Journal of Applied Statistics最新文献_第2页

Recovering house price trajectories from sparse transaction data using functional principal component analysis. 利用功能主成分分析从稀疏交易数据中恢复房价轨迹。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-01-03 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2611298

Xiaolei Wang, Naiming Xie, Jiguo Cao

引用次数: 0

Efficient Bayesian variable selection with reversible jump MCMC in imaging genetics: an application to schizophrenia. 影像遗传学中具有可逆跳跃MCMC的有效贝叶斯变量选择：在精神分裂症中的应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-01-01 DOI: 10.1080/02664763.2025.2606972

Djidenou Montcho, Daiane A Zuanetti, Thierry Chekouo, Luis A Milan

引用次数: 0

Modeling extreme events in the presence of inliers: a mixture approach. 模拟存在内线的极端事件：一种混合方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-28 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2606244

Shivshankar Nila, Ishapathik Das, N Balakrishna

{"title":"Modeling extreme events in the presence of inliers: a mixture approach.","authors":"Shivshankar Nila, Ishapathik Das, N Balakrishna","doi":"10.1080/02664763.2025.2606244","DOIUrl":"https://doi.org/10.1080/02664763.2025.2606244","url":null,"abstract":"In many real-life situations, such as life testing experiments and environmental studies, including rainfall, snowfall, streamflow, and soil moisture, responses contain an excess of zero observations, which are known as inliers at zero. Standard modeling approaches fail to provide expected results if these inliers are not considered in the model. Similar challenges arise in extreme value analysis, where accurate tail estimation depends on the appropriate threshold selection. Inliers can adversely affect both threshold estimation and tail behavior modeling if not properly considered. Although some extreme value mixture models address threshold and tail estimation, they often ignore inliers or don't account thoroughly, leading to suboptimal results. There is no unified framework for defining extreme value mixture models; the misspecification of the bulk model can affect the threshold, tail estimates, and particularly the tail proportion. This paper introduces a framework for modeling extreme events, addressing threshold uncertainty, and capturing inliers at zero. The model parameters are estimated using maximum likelihood estimation, ensuring precision. We compare the proposed model with classical mean excess and parameter stability plot estimates. Theoretical results are established, and the model's utility is shown through real applications. Simulation studies and real data examples demonstrate that the proposed model significantly outperforms traditional methods, which typically neglect inliers.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 13","pages":"2520-2552"},"PeriodicalIF":1.1,"publicationDate":"2025-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149110091","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 new causal rule learning approach to interpretable estimation of heterogeneous treatment effect. 异质治疗效果可解释估计的新因果规则学习方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-24 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2606968

Ying Wu, Hanzhong Liu, Kai Ren, Shujie Ma, Xiangyu Chang

{"title":"A new causal rule learning approach to interpretable estimation of heterogeneous treatment effect.","authors":"Ying Wu, Hanzhong Liu, Kai Ren, Shujie Ma, Xiangyu Chang","doi":"10.1080/02664763.2025.2606968","DOIUrl":"https://doi.org/10.1080/02664763.2025.2606968","url":null,"abstract":"Interpretability plays a crucial role in the application of statistical learning to estimate heterogeneous treatment effects (HTE) in complex diseases. In this study, we leverage a rule-based workflow, namely causal rule learning (CRL), to estimate and improve our understanding of HTE for atrial septal defect, addressing an overlooked question in the previous literature: what if an individual simultaneously belongs to multiple groups with different average treatment effects? The CRL process consists of three steps: rule discovery, which generates a set of causal rules with corresponding subgroup average treatment effects; rule selection, which identifies a subset of these rules to deconstruct individual-level treatment effects as a linear combination of subgroup-level effects; and rule analysis, which presents a detailed procedure for further analyzing each selected rule from multiple perspectives to identify the most promising rules for validation. Extensive simulation studies and real-world data analysis demonstrate that CRL outperforms other methods in providing interpretable estimates of HTE, especially when dealing with complex ground truth and sufficient sample sizes.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 13","pages":"2602-2631"},"PeriodicalIF":1.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149110083","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

Power comparisons for testing goodness of fit for constrained models on high-dimensional cross-classified tables. 检验高维交叉分类表上约束模型拟合优度的功率比较。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-24 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2606978

Mark Reiser, Lulu Wang, Hazar A Khogeer

{"title":"Power comparisons for testing goodness of fit for constrained models on high-dimensional cross-classified tables.","authors":"Mark Reiser, Lulu Wang, Hazar A Khogeer","doi":"10.1080/02664763.2025.2606978","DOIUrl":"https://doi.org/10.1080/02664763.2025.2606978","url":null,"abstract":"In applications of models for cross-classified categorical variables, goodness of fit (GOF) is commonly assessed by either Pearson's <math><msup><mi>X</mi> <mn>2</mn></msup> </math> statistic or the likelihood ratio statistic with reference to an asymptotic chi-square approximation. However, if the number of cross-classified variables is large, difficulties are encountered in obtaining a valid test using the traditional statistics due to sparseness and dilution of power. Because of these difficulties, several asymptotic chi-square GOF statistics that focus on lower-order marginal distributions have been proposed. Another method that generally does not encounter the difficulties caused by sparseness and dilution is the likelihood ratio difference test of a nested model. In this paper, Monte Carlo simulations are used to compare the power of a test of fit on lower-order marginals to the power of a test using the likelihood ratio difference statistic for detecting lack of fit in a constrained latent variable model. Results show that the likelihood ratio difference test has substantially higher power. We recommend using a statistic based on lower-order marginals to test the unconstrained model before applying the likelihood ratio difference test. Then, if the general model has an adequate fit, we recommend testing the constrained model using a likelihood ratio difference statistic.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 13","pages":"2669-2686"},"PeriodicalIF":1.1,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149110141","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

Nested functional ANOVA with pointwise and global inference: theory and application to COVID-Era financial data. 基于点和全局推理的嵌套函数方差分析：新冠时代金融数据的理论与应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-23 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2606963

N Celik

{"title":"Nested functional ANOVA with pointwise and global inference: theory and application to COVID-Era financial data.","authors":"N Celik","doi":"10.1080/02664763.2025.2606963","DOIUrl":"https://doi.org/10.1080/02664763.2025.2606963","url":null,"abstract":"We propose a nested functional analysis of variance (FANOVA) model to investigate hierarchical variation structures in functional data. The model extends classical ANOVA by incorporating both main effects and nested subgroup effects, allowing for the decomposition of functional variation across multiple levels. Model parameters are estimated using matrix-based representations under functional linear model assumptions. For hypothesis testing, we develop pointwise F-type statistics and global tests based on the <math><msup><mi>L</mi> <mn>2</mn></msup> </math> norm of the effect functions, with null distributions approximated via Welch-Satterthwaite techniques. Through extensive simulation studies, we assess the accuracy of parameter estimation, control of Type I error, and power under various noise levels, sample sizes, and contamination scenarios. The methodology is further illustrated through an application to stock market index data from 24 countries during the COVID-19 pandemic, where we analyze regional and nested economic effects. The results demonstrate that the proposed nested FANOVA framework effectively captures structured functional variation and remains robust under a range of realistic data conditions.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 13","pages":"2574-2589"},"PeriodicalIF":1.1,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149110086","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 multicomponent stress-strength reliability under competing risk. 竞争风险下多构件应力-强度可靠性研究。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-23 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2606971

Çağatay Çetinkaya, İsmet Birbiçer, Farha Sultana

引用次数: 0

Statistical power analysis of data with zero-inflation under hurdle regression models. 障碍回归模型下零通货膨胀数据的统计能力分析。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-23 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2606245

Mehboob Hussain, Sajid Ali, Ismail Shah

引用次数: 0

A novel variable selection method in nonlinear multivariate models using B-splines with an application to geoscience. 基于b样条的非线性多元模型变量选择新方法及其在地球科学中的应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-09 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2599838

Mary E Savino, Céline Lévy-Leduc

{"title":"A novel variable selection method in nonlinear multivariate models using B-splines with an application to geoscience.","authors":"Mary E Savino, Céline Lévy-Leduc","doi":"10.1080/02664763.2025.2599838","DOIUrl":"10.1080/02664763.2025.2599838","url":null,"abstract":"In this article, we introduce a novel data-driven variable selection approach in a multivariate nonparametric regression model designed to capture only the variables on which the regression function depends. The core concept of our method consists in approximating the underlying function by a linear combination of B-splines of order M and their pairwise interactions. The coefficients of this linear combination are estimated by minimizing the standard least-squares criterion penalized by the sum of the <math><msub><mi>ℓ</mi> <mn>2</mn></msub> </math> -norms of the partial derivatives with respect to the different variables on which the function depends. We validate our approach through numerical experiments varying the number of observations, the noise level and the total number of variables and compare it to four other state-of-the-art methods. An application to a geochemical system based on calcite precipitation is also considered. In most of these contexts, our approach outperforms the other procedures. Our completely data-driven method is implemented in the absorber R package which is available on the Comprehensive R Archive Network (CRAN).","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 12","pages":"2441-2467"},"PeriodicalIF":1.1,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912220","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 composite likelihood approach to gaussian network differentiation with application to epigenetics. 高斯网络微分的复合似然方法及其在表观遗传学中的应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-05 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2596185

James O Adefisoye, S Hasan Arshad, Hongmei Zhang

引用次数: 0