Journal of Applied Statistics最新文献

An empirical Bayes approach for constructing confidence intervals for clonality and entropy. 构造克隆性和熵置信区间的经验贝叶斯方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-04-30 DOI: 10.1080/02664763.2025.2496724

Zhongren Chen, Lu Tian, Richard A Olshen

{"title":"An empirical Bayes approach for constructing confidence intervals for clonality and entropy.","authors":"Zhongren Chen, Lu Tian, Richard A Olshen","doi":"10.1080/02664763.2025.2496724","DOIUrl":"10.1080/02664763.2025.2496724","url":null,"abstract":"This paper is motivated by the need to quantify human immune responses to environmental challenges. Specifically, the genome of the selected cell population from a blood sample is amplified by the PCR process, producing a large number of reads. Each read corresponds to a particular rearrangement of so-called V(D)J sequences. The observed data consist of a set of integers, representing numbers of reads corresponding to different V(D)J sequences. The underlying relative frequencies of distinct V(D)J sequences can be summarized by a probability vector, with the cardinality being the number of distinct V(D)J rearrangements. The statistical question is to make inferences on a summary parameter of this probability vector based on a multinomial-type observation of a large dimension. Popular summaries of the diversity include clonality and entropy. A point estimator of the clonality based on multiple replicates from the same blood sample has been proposed previously. Therefore, the remaining challenge is to construct confidence intervals of the parameters to reflect their uncertainty. In this paper, we propose to couple the Empirical Bayes method with a resampling-based calibration procedure to construct a robust confidence interval for different population diversity parameters. The method is illustrated via extensive numerical studies and real data examples.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12435542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145075083","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

Quantile regression model for interval-censored data with competing risks. 具有竞争风险的区间截尾数据的分位数回归模型。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-03-15 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2474627

Amirah Afiqah Binti Che Ramli, Yang-Jin Kim

引用次数: 0

Diagnostic analytics for the mixed Poisson INGARCH model with applications. 混合泊松INGARCH模型的诊断分析及其应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-03-12 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2476658

Wenjie Dang, Fukang Zhu, Nuo Xu, Shuangzhe Liu

{"title":"Diagnostic analytics for the mixed Poisson INGARCH model with applications.","authors":"Wenjie Dang, Fukang Zhu, Nuo Xu, Shuangzhe Liu","doi":"10.1080/02664763.2025.2476658","DOIUrl":"https://doi.org/10.1080/02664763.2025.2476658","url":null,"abstract":"In statistical diagnosis and sensitivity analysis, the local influence method plays a crucial role and is sometimes more advantageous than other methods. The mixed Poisson integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) model is built on a flexible family of mixed Poisson distributions. It not only encompasses the negative binomial INGARCH model but also allows for the introduction of the Poisson-inverse Gaussian INGARCH model and the Poisson generalized hyperbolic secant INGARCH model. This paper applies the local influence analysis method to count time series data within the framework of the mixed Poisson INGARCH model. For parameter estimation, the Expectation-Maximization algorithm is utilized. In the context of local influence analysis, two global influence methods (generalized Cook distance and Q-distance) and four perturbations-case weights perturbation, data perturbation, additive perturbation, and scale perturbation-are considered to identify influential points. Finally, the feasibility and effectiveness of the proposed methods are demonstrated through simulations and analysis of a real data set.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 13","pages":"2495-2523"},"PeriodicalIF":1.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12490395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145232698","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

Improving the within-node estimation of survival trees while retaining interpretability. 在保留可解释性的同时，改进生存树的节点内估计。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-03-11 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2473535

Haolin Li, Yiyang Fan, Jianwen Cai

引用次数: 0

Estimating an executive summary of a time series: the tendency. 估计时间序列的执行摘要：趋势。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-03-10 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2475351

Caio Alves, Juan M Restrepo, Jorge M Ramirez

{"title":"Estimating an executive summary of a time series: the tendency.","authors":"Caio Alves, Juan M Restrepo, Jorge M Ramirez","doi":"10.1080/02664763.2025.2475351","DOIUrl":"10.1080/02664763.2025.2475351","url":null,"abstract":"In this paper, we revisit the problem of decomposing a signal into a tendency and a residual. The tendency describes an executive summary of a signal that encapsulates its notable characteristics while disregarding seemingly random, less interesting aspects. Building upon the Intrinsic Time Decomposition (ITD) and information-theoretical analysis, we introduce two alternative procedures for selecting the tendency from the ITD baselines. The first is based on the maximum extrema prominence, namely the maximum difference between extrema within each baseline. Specifically this method selects the tendency as the baseline from which an ITD step would produce the largest decline of the maximum prominence. The second method uses the rotations from the ITD and selects the tendency as the last baseline for which the associated rotation is statistically stationary. We delve into a comparative analysis of the information content and interpretability of the tendencies obtained by our proposed methods and those obtained through conventional low-pass filtering schemes, particularly the Hodrik-Prescott (HP) filter. Our findings underscore a fundamental distinction in the nature and interpretability of these tendencies, highlighting their context-dependent utility with emphasis in multi-scale signals. Through a series of real-world applications, we demonstrate the computational robustness and practical utility of our proposed tendencies, emphasizing their adaptability and relevance in diverse time series contexts.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 13","pages":"2478-2494"},"PeriodicalIF":1.1,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12490379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145232711","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

Hierarchical Bayesian models for small area estimation with GB2 distribution. GB2分布下小面积估计的层次贝叶斯模型。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-03-10 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2475349

Binod Manandhar, Balgobin Nandram

引用次数: 0

Bayes factors for two-group comparisons in Cox regression with an application for reverse-engineering raw data from summary statistics. Cox回归中两组比较的贝叶斯因子与来自汇总统计的逆向工程原始数据的应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-03-01 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2472150

Maximilian Linde, Jorge N Tendeiro, Don van Ravenzwaaij

引用次数: 0

An integrated change point detection and online monitoring approach for the ratio of two variables using clustering-based control charts. 基于聚类控制图的两变量比值变化点检测与在线监测方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-02-14 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2455625

Adel Ahmadi Nadi, Ali Yeganeh, Sandile Charles Shongwe, Alireza Shadman

{"title":"An integrated change point detection and online monitoring approach for the ratio of two variables using clustering-based control charts.","authors":"Adel Ahmadi Nadi, Ali Yeganeh, Sandile Charles Shongwe, Alireza Shadman","doi":"10.1080/02664763.2025.2455625","DOIUrl":"10.1080/02664763.2025.2455625","url":null,"abstract":"Online monitoring of the ratio of two random characteristics rather than monitoring their individual behaviors has many applications. For this aim, there are various control charts, known as RZ charts in the literature, e.g. Shewhart, memory-type and adaptive monitoring schemes, have been designed to detect the ratio's abnormal patterns as soon as possible. Most of the existing RZ charts rely on two assumptions about the process: (i) both individual characteristics are normally distributed, and (ii) the direction (upward or downward) of the RZ's deviation from its in-control (IC) state to an out-of-control (OC) condition is known. However, these assumptions can be violated in many practical situations. In recent years, applying the machine learning (ML) models in the Statistical Process Monitoring (SPM) area has provided several contributions compared to traditional statistical methods. However, ML-based control charts have not yet been discussed in the RZ monitoring literature. To this end, this study introduces a novel clustering-based control chart for monitoring RZ in Phase II. This method avoids making any assumptions about the direction of RZ's deviation and does not need to assume a specific distribution for the two random characteristics. Furthermore, it can estimate the Change Point (CP) in the process.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 11","pages":"2060-2093"},"PeriodicalIF":1.1,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12404067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144992745","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

BSTPP: a python package for Bayesian spatiotemporal point processes. 贝叶斯时空点处理的python包。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-02-11 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2462969

Isaac Manring, Honglang Wang, George Mohler, Xenia Miscouridou

引用次数: 0

On the improved estimation of the normal mixture components for longitudinal data. 纵向数据法向混合分量的改进估计。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-02-07 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2459293

Tapio Nummi, Jyrki Möttönen, Pasi Väkeväinen, Janne Salonen, Timothy E O'Brien

{"title":"On the improved estimation of the normal mixture components for longitudinal data.","authors":"Tapio Nummi, Jyrki Möttönen, Pasi Väkeväinen, Janne Salonen, Timothy E O'Brien","doi":"10.1080/02664763.2025.2459293","DOIUrl":"10.1080/02664763.2025.2459293","url":null,"abstract":"When analyzing real data sets, statisticians often face the question that the data are heterogeneous and it may not necessarily be possible to model this heterogeneity directly. One natural option in this case is to use the methods based on finite mixtures. The key question in these techniques often is what is the best number of mixtures or, depending on the focus of the analysis, the best number of sub-populations when the model is otherwise fixed. Moreover, when the distribution of the response variable deviates from meeting the assumptions, it's common to employ an appropriate transformation to align the distribution with the model's requirements. To solve the problem in the mixture regression context we propose a technique based on the scaled Box-Cox transformation for normal mixtures. The specific focus here is on mixture regression for longitudinal data, the so-called trajectory analysis. We present interesting practical results as well as simulation experiments to demonstrate that our method yields reasonable results. Associated R-programs are also provided.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 12","pages":"2271-2290"},"PeriodicalIF":1.1,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12416014/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029994","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