Query：
学者姓名：吴密霞
Refining：
Year
Type
Indexed by
Source
Complex
Co-Author
Language
Clean All
Abstract ：
In this paper, bias-corrected instrumental variable estimation methods, specifically the bias- corrected two-stage least square (2SLS) estimation and the bias-corrected asymptotically best 2SLS estimation, are proposed for spatial autoregressive (SAR) models with covariate measurement errors, utilizing available information regarding the variance of the measurement error. Under mild assumptions, the consistency and asymptotic normality of the proposed estimators are derived. Simulation studies further reveal that the proposed methods exhibit robustness regardless of the presence of spatial dependence in the model. Additionally, a real data example is utilized to illustrate the developed methods.
Keyword ：
Bias-corrected Bias-corrected Measurement error Measurement error Instrumental variable Instrumental variable Spatial data Spatial data
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Luo, Guowang , Wu, Mixia . Bias-corrected instrumental variable estimation for spatial autoregressive models with measurement errors [J]. | SPATIAL STATISTICS , 2025 , 65 . |
| MLA | Luo, Guowang 等. "Bias-corrected instrumental variable estimation for spatial autoregressive models with measurement errors" . | SPATIAL STATISTICS 65 (2025) . |
| APA | Luo, Guowang , Wu, Mixia . Bias-corrected instrumental variable estimation for spatial autoregressive models with measurement errors . | SPATIAL STATISTICS , 2025 , 65 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Xirui , Wu, Mixia , Xu, Liwen . Communication-efficient distributed EM algorithm [J]. | STATISTICAL PAPERS , 2024 , 65 (9) : 5575-5592 . |
| MLA | Liu, Xirui 等. "Communication-efficient distributed EM algorithm" . | STATISTICAL PAPERS 65 . 9 (2024) : 5575-5592 . |
| APA | Liu, Xirui , Wu, Mixia , Xu, Liwen . Communication-efficient distributed EM algorithm . | STATISTICAL PAPERS , 2024 , 65 (9) , 5575-5592 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
In this paper, we consider the distributed inference for heterogeneous linear models with massive datasets. Noting that heterogeneity may exist not only in the expectations of the subpopulations, but also in their variances, we propose the heteroscedasticity-adaptive distributed aggregation (HADA) estimation, which is shown to be communication-efficient and asymptotically optimal, regardless of homoscedasticity or heteroscedasticity. Furthermore, a distributed test for parameter heterogeneity across subpopulations is constructed based on the HADA estimator. The finite-sample performance of the proposed methods is evaluated using simulation studies and the NYC flight data.
Keyword ：
Levene's test Levene's test massive heterogeneous data massive heterogeneous data Distributed estimation Distributed estimation heterogeneity heterogeneity
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Xin , Yan, Qi Jing , Wu, Mi Xia . Adaptive Distributed Inference for Multi-source Massive Heterogeneous Data [J]. | ACTA MATHEMATICA SINICA-ENGLISH SERIES , 2024 , 40 (11) : 2751-2770 . |
| MLA | Yang, Xin 等. "Adaptive Distributed Inference for Multi-source Massive Heterogeneous Data" . | ACTA MATHEMATICA SINICA-ENGLISH SERIES 40 . 11 (2024) : 2751-2770 . |
| APA | Yang, Xin , Yan, Qi Jing , Wu, Mi Xia . Adaptive Distributed Inference for Multi-source Massive Heterogeneous Data . | ACTA MATHEMATICA SINICA-ENGLISH SERIES , 2024 , 40 (11) , 2751-2770 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Identifying multiple change points in the mean and/or variance is crucial across various fields, including finance and quality control. We introduce a novel technique that detects change points for the mean and/or variance of a noisy sequence and constructs confidence intervals for both the mean and variance of the sequence. This method integrates the weighted bootstrap with the Sequential Binary Segmentation (SBS) algorithm. Not only does our technique pinpoint the location and number of change points, but it also determines the type of change for each estimated point, specifying whether the change occurred in the mean, variance, or both. Our simulations show that our method outperforms other approaches in most scenarios, clearly demonstrating its superiority. Finally, we apply our technique to three datasets, including DNA copy number variation, stock volume, and traffic flow data, further validating its practical utility and wide-ranging applicability.
Keyword ：
Mean-variance change points Mean-variance change points the SBS algorithm the SBS algorithm the intensity score the intensity score confidence interval confidence interval the weighted bootstrap the weighted bootstrap
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Yang , Wu, Mixia , Ding, Wenxin . Bootstrap-based inference for multiple mean-variance changepoint models [J]. | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , 2024 , 95 (1) : 70-95 . |
| MLA | Li, Yang 等. "Bootstrap-based inference for multiple mean-variance changepoint models" . | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 95 . 1 (2024) : 70-95 . |
| APA | Li, Yang , Wu, Mixia , Ding, Wenxin . Bootstrap-based inference for multiple mean-variance changepoint models . | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , 2024 , 95 (1) , 70-95 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 等. "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 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Export
| Results： |
Selected to |
| Format： |
