Journal of Applied Statistics最新文献_第4页

An empirical power comparison of directional goodness-of-fit tests for 2-PL IRT model under different intercept patterns. 不同截距模式下2-PL IRT模型定向拟合优度检验的经验功率比较。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-23 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2590005

Jinhui Xu, Jingwen Chen, Mark Reiser

引用次数: 0

Evaluating discrimination accuracy of prognostic models with competing risk outcomes. 评估具有竞争风险结果的预后模型的判别准确性。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-21 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2589994

Yang Qu, Yu Cheng

{"title":"Evaluating discrimination accuracy of prognostic models with competing risk outcomes.","authors":"Yang Qu, Yu Cheng","doi":"10.1080/02664763.2025.2589994","DOIUrl":"10.1080/02664763.2025.2589994","url":null,"abstract":"We evaluate the discrimination accuracy of prognostic models for competing risk outcomes when multiple events are modeled simultaneously. Existing methods are mainly cause-specific assessments that focus on one event of interest and ignore model information for other events, therefore cannot fully characterize model's ability to distinguish different groups. In this work, our goal is to first introduce several new measures for overall and cause-specific evaluation that account for multiple events simultaneously, to better capture models' discrimination ability from different aspect based on varying rules of classification and concordance, and then to systematically investigate their functionalities under various scenarios via simulation studies and real data analyses. Estimators of proposed metrics are provided, along with asymptotic properties of one estimator demonstrated as an example. Our results highlight that one of the proposed cause-specific evaluation indices is an attractive alternative for distinguishing models from different families. Furthermore, all measures demonstrate sensitivity to the exclusion of important variables in prognostic models, highlighting their utility in evaluating model performance.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 11","pages":"2183-2202"},"PeriodicalIF":1.1,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13537067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148880511","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

Alternative tests for one-way ANCOVA under heteroscedasticity. 异方差条件下单向ANCOVA的替代检验。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-21 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2581066

Anjana Mondal, Somesh Kumar

引用次数: 0

Bayesian singular value decomposition procedure based on the horseshoe prior. 基于马蹄形先验的贝叶斯奇异值分解方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-21 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2589992

Jun Wang, Yixiao Gong, Hongmei Lin, Riquan Zhang, Weihua Zhao

引用次数: 0

Testing the distribution change in multivariate data using rank energy statistics. 用秩能统计检验多元数据的分布变化。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-15 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2578653

Yanhong Liu, Jiaqi Li, Zhonghua Li, Jiyang Wang, Mengxin Wang

引用次数: 0

Estimation of shifted Weibull distribution parameters using continuous Hopfield neural networks: an application to investment risk modeling. 用连续Hopfield神经网络估计转移威布尔分布参数：在投资风险建模中的应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-12 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2585946

Hamza Abubakar, Amani A Idris Sayed

{"title":"Estimation of shifted Weibull distribution parameters using continuous Hopfield neural networks: an application to investment risk modeling.","authors":"Hamza Abubakar, Amani A Idris Sayed","doi":"10.1080/02664763.2025.2585946","DOIUrl":"10.1080/02664763.2025.2585946","url":null,"abstract":"Parameter estimation is a fundamental component of statistical modeling, shaping the reliability of inferences across disciplines such as finance, engineering, and risk management. Accurate and stable estimation is particularly critical when modeling tail risks and extreme events. The Shifted Weibull Distribution (SWD) provides flexibility in reliability and financial applications; however, traditional estimation methods often struggle to balance accuracy, robustness, and computational efficiency. This study proposes a Continuous Hopfield Neural Network (CHNN)-based framework for estimating SWD parameters and compares its performance with the Newton-Raphson (NR-SWD) and Artificial Neural Network (ANN-SWD) estimators. The CHNN-SWD model is formulated through Lyapunov energy minimization to ensure stable convergence. Its performance was evaluated through simulations with varying sample sizes, using estimation accuracy, model adequacy, and computational efficiency as evaluation criteria. Empirical validation was conducted using Malaysian Shariah-compliant property investment returns (2008-2022), with risk assessment performed through Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), and Kupiec backtesting. Results indicate that CHNN-SWD outperformed both NR-SWD and ANN-SWD in terms of robustness, accuracy, and stability, particularly in modeling extreme risk behavior. Overall, CHNN-SWD demonstrated superior performance across all validation criteria, establishing it as a reliable and computationally efficient estimator suitable for tail-dependent risk modeling applications.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 11","pages":"2104-2136"},"PeriodicalIF":1.1,"publicationDate":"2025-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13537064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148880501","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

Latent space network model for the popularity effect, with applications to Bitcoin networks. 潜在空间网络模型的普及效应，与比特币网络的应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-05 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2583208

Namgil Lee, Jaehyun Park, Yoonjin Lee, Suji Lee, Nakyung Shin, Hohyun Jung

引用次数: 0

Generating families of distributions using Yun and sigmoidal transformations with an application to exponential distribution. 使用Yun和s型变换生成分布族，并应用于指数分布。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-03 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2581077

Greeshma Chandran, M Manoharan

{"title":"Generating families of distributions using Yun and sigmoidal transformations with an application to exponential distribution.","authors":"Greeshma Chandran, M Manoharan","doi":"10.1080/02664763.2025.2581077","DOIUrl":"10.1080/02664763.2025.2581077","url":null,"abstract":"Modeling complex data in modern applications demands highly flexible probability distributions capable of capturing diverse patterns in behavior. This study presents a novel framework for generating flexible families of distributions by incorporating useful mathematical functions. The proposed method, termed the QT-transformation, is built by combining a quantile function <math><mi>Q</mi> <mo>(</mo> <mo>⋅</mo> <mo>)</mo></math> with a continuous function <math><mi>T</mi> <mo>(</mo> <mo>⋅</mo> <mo>)</mo></math> . Using this approach, we construct two distinct families of distributions based on the Yun and sigmoidal transformations and analyze two sub-models derived from the exponential distribution as the baseline. Structural properties such as skewness, kurtosis, reliability characteristics, and tail behavior are thoroughly examined. The proposed models are shown to belong to several important classes of distributions. Parameter estimation is carried out using both the maximum likelihood estimation (MLE) and maximum product spacing (MPS) methods, and asymptotic confidence intervals are also constructed. The performance of the estimators is evaluated through extensive Monte Carlo simulations based on absolute bias, mean squared error, and confidence interval length. Finally, the practical utility of the proposed models is demonstrated through applications to two real-world datasets. Thus, the study introduces a unified and versatile distribution-generating mechanism that enhances lifetime analysis capabilities and offers broad applicability across statistical disciplines.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 11","pages":"2055-2083"},"PeriodicalIF":1.1,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13537068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148880490","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

An integrated distribution-free approach to monitor multiple process aspects with application in delivery time management. 一种集成的无分布方法，用于监控多个过程方面，并应用于交付时间管理。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-01 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2575041

Amitava Mukherjee, Hidetoshi Murakami

{"title":"An integrated distribution-free approach to monitor multiple process aspects with application in delivery time management.","authors":"Amitava Mukherjee, Hidetoshi Murakami","doi":"10.1080/02664763.2025.2575041","DOIUrl":"10.1080/02664763.2025.2575041","url":null,"abstract":"Monitoring time between events, operational delays or responding to a customer call is essential for maintaining and enhancing service quality. Several aspects of the processes, including location such as median time, variability and shape, are pivotal. This paper introduces a new and innovative distribution-free cumulative sum (CUSUM) procedure based on a combination of three orthogonal rank statistics, following from the Legendre polynomials, for simultaneously monitoring location, scale, and skewness aspects in the context of Phase-II applications. Implementation design and post-signal follow-up procedure for identifying which parameter is more responsible for the signal are discussed. The in-control robustness of the proposed scheme is studied via simulation. The run-length properties of the proposed scheme are compared with various CUSUM schemes. The proposed scheme displays outstanding out-of-control performance in identifying a broad class of shifts involving one or more of the three parameters in an underlying process distribution. An illustration of the proposed scheme for monitoring the time it takes to deliver food items by an e-commerce facility is presented to explain manufacturing quality monitoring applications. We finally offer some concluding remarks.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 10","pages":"1860-1888"},"PeriodicalIF":1.1,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630535","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

Acceptance sampling-based investment indicators for financial security selection and portfolio optimization. 基于接受抽样的金融证券投资指标选择与投资组合优化。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-30 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2578662

Naveen Kumar, Vivek Vijay

{"title":"Acceptance sampling-based investment indicators for financial security selection and portfolio optimization.","authors":"Naveen Kumar, Vivek Vijay","doi":"10.1080/02664763.2025.2578662","DOIUrl":"10.1080/02664763.2025.2578662","url":null,"abstract":"We propose simple and double acceptance sampling investment indicators designed to determine the acceptance or rejection of financial securities for investment based on a tolerable threshold assessed through historical data. These indicators alleviate the burden of including underperforming securities in a portfolio and determine the optimal sample size required for decisions at a given significance level. Both the investment indicators are defined to address the perspectives of buyers and sellers of securities. The proposed double acceptance sampling-based indicator extends the simple sampling-based indicator, offering equivalent information with a smaller sample. Furthermore, we develop a generalized mean-variance portfolio optimization model that includes the buyer's risk and entropy-based uncertainty in decision-making for selected securities. The applicability of these indicators and the model's generalizability are illustrated using DJIA 30 index stocks, showcasing reduced portfolio size while producing equivalent or higher portfolio returns as compared to the Markowitz model.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 10","pages":"1985-2011"},"PeriodicalIF":1.1,"publicationDate":"2025-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630518","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