Journal of Applied Statistics最新文献

Clustering in point processes on linear networks using nearest neighbour volumes.

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-07 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2411214

Juan F Díaz-Sepúlveda, Nicoletta D'Angelo, Giada Adelfio, Jonatan A González, Francisco J Rodríguez-Cortés

{"title":"Clustering in point processes on linear networks using nearest neighbour volumes.","authors":"Juan F Díaz-Sepúlveda, Nicoletta D'Angelo, Giada Adelfio, Jonatan A González, Francisco J Rodríguez-Cortés","doi":"10.1080/02664763.2024.2411214","DOIUrl":"10.1080/02664763.2024.2411214","url":null,"abstract":"This study introduces a novel method specifically designed to detect clusters of points within linear networks. This method extends a classification approach used for point processes in spatial contexts. Unlike traditional methods that operate on planar spaces, our approach adapts to the unique geometric challenges of linear networks, where classical properties of point processes are altered, and intuitive data visualisation becomes more complex. Our method utilises the distribution of the Kth nearest neighbour volumes, extending planar-based clustering techniques to identify regions of increased point density within a network. This approach is particularly effective for distinguishing overlapping Poisson processes within the same linear network. We demonstrate the practical utility of our method through applications to road traffic accident data from two Colombian cities, Bogota and Medellin. Our results reveal distinct clusters of high-density points in road segments where severe traffic accidents (resulting in injuries or fatalities) are most likely to occur, highlighting areas of increased risk. These clusters were primarily located on major arterial roads with high traffic volumes. In contrast, low-density points corresponded to areas with fewer accidents, likely due to lower traffic flow or other mitigating factors. Our findings provide valuable insights for urban planning and road safety management.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 5","pages":"993-1016"},"PeriodicalIF":1.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752900","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 PCovR biplot: a graphical tool for principal covariates regression.

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-18 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2417978

Elisa Frutos-Bernal, José Luis Vicente-Villardón

引用次数: 0

Reliability analysis based on doubly-truncated and interval-censored data.

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-14 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2415412

Pao-Sheng Shen, Huai-Man Li

引用次数: 0

A novel ranked k-nearest neighbors algorithm for missing data imputation.

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-11 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2414357

Yasir Khan, Said Farooq Shah, Syed Muhammad Asim

{"title":"A novel ranked k-nearest neighbors algorithm for missing data imputation.","authors":"Yasir Khan, Said Farooq Shah, Syed Muhammad Asim","doi":"10.1080/02664763.2024.2414357","DOIUrl":"10.1080/02664763.2024.2414357","url":null,"abstract":"Missing data is a common problem in many domains that rely on data analysis. The k Nearest Neighbors imputation method has been widely used to address this issue, but it has limitations in accurately imputing missing values, especially for datasets with small pairwise correlations and small values of k. In this study, we proposed a method, Ranked k Nearest Neighbors imputation that uses a similar approach to k Nearest Neighbor, but utilizing the concept of Ranked set sampling to select the most relevant neighbors for imputation. Our results show that the proposed method outperforms the standard k nearest neighbor method in terms of imputation accuracy both in case of Missing Completely at Random and Missing at Random mechanism, as demonstrated by consistently lower MSIE and MAIE values across all datasets. This suggests that the proposed method is a promising alternative for imputing missing values in datasets with small pairwise correlations and small values of k. Thus, the proposed Ranked k Nearest Neighbor method has important implications for data imputation in various domains and can contribute to the development of more efficient and accurate imputation methods without adding any computational complexity to an algorithm.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 5","pages":"1103-1127"},"PeriodicalIF":1.2,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951327/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752879","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

Robust multi-outcome regression with correlated covariate blocks using fused LAD-lasso.

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-11 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2414346

Jyrki Möttönen, Tero Lähderanta, Janne Salonen, Mikko J Sillanpää

引用次数: 0

Bayesian poisson regression tensor train decomposition model for learning mortality pattern changes during COVID-19 pandemic.

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-10-10 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2411608

Wei Zhang, Antonietta Mira, Ernst C Wit

{"title":"Bayesian poisson regression tensor train decomposition model for learning mortality pattern changes during COVID-19 pandemic.","authors":"Wei Zhang, Antonietta Mira, Ernst C Wit","doi":"10.1080/02664763.2024.2411608","DOIUrl":"10.1080/02664763.2024.2411608","url":null,"abstract":"COVID-19 has led to excess deaths around the world. However, the impact on mortality rates from other causes of death during this time remains unclear. To understand the broader impact of COVID-19 on other causes of death, we analyze Italian official data covering monthly mortality counts from January 2015 to December 2020. To handle the high-dimensional nature of the data, we developed a model that combines Poisson regression with tensor train decomposition to explore the lower-dimensional residual structure of the data. Our Bayesian approach incorporates prior information on model parameters and utilizes an efficient Metropolis-Hastings within Gibbs algorithm for posterior inference. Simulation studies were conducted to validate our approach. Our method not only identifies differential effects of interventions on cause-specific mortality rates through Poisson regression but also provides insights into the relationship between COVID-19 and other causes of death. Additionally, it uncovers latent classes related to demographic characteristics, temporal patterns, and causes of death.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 5","pages":"1017-1039"},"PeriodicalIF":1.2,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752896","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

Regression-based rectangular tolerance regions as reference regions in laboratory medicine.

IF 1.2 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":1.2,"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.

IF 1.2 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":1.2,"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.

IF 1.2 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":1.2,"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.

IF 1.2 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