Journal of Applied Statistics最新文献_第3页

Penalized functional regression using R package PFLR. 惩罚函数回归使用R包PFLR。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-28 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2457011

Rob Cameron, Tianyu Guan, Haolun Shi, Zhenhua Lin

引用次数: 0

Upper quantile-based CUSUM-type control chart for detecting small changes in image data. 基于上分位数的cusum型控制图，用于检测图像数据的微小变化。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-27 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2456614

Anik Roy, Partha Sarathi Mukherjee

{"title":"Upper quantile-based CUSUM-type control chart for detecting small changes in image data.","authors":"Anik Roy, Partha Sarathi Mukherjee","doi":"10.1080/02664763.2025.2456614","DOIUrl":"10.1080/02664763.2025.2456614","url":null,"abstract":"Image monitoring is an important research problem that has wide applications in various fields, including manufacturing industries, satellite imaging, medical diagnostics, and so forth. Traditional image monitoring control charts perform rather poorly when the changes occur at very small regions of the image, and when the changes of image intensity values are small in those regions. Their performances get worse if the images contain noise, and the changes occur near the edges of image objects. In applications such as manufacturing industries, the changes in the images are often too small to be detected by human eyes. In this article, we propose a CUSUM-type control chart for online monitoring of grayscale images. Depending on what kind of changes we wish to detect, big or small, we propose to use a certain upper quantile of the local CUSUM statistics. We incorporate a state-of-the-art jump preserving image smoothing technique in the proposed chart that ensures good performance even in presence of low to moderate noise. Theoretical justifications, and superior performance in numerical comparisons ensure that the proposed control chart can be useful to many researchers and practitioners.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 11","pages":"2156-2171"},"PeriodicalIF":1.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12404064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144992685","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

Derivation of a multivariate longitudinal causal effects model. 多元纵向因果效应模型的推导。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-24 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2457013

Halima S Twabi, Samuel O M Manda, Dylan S Small, Hans-Peter Kohler

{"title":"Derivation of a multivariate longitudinal causal effects model.","authors":"Halima S Twabi, Samuel O M Manda, Dylan S Small, Hans-Peter Kohler","doi":"10.1080/02664763.2025.2457013","DOIUrl":"10.1080/02664763.2025.2457013","url":null,"abstract":"This paper presents a causal inference estimation method for longitudinal observational studies with multiple outcomes. The method uses marginal structural models with inverse probability treatment weights (MSM-IPTWs). In developing the proposed method, we re-define the weights as a product of inverse weights at each time point, accounting for time-varying confounders and treatment exposures and possible correlation between and within (serial) the multiple outcomes. The proposed method is evaluated by simulation studies and with an application to estimate the effect of HIV positivity awareness on condom use and multiple sexual partners using the Malawi Longitudinal Study of Families and Health (MLSFH) data. The simulation study shows that the joint MSM-IPTW performs well with coverage within the expected 95% level for a large sample size (n = 1000) and moderate to strong between and within outcome correlation strength ( <math><msub><mi>ρ</mi> <mi>j</mi></msub> <mo>=</mo> <mn>0.3</mn></math> , 0.75, <math><msub><mi>ρ</mi> <mi>k</mi></msub> <mo>=</mo> <mn>0.4</mn></math> , 0.8) when the effects are similar. The joint MSM-IPTW performed relatively the same as the adjusted standard joint model when the treatment effect estimate was the same for the outcomes. In the application, HIV positivity awareness increased the usage of condoms and did not affect the number of sexual partners. We recommend using the proposed MSM-IPTWs to correctly control for time-varying treatment and confounders when estimating causal effects for longitudinal observational studies with multiple outcomes.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 12","pages":"2207-2225"},"PeriodicalIF":1.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12416008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029929","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

Causal effect estimation for competing risk data in randomized trial: adjusting covariates to gain efficiency. 随机试验中竞争风险数据的因果效应估计：调整协变量以获得效率。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-24 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2455626

Youngjoo Cho, Cheng Zheng, Lihong Qi, Ross L Prentice, Mei-Jie Zhang

{"title":"Causal effect estimation for competing risk data in randomized trial: adjusting covariates to gain efficiency.","authors":"Youngjoo Cho, Cheng Zheng, Lihong Qi, Ross L Prentice, Mei-Jie Zhang","doi":"10.1080/02664763.2025.2455626","DOIUrl":"10.1080/02664763.2025.2455626","url":null,"abstract":"The double-blinded randomized trial is considered the gold standard to estimate the average causal effect (ACE). The naive estimator without adjusting any covariate is consistent. However, incorporating the covariates that are strong predictors of the outcome could reduce the issue of unbalanced covariate distribution between the treated and controlled groups and can improve efficiency. Recent work has shown that thanks to randomization, for linear regression, an estimator under risk consistency (e.g. Random Forest) for the regression coefficients could maintain the convergence rate even when a nonparametric model is assumed for the effect of covariates. Also, such an adjusted estimator will always lead to efficiency gain compared to the naive unadjusted estimator. In this paper, we extend this result to the competing risk data setting and show that under similar assumptions, the augmented inverse probability censoring weighting (AIPCW) based adjusted estimator has the same convergence rate and efficiency gain. Extensive simulations were performed to show the efficiency gain in the finite sample setting. To illustrate our proposed method, we apply it to the Women's Health Initiative (WHI) dietary modification trial studying the effect of a low-fat diet on cardiovascular disease (CVD) related mortality among those who have prior CVD.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 11","pages":"2094-2112"},"PeriodicalIF":1.1,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12404078/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144992709","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

Integrative rank-based regression for multi-source high-dimensional data with multi-type responses. 多源高维数据多类型响应的综合秩回归。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-16 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2452964

Fuzhi Xu, Shuangge Ma, Qingzhao Zhang

引用次数: 0

The slashed Lomax distribution: new properties and Mellin-type statistical measures for inference. 割形洛马克斯分布：新的性质和梅林型推断的统计度量。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-15 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2451977

Jaine de Moura Carvalho, Frank Gomes-Silva, Josimar M Vasconcelos, Gauss M Cordeiro

{"title":"The slashed Lomax distribution: new properties and Mellin-type statistical measures for inference.","authors":"Jaine de Moura Carvalho, Frank Gomes-Silva, Josimar M Vasconcelos, Gauss M Cordeiro","doi":"10.1080/02664763.2025.2451977","DOIUrl":"https://doi.org/10.1080/02664763.2025.2451977","url":null,"abstract":"Several continuous distributions have been proposed recently to provide more flexibility in modeling lifetime data. Among these, the Slashed class of models, particularly the Slashed Lomax ( <math><mrow><mi>SL</mi></mrow> </math> ) distribution, has gained special attention. This asymmetric model has positive support and it is notable for its stochastic representation and ability to fit heavy-tailed datasets. Despite the increasing number of new continuous models catering to specific samples, there have been few statistical tools introduced to evaluate their goodness-of-fits. To address this deficit, we employ the methodology outlined in J.M. Nicolas [Introduction aux statistiques de deuxième espèce: Applications des logs-moments et des logs-cumulants à l'analyse des lois d'images radar, TS, Trait. Signal 19 (2002), pp. 139-167] derived from the Mellin Transform (MT) to provide new goodness-of-fit measures for the <math><mrow><mi>SL</mi></mrow> </math> distribution. These measures consider both qualitative and quantitative aspects. We derive the MT for the <math><mrow><mi>SL</mi></mrow> </math> distribution, calculate the log-cumulants, and construct the log-cumulant diagram. Further, we introduce a test statistic using a combination of Hotelling's <math><msup><mi>T</mi> <mn>2</mn></msup> </math> statistic and the multivariate Delta method to test hypotheses about the log-cumulants. We apply the new methodology to two real databases in the context of survival analysis to show its effectiveness in evaluating the fit criteria. We conduct bootstrap experiments to assess the power of the proposed test and to evaluate the performance of the estimators. The results revealed that the adjustment tools performed well and that the log-cumulant method proved to be an effective estimation criterion.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 10","pages":"1984-2006"},"PeriodicalIF":1.1,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144789266","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

Inference under multivariate size-biased sampling. 多元规模偏差抽样下的推断。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-15 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2451972

A Batsidis, G Tzavelas, P Economou

引用次数: 0

Bayesian doubly robust estimation of causal effects for clustered observational data. 聚类观测数据因果效应的贝叶斯双稳健估计。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-09 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2449396

Qi Zhou, Haonan He, Jie Zhao, Joon Jin Song

引用次数: 0

Weighted portmanteau statistics for testing for zero autocorrelation in dependent data. 用于检验相关数据中零自相关的加权组合统计。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-08 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2449413

N Muriel

引用次数: 0

Statistical inference for dependent competing risks data under adaptive Type-II progressive hybrid censoring. 自适应ii型渐进式混合滤波下依赖竞争风险数据的统计推断。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-01-04 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2445237

Subhankar Dutta, Suchandan Kayal

{"title":"Statistical inference for dependent competing risks data under adaptive Type-II progressive hybrid censoring.","authors":"Subhankar Dutta, Suchandan Kayal","doi":"10.1080/02664763.2024.2445237","DOIUrl":"https://doi.org/10.1080/02664763.2024.2445237","url":null,"abstract":"In this article, we consider statistical inference based on dependent competing risks data from Marshall-Olkin bivariate Weibull distribution. The maximum likelihood estimates of the unknown model parameters have been computed by using Newton-Raphson method under adaptive Type II progressive hybrid censoring with partially observed failure causes. Existence and uniqueness of maximum likelihood estimates are derived. Approximate confidence intervals have been constructed via the observed Fisher information matrix using asymptotic normality property of the maximum likelihood estimates. Bayes estimates and highest posterior density credible intervals have been calculated under gamma-Dirichlet prior distribution by using Markov chain Monte Carlo technique. Convergence of Markov chain Monte Carlo samples is tested. In addition, a Monte Carlo simulation is carried out to compare the effectiveness of the proposed methods. Further, three different optimality criteria have been taken into account to obtain the most effective censoring plans. From these simulation study results it has been concluded that Bayesian technique produces superior outcomes. Finally, a real-life data set has been analyzed to illustrate the operability and applicability of the proposed methods.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 10","pages":"1871-1903"},"PeriodicalIF":1.1,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144789265","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