Journal of Applied Statistics最新文献

{"title":"One parameter stochastic distribution of ratio for cross-disciplinary applications: system reliability, healthcare, and energy consumption.","authors":"Muhammad Ilyas, Muhammad Mohsin","doi":"10.1080/02664763.2025.2525884","DOIUrl":"10.1080/02664763.2025.2525884","url":null,"abstract":"<p><p>Statistical modeling of real-life phenomena analyzes their behavior and patterns by quantifying the inherent uncertainty and describes the possible outcomes which help to make proactive strategies attaining optimum results. This paper introduces a one parameter stochastic distribution of ratio derived from the bivariate conditional Weibull distribution to model the data sets from diverse fields such as system reliability, healthcare and energy consumption. Some important features and statistical properties of the proposed distribution are presented. A simulation study is conducted by using the acceptance-rejection method to evaluate the stability of the model parameter based on its average estimated values, standard errors, and biases. The parameter of the proposed distribution is estimated by using maximum likelihood and Bayesian methods. The proposed model and some extant models are employed on the ratio of downtimes to the total times, case fatality rate and proportion of cooling energy. The comparison of the results establishes the competency of the proposed model. The estimated quantiles for the three different data sets provide insights about the potential range of system reliability in terms of downtime to the total time, case fatality rate and energy consumption for cooling the buildings.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 4","pages":"590-613"},"PeriodicalIF":1.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12985401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468115","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}

{"title":"A constrained robust Markov regime-switching model for long-term risk evaluation.","authors":"Shanshan Qin, Beibei Guo, Yuehua Wu, Hong Xie, Jingjing Dong","doi":"10.1080/02664763.2025.2525880","DOIUrl":"10.1080/02664763.2025.2525880","url":null,"abstract":"<p><p>Markov Regime-Switching (MRS) models are widely used for modeling equity return time series. Yet standard MRS models may inadequately capture the mean reversion behavior of long-term equity returns and exhibit unstable parameter estimation due to their reliance on normality assumptions within each regime. These limitations in model adequacy can compromise the accuracy of risk exposure measurements for invested assets. To address these issues, we propose a constrained robust MRS (CRMRS) model, which integrates an order restriction and sparse constraints on regime means and transition probabilities to better capture mean reversion while employing a general <i>ρ</i>-based least favorable distribution to improve distributional flexibility across regimes. We assess the method's performance through finite-sample simulations under various scenarios in the presence or absence of atypical values. Furthermore, we empirically validate the improvements in model adequacy and risk exposure measurement using monthly returns from the S&P/TSX Composite Index, the benchmark for Canadian equity performance, where S&P and TSX stand for Standard & Poor's and the Toronto Stock Exchange, respectively. Our findings demonstrate that the proposed CRMRS-Huber produces stable parameter estimates and superior approximations of higher-order moments, such as skewness and kurtosis, and provides balanced intermediate risk evaluation across all cases.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 4","pages":"574-589"},"PeriodicalIF":1.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12981263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468108","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}

{"title":"A gradient boosting decision tree based estimation method for the mixture cure model.","authors":"Jianing Zheng, Peizhi Li, Yingwei Peng","doi":"10.1080/02664763.2025.2520337","DOIUrl":"10.1080/02664763.2025.2520337","url":null,"abstract":"<p><p>Cure models are useful tools for analyzing censored survival data with a cured fraction. However, existing semiparametric estimation methods still rely on restrictive parametric assumptions, and existing nonparametric estimation methods only work with single covariates. In this work, we propose a gradient boosting decision tree based method to estimate the mixture cure model. The new method inherits the features of the original gradient boosting decision tree method and provides more accurate estimates of the cure probability and the relative risk for uncured subjects than existing methods when there are no a priori parametric assumptions on the forms of complex covariate effects in the model. This is demonstrated with small mean square errors in the estimates of the cure probability, relative risk score, and survival function in a simulation study with large samples. The method also has the potential to deal with high-dimensional covariates. The proposed method is illustrated with a large sample study of colon cancer.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 3","pages":"484-509"},"PeriodicalIF":1.1,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354972","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}

{"title":"Bradford distribution and its application in modeling medical data: a suitable alternative to distributions defined on the unit interval.","authors":"Indranil Ghosh","doi":"10.1080/02664763.2025.2520342","DOIUrl":"10.1080/02664763.2025.2520342","url":null,"abstract":"<p><p>It has been observed that quite often, in health-related studies/clinical trials data, we frequently come across left/right skewed heavy-tailed survival data for which the available probability models from the existing literature are not adequate. In this article, we explore and study a new probability distribution with a bounded domain, namely the Bradford distribution with a single parameter <i>θ</i>, henceforth, in short BF(<i>θ</i>). For this distribution, several tractability advantages (in particular, for modeling medical data) have not been explored, to date, to the best of the knowledge of the author. The BF(<i>θ</i>) distribution represents the advantage of not involving any special function in its formulation. We provide a comprehensive study of general mathematical and statistical properties of this distribution that are desirable traits when fitting clinical trials/health data among many others. The utility of this distribution is highlighted via the application/(re-analysis of) in two different well-known datasets-(i) Arthritis pain relief data, and (ii) Tissue damage dataset for two different objectives. Using information theoretic criteria, we compare the performance of the BF(<i>θ</i>) distribution with several rival probability models defined on the unit interval, such as the beta, Kumaraswamy, bounded-weighted exponential, unit Weibull, and many more. The usefulness of the current paper is manifold, and can be summarized as follows: First, the BF(<i>θ</i>) distribution cannot be obtained via some routine transformation from any of the well-known absolutely continuous univariate random variables, and therefore, it augments the existing literature on statistical distribution theory.The BF(<i>θ</i>) distribution has simple and analytically tractable forms for the associated density and quantile function. Since the quantile function is in a closed form, this distribution can be utilized to model censored data which are an important feature. Specifically, this distribution has a clear advantage over the class of univariate distributions defined on the unit interval and that is, it has only one parameter. Needless to say, the associated inferential measures, such as the MLE (discussed in Section 3), are very easy to implement.Among several existing univariate absolutely continuous probability models, one of the major limitations is that they do not perform satisfactorily well for small samples, because of the presence of two or more model parameters. In contrast, the BF(<i>θ</i>) distribution appears to be working well in the case of small samples (such as for a sample of size 12, see Section 4.2). The above set of arguments warrant for a separate and detailed study for the BF(<i>θ</i>) distribution.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 3","pages":"520-536"},"PeriodicalIF":1.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354937","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}

{"title":"Nonparametric tolerance intervals controlling percentages in both tails and simultaneous testing of quantiles.","authors":"Gboyega Adepoju, K Krishnamoorthy","doi":"10.1080/02664763.2025.2520339","DOIUrl":"10.1080/02664763.2025.2520339","url":null,"abstract":"<p><p>The problem of constructing nonparametric equal-tailed tolerance intervals is considered. A method for finding minimum sample size required so that the <i>r</i>th order statistic and the largest <i>r</i>th order statistic form a <i>p</i> content- <math><mo>(</mo> <mn>1</mn> <mo>-</mo> <mi>α</mi> <mo>)</mo></math> coverage equal-tailed tolerance interval is proposed. A simultaneous test for quantiles is also provided. Minimum sample sizes that are required to construct equal-tailed TIs and those to carry out hypothesis tests at a given nominal level are presented. The methods are illustrated using data collected from a new secure messaging system.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 3","pages":"510-519"},"PeriodicalIF":1.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354916","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}

{"title":"Detection of a core-periphery structure in bipartite user-content networks based on modularity and Stochastic Block Model.","authors":"Lorenzo Federico, Livia De Giovanni, Alessandro Chessa, Pierpaolo D'Urso","doi":"10.1080/02664763.2025.2519132","DOIUrl":"10.1080/02664763.2025.2519132","url":null,"abstract":"<p><p>In this paper, we study the patterns of interaction of the general audience with a sample of Italian Twitter accounts representing various organizations. We model the interaction between the audience and the content produced by each account as a bipartite network in which the nodes are on the right side the tweets of the official account and on the left side the users in the audience, and edges represent direct interactions such as retweets and replies. First, we show how these networks behave very differently from the usual null models for bipartite networks with highly inhomogeneous degrees such as the bipartite configuration model, uniform bipartite graph and the 'soft' configuration model. In particular, we show that these networks exhibit a bipartite core-periphery organization using a Degree Corrected Stochastic Block Model. Finally, through a bipartite modularity optimization, we study how this core-periphery organization reflects in the community structure within each network and find that most communities are made up of one or a few star graphs around either a few core users or a few core tweets.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 3","pages":"447-461"},"PeriodicalIF":1.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954807/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354992","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}

{"title":"Influence networks: Bayesian modeling and diffusion.","authors":"Samuel Sánchez-Gutiérrez, Juan Sosa, Carolina Luque","doi":"10.1080/02664763.2025.2519139","DOIUrl":"10.1080/02664763.2025.2519139","url":null,"abstract":"<p><p>In this article, we make an innovative adaptation of a Bayesian latent space model based on projections in a novel way to analyze influence networks. By appropriately reparameterizing the model, we establish a formal metric for quantifying each individual's influencing capacity and estimating their latent position embedded in a social space. This modeling approach introduces a novel mechanism for simulating the diffusion of an idea based on the estimated latent characteristics. It assumes that each individual takes the following states: Unknown, undecided, supporting, or rejecting an idea. This approach is demonstrated using an influence network from Twitter (now <math><mrow><mi>X</mi></mrow> </math> ) related to the 2022 Tax Reform in Colombia. An exhaustive simulation exercise is also performed to evaluate the proposed diffusion process.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 3","pages":"462-483"},"PeriodicalIF":1.1,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354997","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}

{"title":"Focused information criteria for model selection - a Bayesian perspective.","authors":"Bijit Roy, Emmanuel Lesaffre","doi":"10.1080/02664763.2025.2514152","DOIUrl":"10.1080/02664763.2025.2514152","url":null,"abstract":"<p><p>Most model selection criteria, such as Akaike's Information Criterion or Widely Applicable Information Criterion are based on a global measure of out of sample prediction accuracy for the responses. But such criteria may not do a good job when interest is in a particular aspect of the model such as a particular model parameter. The Focused Information Criterion is a model selection tool that has been suggested for this purpose in a frequentist context. It is a measure of the mean square error of the focus parameters, which is then used for model selection. Here we look at its Bayesian analog, the Bayesian Focused Information Criterion, where we use the posterior distribution of the focus parameter(s) to estimate its mean square error. We illustrate our proposed model selection criteria on a longitudinal growth data set of newborns with the goal to study differences in BMI trajectory development among different birth weight classes. We use the average BMI at one year age for the different subgroups as the focus parameter and use it to choose an appropriate model that addresses the research question.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 3","pages":"412-430"},"PeriodicalIF":1.1,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147355001","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}

{"title":"Robust Bayesian model averaging for linear regression models with heavy-tailed errors.","authors":"Shamriddha De, Joyee Ghosh","doi":"10.1080/02664763.2025.2511938","DOIUrl":"10.1080/02664763.2025.2511938","url":null,"abstract":"<p><p>Our goal is to develop a Bayesian model averaging technique in linear regression models that accommodates heavier tailed error densities than the normal distribution. Motivated by the use of the Huber loss function in the presence of outliers, the Bayesian Huberized lasso with hyperbolic errors has been proposed and recently implemented in the literature. Since the Huberized lasso cannot enforce regression coefficients to be exactly zero, we propose a Bayesian variable selection approach with spike and slab priors to address sparsity more effectively. The shapes of the hyperbolic and the Student-<i>t</i> density functions differ. Furthermore, the tails of a hyperbolic distribution are less heavy compared to those of a Cauchy distribution. Thus, we propose a flexible regression model with an error distribution encompassing both the hyperbolic and the Student-<i>t</i> family of distributions, with an unknown tail heaviness parameter, that is estimated based on the data. It is known that the limiting form of both the hyperbolic and the Student-<i>t</i> distributions is a normal distribution. We develop an efficient Gibbs sampler for posterior computation. Through simulation studies and analyzes of real datasets, we show that our method is competitive with various state-of-the-art methods.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 2","pages":"304-330"},"PeriodicalIF":1.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12872095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146125196","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}

{"title":"The functional gait deviation index.","authors":"Sajal Kaur Minhas, Morgan Sangeux, Julia Polak, Michelle Carey","doi":"10.1080/02664763.2025.2514150","DOIUrl":"10.1080/02664763.2025.2514150","url":null,"abstract":"<p><p>A typical gait analysis requires the examination of the motion of nine joint angles on the left-hand side and six joint angles on the right-hand side across multiple subjects. Due to the quantity and complexity of the data, it is useful to calculate the amount by which a subject's gait deviates from an average normal profile and to represent this deviation as a single number. Such a measure can quantify the overall severity of a condition affecting walking, monitor progress, or evaluate the outcome of an intervention prescribed to improve the gait pattern. The gait deviation index, gait profile score, and the overall abnormality measure are standard benchmarks for quantifying gait abnormality. However, these indices do not account for the intrinsic smoothness of the gait movement at each joint/plane and the potential co-variation between the joints/planes. Utilizing a multivariate functional principal component analysis we propose the functional gait deviation index (FGDI). FGDI accounts for the intrinsic smoothness of the gait movement at each joint/plane and the potential co-variation between the joints. We show that FGDI scales with overall gait function, provides a consistent measure of gait abnormality, and is implemented easily using an interactive web app.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 3","pages":"391-411"},"PeriodicalIF":1.1,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354948","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}