Journal of Applied Statistics最新文献_第2页

Inference for depending competing risks from Marshall-Olikin bivariate Kies distribution under generalized progressive hybrid censoring.

IF 1.2 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.

IF 1.2 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":1.2,"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.

IF 1.2 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":1.2,"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.

IF 1.2 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":1.2,"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

Quantile regression based method for characterizing risk-specific behavioral patterns in relation to longitudinal left-censored biomarker data collected from heterogeneous populations 基于量子回归的方法，用于描述与从异质人群收集的纵向左删失生物标记物数据相关的特定风险行为模式

IF 1.5 4区数学

Journal of Applied Statistics Pub Date : 2024-08-27 DOI: 10.1080/02664763.2024.2394784

MinJae Lee, Belinda M. Reininger, Kelley Pettee Gabriel, Nalini Ranjit, Larkin L. Strong

引用次数: 0

A two-sample nonparametric test for one-sided location-scale alternative 对单侧地点尺度备选方案的双样本非参数检验

IF 1.5 4区数学

Journal of Applied Statistics Pub Date : 2024-08-21 DOI: 10.1080/02664763.2024.2392119

Hidetoshi Murakami, Markus Neuhäuser

引用次数: 0

Modeling the time to dropout under phase-wise variable stress fixed cohort setup 在阶段性可变压力固定队列设置下的辍学时间建模

IF 1.5 4区数学

Journal of Applied Statistics Pub Date : 2024-08-19 DOI: 10.1080/02664763.2024.2392113

Aniket Biswas, Subrata Chakraborty, Anupama Nandi

引用次数: 0

Fast algorithms of computing admissible intervals for discrete distributions with single parameter 计算单参数离散分布可容许区间的快速算法

IF 1.5 4区数学

Journal of Applied Statistics Pub Date : 2024-08-17 DOI: 10.1080/02664763.2024.2392105

Weizhen Wang, Chongxiu Yu, Zhongzhan Zhang

引用次数: 0