Statistics and Its Interface最新文献_第7页

A default Bayesian multiple comparison of two binomial proportions 两个二项式比例的默认贝叶斯倍数比较

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii740

E. Gecili, S. Sivaganesan

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

4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/sii.2023.v16.n1.a7

Zeda Li

引用次数: 1

The elliptical Ornstein–Uhlenbeck process 椭圆Ornstein-Uhlenbeck过程

4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/sii.2023.v16.n1.a11

Adam Sykulski, Sofia Charlotta Olhede, Hanna Sykulska-Lawrence

引用次数: 0

A semi-supervised density peaks clustering algorithm 一种半监督密度峰聚类算法

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii725

Yuanyuan Wang, Bingyi Jing

引用次数: 0

Quadratic upper bound algorithms for estimation under Cox model in case-cohort studies 病例队列研究中Cox模型估计的二次上界算法

IF 0.8 4区数学

Statistics and Its Interface Pub Date : 2023-01-01 DOI: 10.4310/22-sii736

Jieli Ding, Jiaqian Zhang, Yanqin Feng, Yuxuan Du

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

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":"1 1","pages":"505-515"},"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

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":"1 1","pages":""},"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

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

引用次数: 0