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Generalized Gaussian time series model for increments of EEG data 脑电数据增量的广义高斯时间序列模型
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/21-sii692
N. Leonenko, Ž. Salinger, A. Sikorskii, N. Šuvak, M. Boivin
{"title":"Generalized Gaussian time series model for increments of EEG data","authors":"N. Leonenko, Ž. Salinger, A. Sikorskii, N. Šuvak, M. Boivin","doi":"10.4310/21-sii692","DOIUrl":"https://doi.org/10.4310/21-sii692","url":null,"abstract":"We propose a new strictly stationary time series model with marginal generalized Gaussian distribution and exponentially decaying autocorrelation function for modeling of increments of electroencephalogram (EEG) data collected from Ugandan children during coma from cerebral malaria. The model inherits its appealing properties from the strictly stationary strong mixing Markovian diffusion with invari-ant generalized Gaussian distribution (GGD). The GGD parametrization used in this paper comprises some famous light-tailed distributions (e.g., Laplace and Gaussian) and some well known and widely applied heavy-tailed distributions (e.g., Student). Two versions of this model fit to the data from each EEG channel. In the first model, marginal distributions is from the light-tailed GGD sub-family, and the distribution parameters were estimated using quasi-likelihood approach. In the second model, marginal distributions is heavy-tailed (Student), and the tail index was estimated using the approach based on the empirical scaling function. The estimated parameters from models across EEG channels were explored as potential predictors of neurocognitive outcomes of these children 6 months after recov-ering from illness. Several of these parameters were shown to be important predictors even after controlling for neurocognitive scores immediately following cerebral malaria illness and traditional blood and cerebrospinal fluid biomarkers collected during hospitalization.","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":"71150765","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: 2
Compressing recurrent neural network models through principal component analysis 利用主成分分析压缩递归神经网络模型
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii727
Haobo Qi, Jingxuan Cao, Shichong Chen, Jing Zhou
{"title":"Compressing recurrent neural network models through principal component analysis","authors":"Haobo Qi, Jingxuan Cao, Shichong Chen, Jing Zhou","doi":"10.4310/22-sii727","DOIUrl":"https://doi.org/10.4310/22-sii727","url":null,"abstract":"","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":"71152092","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
Multiple hypotheses testing on dependent count data with covariate effects 具有协变量效应的相关计数数据的多假设检验
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii728
Weizhe Su, Xia Wang
{"title":"Multiple hypotheses testing on dependent count data with covariate effects","authors":"Weizhe Su, Xia Wang","doi":"10.4310/22-sii728","DOIUrl":"https://doi.org/10.4310/22-sii728","url":null,"abstract":"","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":"71152206","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
Modified recurrent forecasting in singular spectrum analysis using Kalman filter and its application for bicoid signal extraction 卡尔曼滤波奇异谱分析改进循环预测及其在双曲面信号提取中的应用
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii723
Reza Zabihi Moghadam, M. Yarmohammadi, Hossein Hassani
{"title":"Modified recurrent forecasting in singular spectrum analysis using Kalman filter and its application for bicoid signal extraction","authors":"Reza Zabihi Moghadam, M. Yarmohammadi, Hossein Hassani","doi":"10.4310/22-sii723","DOIUrl":"https://doi.org/10.4310/22-sii723","url":null,"abstract":"","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":"71152379","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
Uniform consistency for local fitting of time series non-parametric regression allowing for discrete-valued response 允许离散值响应的时间序列非参数回归局部拟合的一致一致性
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii745
Rong Peng, Zudi Lu
{"title":"Uniform consistency for local fitting of time series non-parametric regression allowing for discrete-valued response","authors":"Rong Peng, Zudi Lu","doi":"10.4310/22-sii745","DOIUrl":"https://doi.org/10.4310/22-sii745","url":null,"abstract":"Local linear kernel fitting is a popular nonparametric technique for modelling nonlinear time series data. Investigations into it, although extensively made for continuousvalued case, are still rare for the time series that are discrete-valued. In this paper, we propose and develop the uniform consistency of local linear maximum likelihood (LLML) fitting for time series regression allowing response to be discrete-valued under β-mixing dependence condition. Specifically, the uniform consistency of LLML estimators is established under time series conditional exponential family distributions with aid of a beta-mixing empirical process through local estimating equations. The rate of convergence is also provided under mild conditions. Performances of the proposed method are demonstrated by a Monte-Carlo simulation study and an application to COVID-19 data. There is a huge potential for the developed theory contributing to further development of discrete-valued response semiparametric time series models © 2022 American Psychological Association","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":"71152619","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
Markov-switching Poisson generalized autoregressive conditional heteroscedastic models 马尔可夫开关泊松广义自回归条件异方差模型
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii741
Ji-Chun Liu, Yue Pan, Jiazhu Pan, A. Almarashi
{"title":"Markov-switching Poisson generalized autoregressive conditional heteroscedastic models","authors":"Ji-Chun Liu, Yue Pan, Jiazhu Pan, A. Almarashi","doi":"10.4310/22-sii741","DOIUrl":"https://doi.org/10.4310/22-sii741","url":null,"abstract":"","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":"71152895","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
Estimating individualized treatment rules for multicategory type 2 diabetes treatments using electronic health records. 利用电子健康记录估算 2 型糖尿病多类别治疗的个性化治疗规则。
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 Epub Date: 2023-04-14 DOI: 10.4310/22-sii739
Jitong Lou, Yuanjia Wang, Lang Li, Donglin Zeng
{"title":"Estimating individualized treatment rules for multicategory type 2 diabetes treatments using electronic health records.","authors":"Jitong Lou, Yuanjia Wang, Lang Li, Donglin Zeng","doi":"10.4310/22-sii739","DOIUrl":"10.4310/22-sii739","url":null,"abstract":"<p><p>In this article, we propose a general framework to learn optimal treatment rules for type 2 diabetes (T2D) patients using electronic health records (EHRs). We first propose a joint modeling approach to characterize patient's pretreatment conditions using longitudinal markers from EHRs. The estimation accounts for informative measurement times using inverse-intensity weighting methods. The predicted latent processes in the joint model are used to divide patients into a finite of subgroups and, within each group, patients share similar health profiles in EHRs. Within each patient group, we estimate optimal individualized treatment rules by extending a matched learning method to handle multicategory treatments using a one-versus-one approach. Each matched learning for two treatments is implemented by a weighted support vector machine with matched pairs of patients. We apply our method to estimate optimal treatment rules for T2D patients in a large sample of EHRs from the Ohio State University Wexner Medical Center. We demonstrate the utility of our method to select the optimal treatments from four classes of drugs and achieve a better control of glycated hemoglobin than any one-size-fits-all rules.</p>","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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10857856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71152790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Adaptive Clustering and Feature Selection for Categorical Time Series Using Interpretable Frequency-Domain Features. 使用可解释的频域特征对分类时间序列进行自适应聚类和特征选择。
IF 0.3 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 Epub Date: 2023-04-13 DOI: 10.4310/22-sii755
Scott A Bruce
{"title":"Adaptive Clustering and Feature Selection for Categorical Time Series Using Interpretable Frequency-Domain Features.","authors":"Scott A Bruce","doi":"10.4310/22-sii755","DOIUrl":"10.4310/22-sii755","url":null,"abstract":"<p><p>This article presents a novel approach to clustering and feature selection for categorical time series via interpretable frequency-domain features. A distance measure is introduced based on the spectral envelope and optimal scalings, which parsimoniously characterize prominent cyclical patterns in categorical time series. Using this distance, partitional clustering algorithms are introduced for accurately clustering categorical time series. These adaptive procedures offer simultaneous feature selection for identifying important features that distinguish clusters and fuzzy membership when time series exhibit similarities to multiple clusters. Clustering consistency of the proposed methods is investigated, and simulation studies are used to demonstrate clustering accuracy with various underlying group structures. The proposed methods are used to cluster sleep stage time series for sleep disorder patients in order to identify particular oscillatory patterns associated with sleep disruption.</p>","PeriodicalId":51230,"journal":{"name":"Statistics and Its Interface","volume":null,"pages":null},"PeriodicalIF":0.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9488249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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
Hierarchical dynamic PARCOR models for analysis of multiple brain signals 多脑信号分析的层次动态PARCOR模型
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/21-sii699
Wenjie Zhao, R. Prado
{"title":"Hierarchical dynamic PARCOR models for analysis of multiple brain signals","authors":"Wenjie Zhao, R. Prado","doi":"10.4310/21-sii699","DOIUrl":"https://doi.org/10.4310/21-sii699","url":null,"abstract":"We present an efficient hierarchical model for inferring latent structure underlying multiple non-stationary time series. The proposed model describes the time-varying behavior of multiple time series in the partial autocorrelation domain, which results in a lower dimensional representation, and consequently computationally faster inference, than those required by models in the time and/or frequency domains, such as time-varying autoregressive models, which are commonly used in practice. We illustrate the performance of the proposed hierarchical dynamic PARCOR models and corresponding Bayesian inferential procedures in the context of analyzing multiple brain signals recorded simultaneously during specific experimental settings or clinical studies. The proposed approach allows us to efficiently obtain posterior summaries of the time-frequency characteristics of the multiple time series, as well as those summarizing their common underlying structure.","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":"71151168","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
Study of impact of COVID-19 on industrial production indices using singular spectrum analysis 利用奇异谱分析研究新冠肺炎疫情对工业生产指标的影响
IF 0.8 4区 数学
Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/21-sii719
Sofia Borodich Suarez, A. Pepelyshev
{"title":"Study of impact of COVID-19 on industrial production indices using singular spectrum analysis","authors":"Sofia Borodich Suarez, A. Pepelyshev","doi":"10.4310/21-sii719","DOIUrl":"https://doi.org/10.4310/21-sii719","url":null,"abstract":"This paper investigates the impact of the COVID-19 pandemic on 8 different indices of industrial production (IIPs) for three major European countries: France, Germany, and the UK. The analysis is based on applying a combination of Singular Spectrum Analysis (SSA) algorithms, in a way that allows for the proper separation of the trend and seasonal subcycles of the IIPs. The main purpose is to illustrate how to carry out the procedure of the correct decomposition by SSA for the specific series. The accurately extracted trends are analysed and the influence of the pandemic is calculated. The results confirm that necessary goods, such as food and utilities, have low income elasticity of demand since the effect of COVID-19 is negligible for these IIPs. However, for the IIPs of less essential products, the negative impact is much more extreme, although the severity varies depending on several factors, which also aligns with the economic theory © 2023, Statistics and its Interface.All Rights Reserved.","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":"71151739","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
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