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A note on dynamic spatiotemporal ARCH models: small- and large-sample results 动态时空ARCH模型注释：小样本和大样本结果
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2025-12-19 DOI: 10.1007/s10182-025-00552-3
Philipp Otto, Osman Doğan, Süleyman Taşpınar
{"title":"A note on dynamic spatiotemporal ARCH models: small- and large-sample results","authors":"Philipp Otto,&nbsp;Osman Doğan,&nbsp;Süleyman Taşpınar","doi":"10.1007/s10182-025-00552-3","DOIUrl":"10.1007/s10182-025-00552-3","url":null,"abstract":"<div><p>This short paper explores the estimation of a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) model. The log-volatility term in this model can depend on (i) the spatial lag of the log-squared outcome variable, (ii) the time-lag of the log-squared outcome variable, (iii) the spatiotemporal lag of the log-squared outcome variable, (iv) exogenous variables, and (v) the unobserved heterogeneity across regions and time, i.e., the regional and time fixed effects. We examine the small- and large-sample properties of two quasi-maximum likelihood estimators and a generalised method of moments estimator for this model. We first summarize the theoretical properties of these estimators and then compare their finite sample properties through Monte Carlo simulations.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 4","pages":"811 - 828"},"PeriodicalIF":1.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00552-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915717","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
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
{"title":"Advances in spatial econometrics and geostatistics: methods, theory, and applications","authors":"Philipp Otto,&nbsp;Janine Illian","doi":"10.1007/s10182-025-00551-4","DOIUrl":"10.1007/s10182-025-00551-4","url":null,"abstract":"","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 4","pages":"633 - 636"},"PeriodicalIF":1.4,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915692","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
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
{"title":"Fuzzy C-modes clustering with spatial regularization and noise cluster","authors":"Pierpaolo D’Urso,&nbsp;Livia De Giovanni,&nbsp;Lorenzo Federico,&nbsp;Vincenzina Vitale","doi":"10.1007/s10182-025-00547-0","DOIUrl":"10.1007/s10182-025-00547-0","url":null,"abstract":"<div><p>Clustering categorical data presents unique challenges that traditional techniques do not adequately address. This paper proposes an extension of the fuzzy C-modes algorithm. By incorporating a noise cluster and integrating spatial contiguity relationships among units, the algorithm’s robustness is significantly enhanced. Performance evaluations using synthetic data demonstrate the efficacy of the proposed algorithm in handling both global and local outliers. Furthermore, the paper discusses the application of the algorithm to real-world data on sustainable urban mobility in the Italian provincial capitals during 2021, highlighting its practical relevance and potential impact in real-world scenarios.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 4","pages":"771 - 809"},"PeriodicalIF":1.4,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00547-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915722","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
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
{"title":"Margin-closed regime-switching multivariate time series models","authors":"Lin Zhang,&nbsp;Harry Joe,&nbsp;Natalia Nolde","doi":"10.1007/s10182-025-00546-1","DOIUrl":"10.1007/s10182-025-00546-1","url":null,"abstract":"<div><p>Regime-switching multivariate time series models tend to involve a large number of parameters, which complicates their estimation and inference. We propose a new class of parsimonious regime-switching multivariate time series models such that there is a stationary Markov process for each regime with non-Gaussian marginal distributions. A simple serial dependence construction is suggested for transitions between regimes. The stochastic process in each regime has the property that all lower-dimensional sub-processes follow a regime-switching process sharing the same latent regime sequence and having the same Markov order as the multivariate process. This results in a model that is closed under margins, a property that allows inference on the latent regimes to be based on chosen lower-dimensional subprocesses and enables the use of a more computationally convenient multi-stage estimation procedure. We conduct a simulation study to evaluate the finite sample performance of the proposed estimation procedure. Then, we apply the model to a macroeconomic dataset to infer the latent business cycle and compare it to a relevant benchmark.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 1","pages":"1 - 40"},"PeriodicalIF":1.4,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147339444","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,&nbsp;Marina Fragalá,&nbsp;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":"<p><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>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514071/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Testing for causal effect for binary data when propensity scores are estimated through Bayesian Networks 通过贝叶斯网络估计倾向分数时，二元数据的因果效应检验
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2025-08-28 DOI: 10.1007/s10182-025-00535-4
Paola Vicard, Paola Maria Vittoria Rancoita, Federica Cugnata, Alberto Briganti, Fulvia Mecatti, Clelia Di Serio, Pier Luigi Conti
{"title":"Testing for causal effect for binary data when propensity scores are estimated through Bayesian Networks","authors":"Paola Vicard,&nbsp;Paola Maria Vittoria Rancoita,&nbsp;Federica Cugnata,&nbsp;Alberto Briganti,&nbsp;Fulvia Mecatti,&nbsp;Clelia Di Serio,&nbsp;Pier Luigi Conti","doi":"10.1007/s10182-025-00535-4","DOIUrl":"10.1007/s10182-025-00535-4","url":null,"abstract":"<div><p>This paper proposes a new statistical approach for assessing treatment effect using Bayesian Networks (BNs). The goal is to draw causal inferences from observational data with a binary outcome and discrete covariates. The BNs are here used to estimate the propensity score, which enables flexible modeling and ensures maximum likelihood properties. When the propensity score is estimated by BNs, two point estimators are considered—Hájek and Horvitz–Thompson—based on inverse probability weighting, and their main distributional properties are derived for constructing confidence intervals and testing hypotheses about the absence of the treatment effect. Empirical evidence is presented to show the good behavior of the proposed methodology through a simulation study mimicking the characteristics of a real dataset of prostate cancer patients from Milan San Raffaele Hospital.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 3","pages":"483 - 508"},"PeriodicalIF":1.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00535-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384846","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
Basketball players performance measurement with algorithmic survival data analysis 基于算法的篮球运动员生存数据分析
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2025-08-28 DOI: 10.1007/s10182-025-00533-6
Ambra Macis
{"title":"Basketball players performance measurement with algorithmic survival data analysis","authors":"Ambra Macis","doi":"10.1007/s10182-025-00533-6","DOIUrl":"10.1007/s10182-025-00533-6","url":null,"abstract":"<div><p>Performance measurement is of paramount importance in the context of sports analytics. A great variety of data analysis methods has been exploited to this aim. All these proposals almost never include resorting to survival analysis techniques, although time-to-event data are suitable for addressing this issue. This work aims to identify the main achievements of a National Basketball Association player that affect the time it takes for him to exceed a given threshold of points. In order to identify nonlinear effects and possible interactions among the predictors, the analysis is carried out with machine learning methods, specifically survival trees and random survival forests.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 3","pages":"529 - 555"},"PeriodicalIF":1.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00533-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384844","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
Forecasting time series by long-memory models for count data with an application to price jumps 用长记忆模型预测计数数据的时间序列，并应用于价格跳跃
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2025-08-13 DOI: 10.1007/s10182-025-00538-1
Luisa Bisaglia, Massimiliano Caporin, Matteo Grigoletto
{"title":"Forecasting time series by long-memory models for count data with an application to price jumps","authors":"Luisa Bisaglia,&nbsp;Massimiliano Caporin,&nbsp;Matteo Grigoletto","doi":"10.1007/s10182-025-00538-1","DOIUrl":"10.1007/s10182-025-00538-1","url":null,"abstract":"<div><p>We discuss the estimation and forecast of long-memory models for count data time series. We first demonstrate by Monte Carlo simulations that the Whittle estimator is the most appropriate for recovering the memory degree of a count data time series. In the following, we introduce the possibility of forecasting count data by exploiting the infinite autoregressive representation of the model. We complete our analysis with an empirical example in which we verify the predictability of the price jump numbers.\u0000</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 3","pages":"417 - 441"},"PeriodicalIF":1.4,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00538-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384845","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 self-exciting spatio-temporal model with a smooth space-time-varying productivity parameter 具有光滑时空变化生产率参数的自激时空模型
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2025-08-01 DOI: 10.1007/s10182-025-00537-2
Álvaro Briz-Redón, Jorge Mateu
{"title":"A self-exciting spatio-temporal model with a smooth space-time-varying productivity parameter","authors":"Álvaro Briz-Redón,&nbsp;Jorge Mateu","doi":"10.1007/s10182-025-00537-2","DOIUrl":"10.1007/s10182-025-00537-2","url":null,"abstract":"<div><p>The self-exciting spatio-temporal point process model is a fundamental tool for studying recurrent events in fields such as economics, criminology, and seismology. Existing models often assume that the productivity parameter, which measures the rate of triggered events, is constant in space and time. This assumption is often unrealistic, as it may not capture the complexity of some real-world phenomena. In this paper, we propose a new self-exciting model that relaxes this assumption by allowing the productivity parameter to vary smoothly in both space and time. Through simulation experiments, we demonstrate that our model can effectively recover the underlying pattern of excitation. Furthermore, we apply the proposed framework to a crime dataset, showing its ability to identify spatial and temporal heterogeneity in event dynamics. This approach offers a more realistic method for modeling spatio-temporal patterns, with significant potential for the development of surveillance and prevention tools in a range of applications.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 1","pages":"65 - 89"},"PeriodicalIF":1.4,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00537-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147335965","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
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