Statistics and Its Interface最新文献_第4页

Sieve maximum likelihood estimation for generalized linear mixed models with an unknown link function 具有未知链接函数的广义线性混合模型的筛极大似然估计

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-11-27 DOI: 10.4310/23-sii813

Guoqing Diao, Mengdie Yuan

引用次数: 0

Abnormal sample detection based on robust Mahalanobis distance estimation in adversarial machine learning 对抗机器学习中基于鲁棒马氏距离估计的异常样本检测

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-11-27 DOI: 10.4310/23-sii818

Wan Tian, Lingyue Zhang, Hengjian Cui

引用次数: 0

Hankel low-rank approximation and completion in time series analysis and forecasting: a brief review 汉克尔低秩逼近与补全在时间序列分析与预测中的应用综述

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-04-13 DOI: 10.4310/22-sii735

Jonathan Gillard, Konstantin Usevich

引用次数: 0

Approximate hidden semi-Markov models for dynamic connectivity analysis in resting-state fMRI 静息状态fMRI动态连通性分析的近似隐半马尔可夫模型

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-04-13 DOI: 10.4310/22-sii730

Mark B. Fiecas, Christian Coffman, Meng Xu, Timothy J. Hendrickson, Bryon A. Mueller, Bonnie Klimes-Dougan, Kathryn R. Cullen

{"title":"Approximate hidden semi-Markov models for dynamic connectivity analysis in resting-state fMRI","authors":"Mark B. Fiecas, Christian Coffman, Meng Xu, Timothy J. Hendrickson, Bryon A. Mueller, Bonnie Klimes-Dougan, Kathryn R. Cullen","doi":"10.4310/22-sii730","DOIUrl":"https://doi.org/10.4310/22-sii730","url":null,"abstract":"Motivated by a study on adolescent mental health, we conduct a dynamic connectivity analysis using resting-state functional magnetic resonance imaging (fMRI) data. A dynamic connectivity analysis investigates how the interactions between different regions of the brain, represented by the different dimensions of a multivariate time series, change over time. HiddenMarkov models (HMMs) and hidden semi-Markov models (HSMMs) are common analytic approaches for conducting dynamic connectivity analyses. However, existing approaches for HSMMs are limited in their ability to incorporate covariate information. In this work, we approximate an HSMM using an HMM for modeling multivariate time series data. The approximate HSMM (aHSMM) model allows one to explicitly model dwell-time distributions that are available to HSMMs, while maintaining the theoretical and methodological advances that are available to HMMs. We conducted a simulation study to show the performance of the aHSMM relative to other approaches. Finally, we used the aHSMM to conduct a dynamic connectivity analysis, where we showed how dwell-time distributions vary across the severity of non-suicidal self-injury (NSSI) in adolescents. The aHSMM allowed us to identify states that have greater dwell-times for those with moderate or severe NSSI.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138525553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Forecasting industrial production indices with a new singular spectrum analysis forecasting algorithm 一种新的奇异谱分析预测算法预测工业生产指标

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/21-sii693

Sofia Borodich Suarez, S. Heravi, A. Pepelyshev

{"title":"Forecasting industrial production indices with a new singular spectrum analysis forecasting algorithm","authors":"Sofia Borodich Suarez, S. Heravi, A. Pepelyshev","doi":"10.4310/21-sii693","DOIUrl":"https://doi.org/10.4310/21-sii693","url":null,"abstract":"Existing time series analysis and forecasting approaches struggle to produce accurate results in application to time series with complex trend, such as those commonly displayed by indices of industrial production (IIPs). In this study, a new version of the Singular Spectrum Analysis (SSA) technique is developed, namely the Separate Trend and Seasonality (SSA-STS) forecasting algorithm. Its performance is compared to those of benchmark, classical times series forecasting methods, including Basic SSA (the core version of SSA), ARIMA, Exponential Smoothing (ETS) and Neural Network (NN). The methods in this study are applied to both simulated and real data. The latter includes twenty four monthly series of seasonally unadjusted IIPs of various sectors for the UK, Germany and France. Using the out-of-sample forecasts, the results of this newly developed SSA-STS algorithm were compared to the other aforemen-tioned forecasting schemes by the means of pooled Root-Mean-Square-Error (RMSE). The pooling is done based on the number of steps ahead the forecasts extend, allowing for the performance of the methods to be evaluated on short and long horizons. The Kolmogorov-Smirnov Predictive Accuracy (KSPA) statistical test is applied to certify whether the errors produced by SSA-STS are statistically signiﬁcantly smaller than those of all the benchmark methods. Since this new technique is based on separate trend and seasonality forecasting, it overcomes the diﬃculties in forecasting series with complex trends and seasonality, thus demonstrating a clear advantage over other methods in such particular cases.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71150887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Network vector autoregressive moving average model 网络向量自回归移动平均模型

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii747

Xiao Chen, Yu Chen, Xixu Hu

引用次数: 0

SIMEX estimation for quantile regression model with measurement error 带有测量误差的分位数回归模型的SIMEX估计

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii742

Yiping Yang, Peixin Zhao, Dongsheng Wu

引用次数: 0

Robust conditional spectral analysis of replicated time series 复制时间序列的鲁棒条件谱分析

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/21-sii698

Zeda Li

{"title":"Robust conditional spectral analysis of replicated time series","authors":"Zeda Li","doi":"10.4310/21-sii698","DOIUrl":"https://doi.org/10.4310/21-sii698","url":null,"abstract":"Classical second-order spectral analysis, which is based on the Fourier transform of the autocovariance functions, focuses on summarizing the oscillatory behaviors of a time series. However, this type of analysis is subject to two major limitations: ﬁrst, being covariance-based, it cannot captures oscillatory information beyond the second moment, such as time-irreversibility and kurtosis, and cannot accommodate heavy-tail dependence and inﬁnite variance; second, focusing on a single time series, it is unable to quantify the association between multiple time series and other covariates of interests. In this article, we propose a novel nonparametric approach to the spectral analysis of multiple time series and the associated covariates. The procedure is based on the copula spectral density kernel, which inherits the robust-ness properties of quantile regression and does not require any distributional assumptions such as the existence of ﬁnite moments. Copula spectral density kernels of diﬀerent pairs are modeled jointly as a matrix to allow ﬂexible smoothing. Through a tensor-product spline model of Cholesky components of the conditional copula spectral density matrix, the approach provides ﬂexible nonparametric estimates of the copula spectral density matrix as nonparametric functions of frequency and covariate while preserving geometric con-straints. Empirical performance is evaluated in simulation studies and illustrated through an analysis of stride interval time series.","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71151106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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: 1

An iterative algorithm with adaptive weights and sparse Laplacian shrinkage for regression problems 回归问题的自适应加权和稀疏拉普拉斯收缩迭代算法

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii732

Xingyu Chen, Yuehan Yang

引用次数: 0

Two-stage multivariate dynamic linear models to extract environmental and climate signals in coastal ecosystem data 两阶段多元动态线性模型提取沿海生态系统数据中的环境和气候信号

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii731

J. Strock, G. Puggioni, S. Menden‐Deuer

引用次数: 0