 | Luo, Guowang , Wu, Mixia , Pang, Zhen . Estimation of spatial autoregressive models with covariate measurement errors [J]. | JOURNAL OF MULTIVARIATE ANALYSIS , 2022 , 192 . |
| MLA | Luo, Guowang et al. "Estimation of spatial autoregressive models with covariate measurement errors" . | JOURNAL OF MULTIVARIATE ANALYSIS 192 (2022) . |
| APA | Luo, Guowang , Wu, Mixia , Pang, Zhen . Estimation of spatial autoregressive models with covariate measurement errors . | JOURNAL OF MULTIVARIATE ANALYSIS , 2022 , 192 . |
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Abstract ：
多均值变点估计问题是目前统计界的一个热点问题。文献中已有多种算法处理该问题，其中筛选排序算法(Screening and Ranking algorithm, SaRa)由于具有快速检测和高精度的特点而被广泛关注。值得注意的是，该算法在筛选步骤的阈值选取倾向于保守，其主要原因是SaRa算法中方差参数采用了分段方法进行估计。本文的主要目的是改进多均值变点估计的SaRa算法。首先，运用局部多项式结合交叉验证方法给出了误差标准差的一个全局估计，并将其应用于初筛变点步骤中。然后，通过对候选变点的局部诊断函数值进行排序，进而结合MBIC准则得到了最终的变点估计。数值模拟结果显示了本文所提出的改进的SaR...
Keyword ：
MBIC准则 MBIC准则 筛选排序 筛选排序 多均值变点 多均值变点 交叉验证 交叉验证 局部多项式估计 局部多项式估计
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| GB/T 7714 | 李扬 , 吴密霞 , 胡尧 et al. 基于筛选排序算法的多均值变点估计 [J]. | 工程数学学报 , 2022 , 39 (03) : 401-412 . |
| MLA | 李扬 et al. "基于筛选排序算法的多均值变点估计" . | 工程数学学报 39 . 03 (2022) : 401-412 . |
| APA | 李扬 , 吴密霞 , 胡尧 , 杨超 . 基于筛选排序算法的多均值变点估计 . | 工程数学学报 , 2022 , 39 (03) , 401-412 . |
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Abstract ：
In this paper empirical likelihood (EL)-based inference for a semiparametric varying-coefficient spatial autoregressive model is investigated. The maximum EL estimators for the parametric component and the nonparametric component are established. Furthermore, asymptotic properties of the proposed estimators and EL ratios are derived, and the corresponding confidence regions/bands are constructed. Their finite sample performances are studied via simulation and an example.
Keyword ：
instrumental variable instrumental variable empirical likelihood empirical likelihood Wilks theorem Wilks theorem Confidence regions Confidence regions residual-adjusted residual-adjusted
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| GB/T 7714 | Luo Guowang , Wu Mixia , Pang Zhen . Empirical Likelihood Inference for the Semiparametric Varying-Coefficient Spatial Autoregressive Model [J]. | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2021 , 34 (6) : 2310-2333 . |
| MLA | Luo Guowang et al. "Empirical Likelihood Inference for the Semiparametric Varying-Coefficient Spatial Autoregressive Model" . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY 34 . 6 (2021) : 2310-2333 . |
| APA | Luo Guowang , Wu Mixia , Pang Zhen . Empirical Likelihood Inference for the Semiparametric Varying-Coefficient Spatial Autoregressive Model . | JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY , 2021 , 34 (6) , 2310-2333 . |
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Abstract ：
In this paper, an IPW-based robust estimator is developed for the spatial autoregressive model with response missing at random. Its consistency and asymptotical normality are proved and its finite-sample performance is investigated by simulations. (C) 2021 Elsevier B.V. All rights reserved.
Keyword ：
Spatial data Spatial data Propensity score Propensity score Autoregressive model Autoregressive model Missing data Missing data Instrumental variable Instrumental variable
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| GB/T 7714 | Luo, Guowang , Wu, Mixia , Xu, Liwen . IPW-based robust estimation of the SAR model with missing data [J]. | STATISTICS & PROBABILITY LETTERS , 2021 , 172 . |
| MLA | Luo, Guowang et al. "IPW-based robust estimation of the SAR model with missing data" . | STATISTICS & PROBABILITY LETTERS 172 (2021) . |
| APA | Luo, Guowang , Wu, Mixia , Xu, Liwen . IPW-based robust estimation of the SAR model with missing data . | STATISTICS & PROBABILITY LETTERS , 2021 , 172 . |
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