Asta-Advances in Statistical Analysis最新文献_第2页

Enhancing accuracy in surveys obtained by respondent-driven sampling with bias adjustment techniques 通过偏差调整技术提高被调查者驱动的抽样调查的准确性

IF 1.6 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-12-13 DOI: 10.1007/s10182-025-00548-z

Ismael Sánchez-Borrego, Jorge L. Rueda-Sánchez, María del Mar Rueda

{"title":"Enhancing accuracy in surveys obtained by respondent-driven sampling with bias adjustment techniques","authors":"Ismael Sánchez-Borrego, Jorge L. Rueda-Sánchez, María del Mar Rueda","doi":"10.1007/s10182-025-00548-z","DOIUrl":"10.1007/s10182-025-00548-z","url":null,"abstract":"<div><p>Respondent-driven sampling (RDS) is a network-based sampling technique widely used to survey hidden or hard-to-reach populations. Existing estimators rely on strong assumptions intended to ensure the unbiasedness of RDS estimators. Even though survey practitioners employ various practical tools to account for them, these assumptions are often unrealistic and challenging to satisfy in practice. In particular, when working with real-world survey data obtained through this methodology, RDS estimators may demonstrate poor performance, exhibiting notable bias. In this context, several strategies to adjust for selection biases inherent in non-probability samples, such as those that use propensity score adjustment, statistical matching and double robust estimators, are available and can outperform conventional RDS estimators. This study investigates the performance of such estimators with a simulated population designed to deviate from certain assumptions of respondent-driven sampling, and with an empirically collected network dataset. We examine how the number of seeds affects the performance of the RDS methodology by using different number of seeds to the first simulated population. We also examine network structures featuring individuals with exceptionally high centrality, along with structures where connections are either concentrated within small groups or more evenly distributed across groups. Our analysis reveals that these alternative estimators can outperform conventional RDS estimators, particularly in scenarios where the underlying assumptions of RDS are violated. This study represents the first application of these methods and techniques within an RDS framework.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 3","pages":"519 - 549"},"PeriodicalIF":1.6,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in spatial econometrics and geostatistics: methods, theory, and applications 空间计量经济学与地质统计学进展：方法、理论与应用

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-12-12 DOI: 10.1007/s10182-025-00551-4

Philipp Otto, Janine Illian

引用次数: 0

Fuzzy C-modes clustering with spatial regularization and noise cluster 基于空间正则化和噪声聚类的模糊c模聚类

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-11-21 DOI: 10.1007/s10182-025-00547-0

Pierpaolo D’Urso, Livia De Giovanni, Lorenzo Federico, Vincenzina Vitale

引用次数: 0

Margin-closed regime-switching multivariate time series models 边际封闭状态切换多变量时间序列模型

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-11-14 DOI: 10.1007/s10182-025-00546-1

Lin Zhang, Harry Joe, Natalia Nolde

引用次数: 0

Confidence intervals for the overlap coefficient under multimodal distributions: with an application to breast cancer diagnosis 多模态分布下重叠系数的置信区间：在乳腺癌诊断中的应用

IF 1.6 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-11-14 DOI: 10.1007/s10182-025-00545-2

A. Alcaraz-Peñalba, A.M. Franco-Pereira, M. C. Pardo

{"title":"Confidence intervals for the overlap coefficient under multimodal distributions: with an application to breast cancer diagnosis","authors":"A. Alcaraz-Peñalba, A.M. Franco-Pereira, M. C. Pardo","doi":"10.1007/s10182-025-00545-2","DOIUrl":"10.1007/s10182-025-00545-2","url":null,"abstract":"<div><p>In medical diagnostic research, medical diagnostic tests with continuous values are widely employed to distinguish between diseased and non-diseased subjects. In light of the overlap coefficient’s (OVL) practical relevance and intuitive interpretability as a measure of distributional similarity, this measure has been proposed to assess the accuracy of these tests. Several inference methods for constructing confidence intervals for OVL have recently been developed. However, the most promising inference methods for constructing confidence intervals have not yet been systematically compared. In this paper, we first compare two of the most accurate approaches. The performances of these methods are evaluated empirically under a variety of distributional scenarios. At sight of these results, a novel delta method for mixtures of normal distributions is introduced and compared with the generalized pivotal quantity-based approach. Finally, all these approaches are applied to datasets on diabetes and breast cancer diagnosis to assess the glucose and the mean radius of the tumor cells, respectively.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 3","pages":"613 - 637"},"PeriodicalIF":1.6,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148807484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An unbiased theoretical estimator for the case fatality rate 病死率的无偏理论估计

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-10-08 DOI: 10.1007/s10182-025-00543-4

Agustín Alvarez, Marina Fragalá, Marina Valdora

{"title":"An unbiased theoretical estimator for the case fatality rate","authors":"Agustín Alvarez, Marina Fragalá, Marina Valdora","doi":"10.1007/s10182-025-00543-4","DOIUrl":"10.1007/s10182-025-00543-4","url":null,"abstract":"<div><p>During an epidemic outbreak of a new disease, the probability of dying once infected is considered an important though difficult task to be computed. Since it is very hard to know the true number of infected people, the focus is placed on estimating the case fatality rate, which is defined as the probability of dying once tested and confirmed as infected. The estimation of this rate at the beginning of an epidemic remains challenging for several reasons, including the time gap between diagnosis and death, and the rapid growth in the number of confirmed cases. In this work, an unbiased estimator of the case fatality rate of a virus is presented. The consistency of the estimator is demonstrated, and its asymptotic distribution is derived, enabling the corresponding confidence intervals (C.I.) to be established. The proposed method is based on the distribution <i>F</i> of the time between confirmation and death of individuals who die because of the virus. The estimator’s performance is analyzed in both simulation scenarios and the real-world context of Argentina in 2020 for the COVID-19 pandemic, consistently achieving excellent results when compared to an existing proposal as well as to the conventional “naive” estimator that was employed to report the case fatality rates during the last COVID-19 pandemic. In the simulated scenarios, the empirical coverage of our C.I. is studied, both using the <i>F</i> employed to generate the data and an estimated <i>F</i>, and it is observed that the desired level of confidence is reached quickly when using real <i>F</i> and in a reasonable period of time when estimating <i>F</i>.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 1","pages":"91 - 115"},"PeriodicalIF":1.4,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147337557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Machine learning approach for analyzing mixed case interval censored data with a cured subgroup 带有治愈子群的混合案例区间截尾数据分析的机器学习方法。

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-10-06 DOI: 10.1007/s10182-025-00544-3

Wisdom Aselisewine, Suvra Pal

{"title":"Machine learning approach for analyzing mixed case interval censored data with a cured subgroup","authors":"Wisdom Aselisewine, Suvra Pal","doi":"10.1007/s10182-025-00544-3","DOIUrl":"10.1007/s10182-025-00544-3","url":null,"abstract":"<div><p>We introduce a novel two-component framework for analyzing mixed case interval censored (MCIC) data featuring a cured subgroup. In such data, the time-to-event is known only within certain intervals determined by multiple random examination time points. Moreover, a portion of the subjects will never experience the event. The first component of our model focuses on estimating the likelihood of being cured (incidence), departing from the conventional generalized linear model to adopt a more adaptable support vector machine (SVM) approach capable of accommodating complex or non-linear covariate effects. The second component addresses the survival distribution of the uncured individuals (latency) and employs a Cox proportional hazards structure to maintain the straightforward interpretation of covariate effects. We develop an expectation maximization algorithm, incorporating the Platt scaling method, to estimate the probability of being cured. Our simulation study demonstrates that our model outperforms both logit-based and spline-based models in capturing complex classification boundaries, leading to more accurate estimates of cured/uncured probabilities and enhanced predictive accuracy for cure. We emphasize that enhancing the estimation accuracy regarding incidence subsequently improves the estimation outcomes concerning latency. Finally, we illustrate the efficacy of our methodology by applying it to the NASA’s hypobaric decompression sickness data.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 2","pages":"265 - 288"},"PeriodicalIF":1.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145281887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Testing modified zeros for marginal Poisson regression models 检验边际泊松回归模型的修正零点

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-09-29 DOI: 10.1007/s10182-025-00542-5

Tingjie Zhao, Hua He, John Lefante, Wan Tang

{"title":"Testing modified zeros for marginal Poisson regression models","authors":"Tingjie Zhao, Hua He, John Lefante, Wan Tang","doi":"10.1007/s10182-025-00542-5","DOIUrl":"10.1007/s10182-025-00542-5","url":null,"abstract":"<div><p>Marginal Poisson regression models are a common choice for modeling count responses in longitudinal studies. However, these models often encounter a high prevalence of zero counts in the outcome variable, potentially exceeding what is expected by the Poisson distribution. Such zero-inflated scenarios can lead to overdispersion, compromising the validity of inferences derived from Poisson regression models. In this paper, we introduce novel tests for detecting zero inflation or more broadly, zero modification (including both zero inflation and zero deflation) within marginal Poisson regression models. Our methodology involves a direct comparison between the observed frequency of zeros and the expected count based on Poisson models for longitudinal data, obviating the need to specify zero-inflated Poisson models. We assess the performance of these tests through comprehensive simulation studies, evaluating their ability to control Type I errors and achieve statistical power under various conditions. Our findings demonstrate that the proposed test statistics exhibit robust performance across a wide spectrum of scenarios. The methods are illustrated using data from a clinical trial on urinary catheter management.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 2","pages":"347 - 369"},"PeriodicalIF":1.4,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new estimator for the constrained regime-switching model 约束状态切换模型的一种新的估计器

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-09-25 DOI: 10.1007/s10182-025-00540-7

Andrea Beccarini

引用次数: 0

Bayesian analysis of censored measurement error models using finite mixture of heavy-tailed distributions 重尾分布有限混合截尾测量误差模型的贝叶斯分析

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-09-16 DOI: 10.1007/s10182-025-00539-0

Jie Jiang, Lichun Wang, Liqun Wang

引用次数: 0