Asta-Advances in Statistical Analysis最新文献_第4页

High-dimensional confounding adjustment in causal inference 因果推理中的高维混淆调整

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-06-05 DOI: 10.1007/s10182-025-00528-3

Sanghun Cha, Joon Jin Song, Kyeong Eun Lee

引用次数: 0

Using penalized-distance likelihood functions to analyze high-dimensional sparse/non-sparse data 使用惩罚距离似然函数分析高维稀疏/非稀疏数据

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-05-30 DOI: 10.1007/s10182-025-00527-4

S. K. Ghoreishi, Jingjing Wu, Qingrun Zhang, Ghazal S. Ghoreishi

引用次数: 0

Gradient boosting for Dirichlet regression models Dirichlet回归模型的梯度增强

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-04-28 DOI: 10.1007/s10182-025-00526-5

Michael Balzer, Elisabeth Bergherr, Swen Hutter, Tobias Hepp

{"title":"Gradient boosting for Dirichlet regression models","authors":"Michael Balzer, Elisabeth Bergherr, Swen Hutter, Tobias Hepp","doi":"10.1007/s10182-025-00526-5","DOIUrl":"10.1007/s10182-025-00526-5","url":null,"abstract":"<div><p>In various real-world applications, researchers often work with compositional data which appears as proportions, amounts or rates. As a framework for dealing with the unique nature of compositional data, Dirichlet regression models have been introduced. In this article, we propose a novel model-based gradient boosting approach for Dirichlet regression models embedded in the framework of generalized additive models for location, scale and shape. This approach allows for data-driven variable selection in low- as well as high-dimensional data settings. Moreover, the implementation enables the direct calculation of marginal effects for different predictor variables. Thus, it provides an alternative estimation procedure besides the well-established approach based on the maximum likelihood principle. After conducting detailed simulation studies to evaluate the performance of the estimation procedure regarding prediction accuracy and variable selection in low- and high-dimensional settings, we present a real-world application concerning the changes in election results in the Great Recession utilizing a large-scale European dataset. Using our proposed approach, we investigate the effect of protests on voting proportions of distinct party families while identifying important socioeconomic variables and their effect on those voting proportions via variable selection.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 1","pages":"149 - 189"},"PeriodicalIF":1.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00526-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147342295","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

Bias-corrected estimation for (mathcal{G}^0_I) regression with applications 偏差校正估计(mathcal{G}^0_I)回归与应用程序

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-03-31 DOI: 10.1007/s10182-025-00525-6

M. F. S. S. Sousa, J. M. Vasconcelos, A. D. C. Nascimento

{"title":"Bias-corrected estimation for (mathcal{G}^0_I) regression with applications","authors":"M. F. S. S. Sousa, J. M. Vasconcelos, A. D. C. Nascimento","doi":"10.1007/s10182-025-00525-6","DOIUrl":"10.1007/s10182-025-00525-6","url":null,"abstract":"<div><p>Synthetic aperture radar (SAR) systems are highly efficient tools for addressing remote sensing challenges. They offer several advantages, such as operating independently of atmospheric conditions and producing high spatial resolution images. However, SAR images are often contaminated by a type of interference called speckle noise, which complicates their analysis and processing. Therefore, proposing statistical methods, such as regression models, that account for speckle behavior is an important step for users of SAR systems. In the work [<span>ISPRS J. Photogramm. Remote Sens., 213, 1–13, 2024</span>], the <span>({mathcal{G}^{0}_{I}})</span> regression model (short for <span>(mathcal{R} {mathcal{G}^{0}_{I}})</span>) was proposed as an interpretable tool to relate SAR intensity features to other physical properties. The authors employed maximum likelihood estimators (MLEs), known for their good asymptotic properties but prone to considerable bias in small and medium sample sizes. In this paper, we propose a matrix expression for the second-order bias of MLEs for <span>(mathcal{R} {mathcal{G}^{0}_{I}})</span> parameters, based on the Cox and Snell method. This proposal is justified by the necessity of using small and moderate windows when processing SAR images, such as for classification and filtering purposes. We compare bias-corrected MLEs with their counterparts using both Monte Carlo experiments and an application to SAR data from a Brazilian region. Numerical evidence demonstrates the effectiveness of our proposal.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 3","pages":"557 - 589"},"PeriodicalIF":1.4,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384891","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 two-part beta regression with mismeasured dependent variable for modeling quasi-formal employment in Europe 对欧洲准正规就业建模的两部分β回归与误测因变量

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-02-22 DOI: 10.1007/s10182-025-00524-7

Maria Felice Arezzo, Giuseppina Guagnano, Domenico Vitale

{"title":"A two-part beta regression with mismeasured dependent variable for modeling quasi-formal employment in Europe","authors":"Maria Felice Arezzo, Giuseppina Guagnano, Domenico Vitale","doi":"10.1007/s10182-025-00524-7","DOIUrl":"10.1007/s10182-025-00524-7","url":null,"abstract":"<div><p>Quasi-formal employment refers to a situation in which a formal employer and a formal employee agree to declare only part of the wage and the rest being paid in cash to avoid tax liabilities. This phenomenon is detrimental to government budgets and to worker protection. It is therefore crucial to understand its extent and its drivers. The Eurobarometer survey no. 498 provides the information to achieve these objectives. However, several issues in the data need to be addressed with credible solutions in order to rely upon the inferences drawn. These issues are primarily concerned with the reliability of the data, which is compromised due to the phenomenon of social desirability bias. This can be defined as the tendency of respondents to provide false information when answering sensitive questions relating to socially stigmatized behaviors, such as tax evasion. In this work, we present a unified framework for modeling such survey data that overcomes the problems raised by social desirability bias and accommodates the structure of the variables of interest. In particular, we propose a two-part beta regression model where the part one models the participation in quasi-formal employment, whereas the part two models the share of annual gross income earned under the table. We allow the dependent variables of both parts of the model to be mismeasured to handle social desirability bias and generalize the part two using a beta regression framework that is suitable for the limited dependent variable representing the incidence of wage paid in cash. The performance of the estimators is evaluated through a Monte Carlo simulation study and compared with those achieved through a standard procedure that ignores the issues arising from social desirability bias. An application to the Eurobarometer survey no. 498 on quasi-formal employment is provided.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 1","pages":"191 - 218"},"PeriodicalIF":1.4,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00524-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147341238","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

Robust corrected empirical likelihood for partially linear measurement error models 部分线性测量误差模型的鲁棒修正经验似然

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-01-24 DOI: 10.1007/s10182-024-00518-x

Huihui Sun, Qiang Liu, Yuying Jiang

引用次数: 0

On random coefficient INAR processes with long memory 长记忆随机系数流过程研究

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-01-24 DOI: 10.1007/s10182-025-00523-8

Jan Beran, Frieder Droullier

引用次数: 0

Fuzzy group fixed-effects estimation with spatial clustering 空间聚类模糊群固定效应估计

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-01-03 DOI: 10.1007/s10182-024-00522-1

Roy Cerqueti, Pierpaolo D’Urso, Raffaele Mattera

{"title":"Fuzzy group fixed-effects estimation with spatial clustering","authors":"Roy Cerqueti, Pierpaolo D’Urso, Raffaele Mattera","doi":"10.1007/s10182-024-00522-1","DOIUrl":"10.1007/s10182-024-00522-1","url":null,"abstract":"<div><p>The paper discusses the problem of estimating group heterogeneous fixed-effect panel data models under the assumption of fuzzy clustering, that is each unit belongs to all the clusters with a membership degree. To enhance spatial clustering, a spatio-temporal approach is considered. An iterative procedure, alternating panel data estimation and spatio-temporal clustering of the residuals, is proposed. The proposed method can be of relevance to researchers interested in using fuzzy group fixed-effect methods, but want to leverage spatial dimension for clustering units. Two empirical examples, the first on cigarette consumption in the US states and the second on non-life insurance demand in Italy, are presented to illustrate the performance of the proposed approach. The spatial fuzzy GFE model reveals important regional differences in both the US cigarette consumption and non-life insurance determinants in Italy. In the case of the US, we found a distinction in two main clusters, East and West. For the Italy provinces data, we find a distinction in North and South clusters. Regarding the regression results, for cigarette consumption data, different from the previous studies, we find that the smuggling effect is significant only in east regions, thus suggesting localised impacts of bootlegging. In the context of Italian non-life insurance demand, we find that while population density explains insurance consumption in northern provinces, the trust issues in the south explain the lower insurance demand.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 4","pages":"721 - 752"},"PeriodicalIF":1.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915720","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

Exploring the spatial clustering and spillover effects of COVID-19 vaccination uptake in Romania: an analysis at municipality level 探索罗马尼亚COVID-19疫苗接种的空间聚类和溢出效应：一项市政一级的分析

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-12-30 DOI: 10.1007/s10182-024-00520-3

Codruta Mare, Stefana Belbe, Norbert Petrovici

{"title":"Exploring the spatial clustering and spillover effects of COVID-19 vaccination uptake in Romania: an analysis at municipality level","authors":"Codruta Mare, Stefana Belbe, Norbert Petrovici","doi":"10.1007/s10182-024-00520-3","DOIUrl":"10.1007/s10182-024-00520-3","url":null,"abstract":"<div><p>This study investigates the spatial clustering and spillover effects of COVID-19 vaccine uptake in Romania, focusing on the municipality-level distribution of vaccine acceptance and hesitancy while considering the factors that influence it. The research uses the Spatial Durbin Error Model (SDEM) and identifies spatial clusterization, as well as significant contagion and diffusion processes in the vaccination behaviour conditioned by socioeconomic factors, labour market characteristics, social and religious attitudes, urban, and health indicators. We find evidence in favour of spatial spillover effects of the poverty rate, opposition to same-sex marriage, COVID-19 infection rate, peri-urban towns, and denser cities. Our findings contribute to the literature of the spatial distribution and determinants of vaccine uptake and carry practical implications for policy makers offering evidence-based insights that can inform targeted strategies and interventions to enhance vaccine acceptance and address hesitancy in specific locations.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 4","pages":"667 - 688"},"PeriodicalIF":1.4,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00520-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915718","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

Integrated modified harmonic mean method for spatial panel data models 空间面板数据模型的综合修正调和平均法

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-12-28 DOI: 10.1007/s10182-024-00521-2

Osman Doğan, Ye Yang, Süleyman Taşpınar

引用次数: 0