Asta-Advances in Statistical Analysis最新文献_第3页

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

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

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

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

引用次数: 0

Sequential estimation of multivariate factor stochastic volatility models 多因素随机波动模型的序贯估计

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-08-01 DOI: 10.1007/s10182-025-00536-3

Christian Mücher, Giorgio Calzolari, Roxana Halbleib

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引用次数: 0

Clustering for ranking multivariate data by Linear Ordered Partitions 基于线性有序分区的多变量数据排序聚类

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-07-21 DOI: 10.1007/s10182-025-00534-5

Mariaelena Bottazzi Schenone, Maurizio Vichi

{"title":"Clustering for ranking multivariate data by Linear Ordered Partitions","authors":"Mariaelena Bottazzi Schenone, Maurizio Vichi","doi":"10.1007/s10182-025-00534-5","DOIUrl":"10.1007/s10182-025-00534-5","url":null,"abstract":"<div><p>This paper explores the use of clustering to rank multivariate observations by linking ranking to clustering through the Linear Ordered Partition (LOP) concept. A LOP allows optimal clustering into ordered “<i>equivalence classes</i>”. In fact, unlike simple units’ ordering, cluster ranking identifies classes where units are “<i>incomparable</i>”. The aim is to partition units into clusters with statistically distinct centroids, leading to an optimally ranked total order of clusters, where units within each one are considered “<i>ties</i>”. The proposed model finds the best least-squares (LS) LOP, alongside with a univariate transformation of the observed variables. This is because it identifies the LS LOP by orthogonally projecting multivariate units onto a line, thus creating a composite indicator that summarizes the observed variables. Model’s theoretical properties are discussed, and a large simulation study demonstrates its performance across different scenarios. Three real data applications highlight the method’s potential across different fields.\u0000</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 1","pages":"117 - 148"},"PeriodicalIF":1.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00534-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147341237","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

Correction: Fuzzy group fixed-effects estimation with spatial clustering 修正：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 : 2025-07-07 DOI: 10.1007/s10182-025-00532-7

Roy Cerqueti, Pierpaolo D’Urso, Raffaele Mattera

引用次数: 0

Functional data analysis for wearable sensor data: a systematic review 可穿戴传感器数据的功能数据分析：系统综述

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-06-18 DOI: 10.1007/s10182-025-00531-8

Nihan Acar-Denizli, Pedro Delicado

引用次数: 0

Evaluation of available techniques and their combinations to address selection bias in nonprobability surveys 评估可用的技术及其组合，以解决非概率调查中的选择偏差

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-06-13 DOI: 10.1007/s10182-025-00530-9

Jorge Luis Rueda-Sánchez, Ramón Ferri-García, María del Mar Rueda, Beatriz Cobo

{"title":"Evaluation of available techniques and their combinations to address selection bias in nonprobability surveys","authors":"Jorge Luis Rueda-Sánchez, Ramón Ferri-García, María del Mar Rueda, Beatriz Cobo","doi":"10.1007/s10182-025-00530-9","DOIUrl":"10.1007/s10182-025-00530-9","url":null,"abstract":"<div><p>New survey methodologies that often produce nonprobability samples have recently become very important. However, estimates from nonprobability samples can be subject to selection bias, which is primarily caused by the lack of coverage and the respondent’s ability to decide whether or not to participate in the survey. In such cases, inclusion probabilities can be zero or unknown. When this happens, the estimators normally used in sample surveys are useless, and we must employ methods to reduce this bias. There is a wide variety of techniques to achieve this which depend on the auxiliary information available, but no study has determined which is better among all. In this paper, we briefly explain most of these methods and conduct an extended study to compare their performances. We will study superpopulation models, which require knowledge of the auxiliary variables of all individuals in the population, linear calibration, which requires the population totals of the covariates, and several techniques that use a reference probability sample, such as propensity score adjustment, propensity-adjusted probability prediction, Kernel Weighting, Statistical Matching and, Doubly Robust estimators. In addition, we compare their performance using linear regression or XGBoost as a predictive model, and the design weights in estimating inclusion probabilities or not, and with or without prior variables selection. The study was performed using five different datasets to determine which technique provides accurate and reliable estimates from nonprobability samples. </p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"110 2","pages":"407 - 457"},"PeriodicalIF":1.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-025-00530-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147985215","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

Anisotropic local covariance matrices for spatial blind source separation 空间盲源分离的各向异性局部协方差矩阵

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2025-06-10 DOI: 10.1007/s10182-025-00529-2

Christoph Muehlmann, Claudia Cappello, Sandra De Iaco, Klaus Nordhausen

引用次数: 0