Computational Statistics & Data Analysis最新文献_第10页

Empirical likelihood in a partially linear single-index model with censored response data 有删减响应数据的部分线性单指数模型中的经验似然法

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-01-10 DOI: 10.1016/j.csda.2023.107912

Liugen Xue

{"title":"Empirical likelihood in a partially linear single-index model with censored response data","authors":"Liugen Xue","doi":"10.1016/j.csda.2023.107912","DOIUrl":"10.1016/j.csda.2023.107912","url":null,"abstract":"<div>An empirical likelihood (EL) approach for a partial linear single-index model with censored response data is studied. A bias-corrected EL ratio is proposed, and the asymptotic chi-squared distribution of this ratio is obtained. The result can be directly used to construct the confidence regions of the regression parameters. The estimators of regression parameters and link function are constructed, and their asymptotic distributions are obtained. Also, a confidence band of the link function is constructed. The proposed method has two main features: The first feature is that the EL ratio is calibrated directly from within, instead of multiplying an adjustment factor by an EL ratio, which reflects the nature of EL. The second feature is avoiding undersmoothing of nonparametric functions, thus ensuring that the <math><msqrt><mrow><mi>n</mi></mrow></msqrt></math>-consistency of the parameter estimator. As a byproduct, the EL and estimation of a single-index model with censored response data are studied. The performance of the bias-corrected EL is evaluated by the simulation studies. The proposed method is illustrated with an example of a real data analysis.</div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized latent space model for one-mode networks with awareness of two-mode networks 具有双模网络意识的单模网络广义潜空间模型

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-01-10 DOI: 10.1016/j.csda.2023.107915

Xinyan Fan , Kuangnan Fang , Dan Pu , Ruixuan Qin

{"title":"Generalized latent space model for one-mode networks with awareness of two-mode networks","authors":"Xinyan Fan , Kuangnan Fang , Dan Pu , Ruixuan Qin","doi":"10.1016/j.csda.2023.107915","DOIUrl":"10.1016/j.csda.2023.107915","url":null,"abstract":"<div>Latent space models have been widely studied for one-mode networks, in which the same type of nodes connect with each other. In many applications, one-mode networks are often observed along with two-mode networks, which reflect connections between different types of nodes and provide important information for understanding the one-mode network structure. However, the classical one-mode latent space models have several limitations in incorporating two-mode networks. To address this gap, a generalized latent space model is proposed to capture common structures and heterogeneous connecting patterns across one-mode and two-mode networks. Specifically, each node is embedded with a latent vector and network-specific degree parameters that determine the connection probabilities between nodes. A projected gradient descent algorithm is developed to estimate the latent vectors and degree parameters. Moreover, the theoretical properties of the estimators are established and it has been proven that the estimation accuracy of the shared latent vectors can be improved through incorporating two-mode networks. Finally, simulation studies and applications on two real-world datasets demonstrate the usefulness of the proposed model.</div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139455082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Oracle-efficient estimation and trend inference in non-stationary time series with trend and heteroscedastic ARMA error 具有趋势和异方差 ARMA 误差的非平稳时间序列中的 Oracle 高效估计和趋势推断

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-01-09 DOI: 10.1016/j.csda.2024.107917

Chen Zhong

{"title":"Oracle-efficient estimation and trend inference in non-stationary time series with trend and heteroscedastic ARMA error","authors":"Chen Zhong","doi":"10.1016/j.csda.2024.107917","DOIUrl":"https://doi.org/10.1016/j.csda.2024.107917","url":null,"abstract":"<div>The non-stationary time series often contain an unknown trend and unobserved error terms. The error terms in the proposed model consist of a smooth variance function and the latent stationary ARMA series, which allows heteroscedasticity at different time points. The theoretically justified two-step B-spline estimation method is proposed for the trend and variance function in the model, and then residuals are obtained by removing the trend and variance function estimators from the data. The maximum likelihood estimator (MLE) for the latent ARMA error coefficients based on the residuals is shown to be oracally efficient in the sense that it has the same asymptotic distribution as the infeasible MLE if the trend and variance function were known. In addition to the oracle efficiency, a kernel estimator is obtained for the trend function and shown to converge to the Gumbel distribution. It yields an asymptotically correct simultaneous confidence band (SCB) for the trend function, which can be used to test the specific form of trend. A simulation-based procedure is proposed to implement the SCB, and simulation and real data analysis illustrate the finite sample performance.</div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139433912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Change point detection via feedforward neural networks with theoretical guarantees 通过具有理论保证的前馈神经网络进行变化点检测

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2024-01-09 DOI: 10.1016/j.csda.2023.107913

Houlin Zhou, Hanbing Zhu, Xuejun Wang

引用次数: 0

Group variable selection via group sparse neural network 通过群体稀疏神经网络选择群体变量

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2023-12-29 DOI: 10.1016/j.csda.2023.107911

Xin Zhang , Junlong Zhao

{"title":"Group variable selection via group sparse neural network","authors":"Xin Zhang , Junlong Zhao","doi":"10.1016/j.csda.2023.107911","DOIUrl":"10.1016/j.csda.2023.107911","url":null,"abstract":"<div>Group variable selection is an important issue in high-dimensional data modeling and most of existing methods consider only the linear model. Therefore, a new method based on the deep neural network (DNN), an increasingly popular nonlinear method in both statistics and deep learning communities, is proposed. The method is applicable to general nonlinear models, including the linear model as a special case. Specifically, a group sparse neural network (GSNN) is designed, where the definition of nonlinear group high-level features (NGHFs) is generalized to the network structure. A two-stage group sparse (TGS) algorithm is employed to induce group variables selection by performing group structure selection on the network. GSNN is promising for complex nonlinear systems with interactions and correlated predictors, overcoming the shortcomings of linear or marginal variable selection methods. Theoretical results on convergence and group-level selection consistency are also given. Simulations results and real data analysis demonstrate the superiority of our method.</div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139062656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HiQR: An efficient algorithm for high-dimensional quadratic regression with penalties HiQR：带惩罚的高维二次回归高效算法

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2023-12-28 DOI: 10.1016/j.csda.2023.107904

Cheng Wang , Haozhe Chen , Binyan Jiang

引用次数: 0

Subgroup detection based on partially linear additive individualized model with missing data in response 基于反应数据缺失的部分线性加法个体化模型的分组检测

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2023-12-20 DOI: 10.1016/j.csda.2023.107910

Tingting Cai , Jianbo Li , Qin Zhou , Songlou Yin , Riquan Zhang

{"title":"Subgroup detection based on partially linear additive individualized model with missing data in response","authors":"Tingting Cai , Jianbo Li , Qin Zhou , Songlou Yin , Riquan Zhang","doi":"10.1016/j.csda.2023.107910","DOIUrl":"https://doi.org/10.1016/j.csda.2023.107910","url":null,"abstract":"<div>Based on partially linear additive individualized model, a fusion-penalized inverse probability weighted least squares method is proposed to detect the subgroup for missing data in response. Firstly, the B-spline technique is used to approximate the unknown additive individualized functions and then an inverse probability weighted quadratic loss function is established with fusion penalty on the difference of subject-wise B-spline coefficients. Secondly, minimization of such quadratic loss function leads to the estimation of linear regression parameters and individualized B spline coefficients. With a proper tuning parameter, some differences in penalty term are shrunk into zero and thus the corresponding subjects will be clustered into the same subgroup. Thirdly, a clustering method is developed to automatically determine the subgroup membership for the subjects with missing data. Fourthly, large sample properties of resulting estimates are given under some regular conditions. Finally, numerical studies are presented to illustrate the performance of the proposed subgroup detection method.</div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138838653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discrepancy between structured matrices in the power analysis of a separability test 可分性检验功率分析中结构矩阵之间的差异

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2023-12-13 DOI: 10.1016/j.csda.2023.107907

Katarzyna Filipiak , Daniel Klein , Monika Mokrzycka

{"title":"Discrepancy between structured matrices in the power analysis of a separability test","authors":"Katarzyna Filipiak , Daniel Klein , Monika Mokrzycka","doi":"10.1016/j.csda.2023.107907","DOIUrl":"10.1016/j.csda.2023.107907","url":null,"abstract":"<div>An important task in the analysis of multivariate data is testing of the covariance matrix structure. In particular, for assessing separability, various tests have been proposed. However, the development of a method of measuring discrepancy between two covariance matrix structures, in relation to the study of the power of the test, remains an open problem. Therefore, a discrepancy measure is proposed such that for two arbitrary alternative hypotheses with the same value of discrepancy, the power of tests remains stable, while for increasing discrepancy the power increases. The basic hypothesis is related to the separable structure of the observation matrix under a doubly multivariate normal model, as assessed by the likelihood ratio and Rao score tests. It is shown that the particular one-parameter method and the Frobenius norm fail in the power analysis of tests, while the entropy and quadratic loss functions can be efficiently used to measure the discrepancy between separable and non-separable covariance structures for a multivariate normal distribution.</div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138680557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Laplace-based model with flexible tail behavior 具有灵活尾部行为的拉普拉斯模型

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2023-12-12 DOI: 10.1016/j.csda.2023.107909

Cristina Tortora , Brian C. Franczak , Luca Bagnato , Antonio Punzo

{"title":"A Laplace-based model with flexible tail behavior","authors":"Cristina Tortora , Brian C. Franczak , Luca Bagnato , Antonio Punzo","doi":"10.1016/j.csda.2023.107909","DOIUrl":"10.1016/j.csda.2023.107909","url":null,"abstract":"<div>The proposed multiple scaled contaminated asymmetric Laplace (MSCAL) distribution is an extension of the multivariate asymmetric Laplace distribution to allow for a different excess kurtosis on each dimension and for more flexible shapes of the hyper-contours. These peculiarities are obtained by working on the principal component (PC) space. The structure of the MSCAL distribution has the further advantage of allowing for automatic PC-wise outlier detection – i.e., detection of outliers separately on each PC – when convenient constraints on the parameters are imposed. The MSCAL is fitted using a Monte Carlo expectation-maximization (MCEM) algorithm that uses a Monte Carlo method to estimate the orthogonal matrix of eigenvectors. A simulation study is used to assess the proposed MCEM in terms of computational efficiency and parameter recovery. In a real data application, the MSCAL is fitted to a real data set containing the anthropometric measurements of monozygotic/dizygotic twins. Both a skewed bivariate subset of the full data, perturbed by some outlying points, and the full data are considered.</div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167947323002207/pdfft?md5=d2a7615bc71ed59a59a646714a4b93c6&pid=1-s2.0-S0167947323002207-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138680780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Graph-based spatial segmentation of areal data 基于图形的区域数据空间分割

IF 1.8 3区数学

Computational Statistics & Data Analysis Pub Date : 2023-12-12 DOI: 10.1016/j.csda.2023.107908

Vivien Goepp , Jan van de Kassteele

引用次数: 0