Stat最新文献 - Book学术

Communication‐Efficient Distributed Estimation of Causal Effects With High‐Dimensional Data 利用高维数据对因果效应进行通信效率高的分布式估计

IF 1.7 4区数学

Stat Pub Date : 2024-09-10 DOI: 10.1002/sta4.70006

Xiaohan Wang, Jiayi Tong, Sida Peng, Yong Chen, Yang Ning

引用次数: 0

A Joint Temporal Model for Hospitalizations and ICU Admissions Due to COVID‐19 in Quebec 魁北克 COVID-19 引起的住院和入住重症监护室的联合时间模型

IF 1.7 4区数学

Stat Pub Date : 2024-09-06 DOI: 10.1002/sta4.70000

Mariana Carmona‐Baez, Alexandra M. Schmidt, Shirin Golchi, David Buckeridge

{"title":"A Joint Temporal Model for Hospitalizations and ICU Admissions Due to COVID‐19 in Quebec","authors":"Mariana Carmona‐Baez, Alexandra M. Schmidt, Shirin Golchi, David Buckeridge","doi":"10.1002/sta4.70000","DOIUrl":"https://doi.org/10.1002/sta4.70000","url":null,"abstract":"Infectious respiratory diseases have been of interest in recent years for the great burden they place on health systems, for instance, the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) that caused the global COVID‐19 pandemic. As many of these diseases might require hospitalization and even intensive care unit (ICU) admission, understanding the joint dynamics of hospitalizations and ICU admissions across time and different groups of the population remains of great importance. We aim to understand the joint evolution of hospital and ICU admissions given COVID‐19 test‐positive cases in the province of Quebec, Canada. We obtain the daily counts, by age group, on the number of confirmed COVID‐19 cases, the number of hospitalizations and the number of ICU admissions due to COVID‐19, from March 2020 through October 2021 in Quebec. We propose a joint Bayesian generalized dynamic linear model for the number of hospitalizations and ICU admissions to study their temporal trends and possible associations with sex and age group. Additionally, we use transfer functions to investigate if there is a memory effect of the number of cases on hospitalizations across the different age groups. The results suggest that there is a clear distinction in the patterns of hospitalizations and ICU admissions across age groups and that the number of cases has a persistent effect on the rate of hospitalization.","PeriodicalId":56159,"journal":{"name":"Stat","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193331","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

Bitcoin Price Prediction Using Deep Bayesian LSTM With Uncertainty Quantification: A Monte Carlo Dropout–Based Approach 使用具有不确定性量化的深度贝叶斯 LSTM 预测比特币价格：基于蒙特卡罗剔除的方法

IF 1.7 4区数学

Stat Pub Date : 2024-09-05 DOI: 10.1002/sta4.70001

Masoud Muhammed Hassan

{"title":"Bitcoin Price Prediction Using Deep Bayesian LSTM With Uncertainty Quantification: A Monte Carlo Dropout–Based Approach","authors":"Masoud Muhammed Hassan","doi":"10.1002/sta4.70001","DOIUrl":"https://doi.org/10.1002/sta4.70001","url":null,"abstract":"Bitcoin, being one of the most triumphant cryptocurrencies, is gaining increasing popularity online and is being used in a variety of transactions. Recently, research on Bitcoin price predictions is receiving more attention, and researchers have investigated the various state‐of‐the‐art machine learning (ML) and deep learning (DL) models to predict Bitcoin price. However, despite these models providing promising predictions, they consistently exhibit uncertainty, which cannot be adequately quantified by classical ML models alone. Motivated by the enormous success of applying Bayesian approaches in several disciplines of ML and DL, this study aims to use Bayesian methods alongside Long Short‐Term Memory (LSTM) to predict the closing Bitcoin price and consequently measure the uncertainty of the prediction model. Specifically, we adopted the Monte Carlo dropout (MC‐Dropout) method with the Bayesian LSTM model to quantify the epistemic uncertainty of the model's predictions and provided confidence intervals for the predicted outputs. Experimental results showed that the proposed model is efficient and outperforms other state‐of‐the‐art models in terms of root mean square error (RMSE), mean absolute error (MAE) and R2. Thus, we believe that these models may assist the investors and traders in making critical decisions based on short‐term predictions of Bitcoin price. This study illustrates the potential benefits of utilizing Bayesian DL approaches in time series analysis to improve data prediction accuracy and reliability.","PeriodicalId":56159,"journal":{"name":"Stat","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193332","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

Novel Closed‐Form Point Estimators for a Weighted Exponential Family Derived From Likelihood Equations 从似然方程推导出的加权指数族新闭式点估计器

IF 1.7 4区数学

Stat Pub Date : 2024-08-28 DOI: 10.1002/sta4.723

Roberto Vila, Eduardo Nakano, Helton Saulo

引用次数: 0

Exact interval estimation for three parameters subject to false positive misclassification 受假阳性误分类影响的三个参数的精确区间估计

IF 1.7 4区数学

Stat Pub Date : 2024-08-28 DOI: 10.1002/sta4.717

Shuiyun Lu, Weizhen Wang, Tianfa Xie

{"title":"Exact interval estimation for three parameters subject to false positive misclassification","authors":"Shuiyun Lu, Weizhen Wang, Tianfa Xie","doi":"10.1002/sta4.717","DOIUrl":"https://doi.org/10.1002/sta4.717","url":null,"abstract":"SummaryBinary data subject to one type of misclassification exist in various fields. It is collected in a double‐sampling scheme that includes a gold standard test and a fallible test. The main parameter of interest for this type of data is the positive probability of the gold standard test. Existing intervals are unreliable because the given nominal level is not achieved. In this paper, we construct an exact interval by inverting the E+M score tests and improve it by the general ‐function method. We find that the total length of the improved interval is shorter than the exact intervals that are also the improved intervals when we apply the ‐function to several existing approximate intervals, including the score and Bayesian intervals. Therefore, it is recommended for practice. We are also interested in two other parameters: —the difference between the two positive rates for the fallible and gold standard tests—and —the false positive rate for the fallible test. To the best of our knowledge, the research on these two parameters is limited. For , we find that any interval for can be converted to an interval for . So, the interval converted from the aforementioned recommended interval for is recommended for inferring . For , the improved interval by the ‐function method over the E+M score interval is derived. We use an example to illustrate how the intervals are computed and provide a real data analysis.","PeriodicalId":56159,"journal":{"name":"Stat","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193333","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

Decorrelated nearest shrunken centroids for tensor data 张量数据的相关最近缩减中心点

IF 1.7 4区数学

Stat Pub Date : 2024-08-14 DOI: 10.1002/sta4.720

Shaokang Ren, Munwon Yang, Qing Mai

引用次数: 0

A novel time‐varying coefficient Poisson difference model driven by observation 观测驱动的新型时变系数泊松差分模型

IF 1.7 4区数学

Stat Pub Date : 2024-08-07 DOI: 10.1002/sta4.721

Ye Liu, Dehui Wang

引用次数: 0

A spectral approach for the dynamic Bradley–Terry model 动态布拉德利-特里模型的光谱方法

IF 1.7 4区数学

Stat Pub Date : 2024-08-05 DOI: 10.1002/sta4.722

Xinyu Tian, Jian Shi, Xiaotong Shen, Kai Song

{"title":"A spectral approach for the dynamic Bradley–Terry model","authors":"Xinyu Tian, Jian Shi, Xiaotong Shen, Kai Song","doi":"10.1002/sta4.722","DOIUrl":"https://doi.org/10.1002/sta4.722","url":null,"abstract":"SummaryThe dynamic ranking, due to its increasing importance in many applications, is becoming crucial, especially with the collection of voluminous time‐dependent data. One such application is sports statistics, where dynamic ranking aids in forecasting the performance of competitive teams, drawing on historical and current data. Despite its usefulness, predicting and inferring rankings pose challenges in environments necessitating time‐dependent modelling. This paper introduces a spectral ranker called Kernel Rank Centrality, designed to rank items based on pairwise comparisons over time. The ranker operates via kernel smoothing in the Bradley–Terry model, utilising a Markov chain model. Unlike the maximum likelihood approach, the spectral ranker is nonparametric, demands fewer model assumptions and computations and allows for real‐time ranking. We establish the asymptotic distribution of the ranker by applying an innovative group inverse technique, resulting in a uniform and precise entrywise expansion. This result allows us to devise a new inferential method for predictive inference, previously unavailable in existing approaches. Our numerical examples showcase the ranker's utility in predictive accuracy and constructing an uncertainty measure for prediction, leveraging data from the National Basketball Association (NBA). The results underscore our method's potential compared with the gold standard in sports, the Arpad Elo rating system.","PeriodicalId":56159,"journal":{"name":"Stat","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141944782","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

A non‐stationary factor copula model for non‐Gaussian spatial data 非高斯空间数据的非平稳因子共轭模型

IF 1.7 4区数学

Stat Pub Date : 2024-08-05 DOI: 10.1002/sta4.715

Sagnik Mondal, Pavel Krupskii, Marc G. Genton

引用次数: 0

Empowering collaborative statisticians: The impact of the American Statistical Association's Pathways to Promotion Committee 增强合作统计学家的能力：美国统计协会晋升之路委员会的影响

IF 1.7 4区数学

Stat Pub Date : 2024-08-05 DOI: 10.1002/sta4.716

Margaret R. Stedman, Salem Dehom, Mario A. Davidson, Li Zhang, Robert H. Podolsky, Ryan T. Pohlig, Todd Coffey

引用次数: 0