Journal of Applied Statistics最新文献_第8页

Regression-based rectangular tolerance regions as reference regions in laboratory medicine. 基于回归的矩形公差区域作为检验医学的参考区域。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-10-08 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2411614

Iana Michelle L Garcia, Michael Daniel C Lucagbo

{"title":"Regression-based rectangular tolerance regions as reference regions in laboratory medicine.","authors":"Iana Michelle L Garcia, Michael Daniel C Lucagbo","doi":"10.1080/02664763.2024.2411614","DOIUrl":"10.1080/02664763.2024.2411614","url":null,"abstract":"Reference ranges are invaluable in laboratory medicine, as these are indispensable tools for the interpretation of laboratory test results. When assessing measurements on a single analyte, univariate reference intervals are required. In many cases, however, measurements on several analytes are needed by medical practitioners to diagnose more complicated conditions such as kidney function or liver function. For such cases, it is recommended to use multivariate reference regions, which account for the cross-correlations among the analytes. Traditionally, multivariate reference regions (MRRs) have been constructed as ellipsoidal regions. The disadvantage of such regions is that they are unable to detect component-wise outlying measurements. Because of this, rectangular reference regions have recently been put forward in the literature. In this study, we develop methodologies to compute rectangular MRRs that incorporate covariate information, which are often necessary in evaluating laboratory test results. We construct the reference region using tolerance-based criteria so that the resulting region possesses the multiple use property. Results show that the proposed regions yield coverage probabilities that are accurate and are robust to the sample size. Finally, we apply the proposed procedures to a real-life example on the computation of an MRR for three components of the insulin-like growth factor system.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 5","pages":"1040-1062"},"PeriodicalIF":1.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951328/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752912","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

COVINet: a deep learning-based and interpretable prediction model for the county-wise trajectories of COVID-19 in the United States. COVINet：一个基于深度学习和可解释的预测模型，用于预测美国各州的COVID-19发展轨迹。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-10-08 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2412284

Yukang Jiang, Ting Tian, Wenting Zhou, Yuting Zhang, Zhongfei Li, Xueqin Wang, Heping Zhang

{"title":"COVINet: a deep learning-based and interpretable prediction model for the county-wise trajectories of COVID-19 in the United States.","authors":"Yukang Jiang, Ting Tian, Wenting Zhou, Yuting Zhang, Zhongfei Li, Xueqin Wang, Heping Zhang","doi":"10.1080/02664763.2024.2412284","DOIUrl":"10.1080/02664763.2024.2412284","url":null,"abstract":"The devastating impact of COVID-19 on the United States has been profound since its onset in January 2020. Predicting the trajectory of epidemics accurately and devising strategies to curb their progression are currently formidable challenges. In response to this crisis, we propose COVINet, which combines the architecture of Long Short-Term Memory and Gated Recurrent Unit, incorporating actionable covariates to offer high-accuracy prediction and explainable response. First, we train COVINet models for confirmed cases and total deaths with five input features, and compare Mean Absolute Errors (MAEs) and Mean Relative Errors (MREs) of COVINet against ten competing models from the United States CDC in the last four weeks before April 26, 2021. The results show COVINet outperforms all competing models for MAEs and MREs when predicting total deaths. Then, we focus on prediction for the most severe county in each of the top 10 hot-spot states using COVINet. The MREs are small for all predictions made in the last 7 or 30 days before March 23, 2023. Beyond predictive accuracy, COVINet offers high interpretability, enhancing the understanding of pandemic dynamics. This dual capability positions COVINet as a powerful tool for informing effective strategies in pandemic prevention and governmental decision-making.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 5","pages":"1063-1080"},"PeriodicalIF":1.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752907","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

The efficiency of CUSUM schemes for monitoring the multivariate coefficient of variation in short runs process. CUSUM方案监测短期过程多变量变异系数的有效性。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-09-25 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2405111

Xuelong Hu, Yixuan Ma, Jiening Zhang, Jiujun Zhang, Ali Yeganeh, Sandile Charles Shongwe

{"title":"The efficiency of CUSUM schemes for monitoring the multivariate coefficient of variation in short runs process.","authors":"Xuelong Hu, Yixuan Ma, Jiening Zhang, Jiujun Zhang, Ali Yeganeh, Sandile Charles Shongwe","doi":"10.1080/02664763.2024.2405111","DOIUrl":"10.1080/02664763.2024.2405111","url":null,"abstract":"Current monitoring technologies emphasize and address the issue of monitoring high-volume production processes. The high flexibility and diversity of current industrial production processes make monitoring technology for small batch processes even more important. In multivariate process monitoring, a broader applicability exists in multivariate coefficients of variation (MCV) based monitoring schemes due to the lower restriction of the process. In view of the effectiveness of MCV monitoring and with the aim to achieve further performance improvement of current MCV monitoring schemes in a finite horizon production, we additionally introduce two one-sided cumulative sum (CUSUM) MCV schemes. In the case of deterministic and random shifts, the design parameters of the proposed schemes are obtained via an optimization procedure designed by the Markov chain method and the corresponding performance is analysed based on different run length (RL) characteristics, including the mean and the standard deviation. Simulation comparisons with existing exponentially weighted moving average (EWMA) MCV schemes show that the proposed CUSUM MCV schemes are more efficient in monitoring most of the shifts, including the deterministic and random shifts. Finally, to demonstrate the benefits of the new monitoring schemes, a comprehensive case study on monitoring a steel sleeve manufacturing process is conducted.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 4","pages":"966-992"},"PeriodicalIF":1.1,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873948/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143557090","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

A class of infinite number of unbiased estimators using weighted squared distance for two-deck randomized response model. 一类二阶随机响应模型的无偏加权距离平方估计。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-09-25 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2399574

Daryan Naatjes, Stephen A Sedory, Sarjinder Singh

引用次数: 0

Inference for depending competing risks from Marshall-Olikin bivariate Kies distribution under generalized progressive hybrid censoring. 广义渐进混合审查下Marshall-Olikin二元Kies分布依赖竞争风险的推论。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-09-20 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2405108

Prakash Chandra, Hemanta Kumar Mandal, Yogesh Mani Tripathi, Liang Wang

引用次数: 0

Bayesian inference for Laplace distribution based on complete and censored samples with illustrations. 基于完整样本和删节样本的拉普拉斯分布的贝叶斯推理。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-09-11 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2401470

Wanyue Sun, Xiaojun Zhu, Zhehao Zhang, N Balakrishnan

{"title":"Bayesian inference for Laplace distribution based on complete and censored samples with illustrations.","authors":"Wanyue Sun, Xiaojun Zhu, Zhehao Zhang, N Balakrishnan","doi":"10.1080/02664763.2024.2401470","DOIUrl":"10.1080/02664763.2024.2401470","url":null,"abstract":"In this paper, Bayesian estimates are derived for the location and scale parameters of the Laplace distribution based on complete, Type-I, and Type-II censored samples under different prior settings. Subsequently, Bayesian point and interval estimates, as well as the associated statistical inference, are discussed in detail. The developed methods are then applied to two real data sets for illustrative purposes. Moreover, a detailed Monte Carlo simulation study is carried out for evaluating the performance of the inferential methods developed here. Finally, we provide a brief discussion of the established results to demonstrate their practical utility and present some associated problems of further interest. Overall, this study fills an existing gap in the development of Bayesian inferential techniques for the parameters of the two-parameter Laplace distribution, making this research innovative and offering more investigative implications. It showcases the potential for broader methodological applications of Bayesian inference for complex real-world data sets, especially in scenarios involving different forms of censoring. This research provides a critical tool for statistical analysis in different fields such as engineering and finance, where the Laplace distribution is frequently adopted as a fundamental model.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 4","pages":"914-935"},"PeriodicalIF":1.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873909/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143557088","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

A partitioned weighted moving average control chart. 分割加权移动平均控制图。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-09-08 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2392122

Raja Fawad Zafar, Michael B C Khoo, Huay Woon You, Sajal Saha, Wai Chung Yeong

{"title":"A partitioned weighted moving average control chart.","authors":"Raja Fawad Zafar, Michael B C Khoo, Huay Woon You, Sajal Saha, Wai Chung Yeong","doi":"10.1080/02664763.2024.2392122","DOIUrl":"10.1080/02664763.2024.2392122","url":null,"abstract":"A partitioned weighted moving average (PWMA) chart is developed by partitioning the samples (or observations) into two groups, calculating the groups' weighted average and adding them. This partitioning gives more control over weight distribution in the most recent j (= 2, 3, …) samples. The PWMA, exponentially weighted moving average (EWMA) and homogenously weighted moving average (HWMA) charts are compared. For zero state, the PWMA chart outperforms the EWMA and HWMA charts for most (n, λ, δ) values and the outperformance of the former over the two latter charts increases with the time period (j), employed in the partitioning. Here, λ is the charts' smoothing constant and δ is the shift size (multiples of standard deviation). For steady state, the PWMA chart (regardless of j) generally outperforms the EWMA chart in detecting a small shift (δ = 0.25) when the smoothing constant λ ≥ 0.2 for the sample size n = 1; while a larger λ is needed for n = 5. Moreover, for steady state, the PWMA chart outperforms the HWMA chart in detecting small and moderate shifts (0.25 ≤ δ ≤ 1), regardless of (λ, n, j). The PWMA chart demonstrates robustness to non-normality and estimated process parameters.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 3","pages":"744-777"},"PeriodicalIF":1.1,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11816634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414125","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

Framework for constructing an optimal weighted score based on agreement 构建基于协议的最优加权得分框架

IF 1.5 4区数学

Journal of Applied Statistics Pub Date : 2024-09-05 DOI: 10.1080/02664763.2024.2399586

Zhiping Qiu, Manatunga Amita, Limin Peng, Ying Guo, Tanja Jovanovic

引用次数: 0

Accurate and efficient stock market index prediction: an integrated approach based on VMD-SNNs. 准确高效的股市指数预测：一种基于vmd - snn的综合方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2024-09-03 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2395961

Xuchang Chen, Guoqiang Tang, Yumei Ren, Xin Lin, Tongzhi Li

{"title":"Accurate and efficient stock market index prediction: an integrated approach based on VMD-SNNs.","authors":"Xuchang Chen, Guoqiang Tang, Yumei Ren, Xin Lin, Tongzhi Li","doi":"10.1080/02664763.2024.2395961","DOIUrl":"10.1080/02664763.2024.2395961","url":null,"abstract":"The stock market index typically mirrors the financial market's performance. Hence, accurate prediction of stock market index trends is essential for investors aiming to mitigate financial risk and enhance future investment returns. Traditional statistical approaches often struggle with the non-linear nature of stock market index data, leading to potential inaccuracies in long-term predictions. To address this issue, we introduce the TCN-LSTM-SNN (TLSNN) model, a hybrid framework that integrates Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN) for robust feature extraction, within a highly efficient Spiking Neural Network (SNN) architecture. Additionally, we employ the Subtraction-Average-Based Optimizer (SABO) to refine the Variational Mode Decomposition (VMD) technique, thereby separating the periodic and trend components of stock indices, reducing noise interference, and establishing a decomposition ensemble framework to bolster the model's resilience. The experimental results show that the VMD-TLSNN hybrid model suggested in this study surpasses other individual benchmark models and their hybrid models in prediction accuracy. Additionally, it demonstrates notably lower energy consumption compared to other hybrid models.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 4","pages":"841-867"},"PeriodicalIF":1.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11873965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143557087","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

On function-on-function linear quantile regression 关于函数对函数的线性量化回归

IF 1.5 4区数学

Journal of Applied Statistics Pub Date : 2024-08-28 DOI: 10.1080/02664763.2024.2395960

Muge Mutis, Ufuk Beyaztas, Filiz Karaman, Han Lin Shang

引用次数: 0