Asta-Advances in Statistical Analysis最新文献_第6页

Sieve bootstrapping the memory parameter in long-range dependent stationary functional time series 筛网自举的记忆参数在长期依赖平稳函数时间序列

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-10-01 DOI: 10.1007/s10182-022-00463-7

Han Lin Shang

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引用次数: 1

Distributional properties of continuous time processes: from CIR to bates 连续时间过程的分布性质:从CIR到bates

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-08-25 DOI: 10.1007/s10182-022-00459-3

Ostap Okhrin, Michael Rockinger, Manuel Schmid

引用次数: 1

Hierarchical disjoint principal component analysis 层次不相交主成分分析

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-08-24 DOI: 10.1007/s10182-022-00458-4

Carlo Cavicchia, Maurizio Vichi, Giorgia Zaccaria

{"title":"Hierarchical disjoint principal component analysis","authors":"Carlo Cavicchia, Maurizio Vichi, Giorgia Zaccaria","doi":"10.1007/s10182-022-00458-4","DOIUrl":"10.1007/s10182-022-00458-4","url":null,"abstract":"<div><p>Dimension reduction, by means of Principal Component Analysis (PCA), is often employed to obtain a reduced set of components preserving the largest possible part of the total variance of the observed variables. Several methodologies have been proposed either to improve the interpretation of PCA results (e.g., by means of orthogonal, oblique rotations, shrinkage methods), or to model oblique components or factors with a hierarchical structure, such as in Bi-factor and High-Order Factor analyses. In this paper, we propose a new methodology, called Hierarchical Disjoint Principal Component Analysis (HierDPCA), that aims at building a hierarchy of disjoint principal components of maximum variance associated with disjoint groups of observed variables, from <i>Q</i> up to a unique, general one. HierDPCA also allows choosing the type of the relationship among disjoint principal components of two sequential levels, from the lowest upwards, by testing the component correlation per level and changing from a reflective to a formative approach when this correlation turns out to be not statistically significant. The methodology is formulated in a semi-parametric least-squares framework and a coordinate descent algorithm is proposed to estimate the model parameters. A simulation study and two real applications are illustrated to highlight the empirical properties of the proposed methodology.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"107 3","pages":"537 - 574"},"PeriodicalIF":1.4,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-022-00458-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42994839","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: 2

Multiple imputation of ordinal missing not at random data 非随机数据序号缺失的多重插补

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-08-22 DOI: 10.1007/s10182-022-00461-9

Angelina Hammon

{"title":"Multiple imputation of ordinal missing not at random data","authors":"Angelina Hammon","doi":"10.1007/s10182-022-00461-9","DOIUrl":"10.1007/s10182-022-00461-9","url":null,"abstract":"<div><p>We introduce a selection model-based imputation approach to be used within the Fully Conditional Specification (FCS) framework for the Multiple Imputation (MI) of incomplete ordinal variables that are supposed to be Missing Not at Random (MNAR). Thereby, we generalise previous work on this topic which involved binary single-level and multilevel data to ordinal variables. We apply an ordered probit model with sample selection as base of our imputation algorithm. The applied model involves two equations that are modelled jointly where the first one describes the missing-data mechanism and the second one specifies the variable to be imputed. In addition, we develop a version for hierarchical data by incorporating random intercept terms in both equations. To fit this multilevel imputation model we use quadrature techniques. Two simulation studies validate the overall good performance of our single-level and multilevel imputation methods. In addition, we show its applicability to empirical data by applying it to a common research topic in educational science using data of the National Educational Panel Study (NEPS) and conducting a short sensitivity analysis. Our approach is designed to be used within the <span>R</span> software package <span>mice</span> which makes it easy to access and apply.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"107 4","pages":"671 - 692"},"PeriodicalIF":1.4,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-022-00461-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46920381","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 the presence of treatment effect under selection on observables 在可观察器上选择下治疗效果的存在性测试

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-08-09 DOI: 10.1007/s10182-022-00454-8

Pier Luigi Conti, Livia De Giovanni

引用次数: 0

Authors’ response: on the role of data, statistics and decisions in a pandemic 作者的回应:关于数据、统计和决策在大流行中的作用

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-07-30 DOI: 10.1007/s10182-022-00460-w

Beate Jahn, Sarah Friedrich, Joachim Behnke, Joachim Engel, Ursula Garczarek, Ralf Münnich, Markus Pauly, Adalbert Wilhelm, Olaf Wolkenhauer, Markus Zwick, Uwe Siebert, Tim Friede

引用次数: 1

A new price index for multi-period and multilateral comparisons 用于多时期和多边比较的新价格指数

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-07-12 DOI: 10.1007/s10182-022-00457-5

Mario Faliva, Consuelo Rubina Nava, Maria Grazia Zoia

引用次数: 0

Editorial special issue: Statistics in sports 社论特刊:体育统计

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-07-11 DOI: 10.1007/s10182-022-00453-9

Andreas Groll, Dominik Liebl

引用次数: 3

Integration of model-based recursive partitioning with bias reduction estimation: a case study assessing the impact of Oliver’s four factors on the probability of winning a basketball game 基于模型的递归划分与偏差减少估计的集成:一个评估奥利弗的四个因素对篮球比赛获胜概率影响的案例研究

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-07-04 DOI: 10.1007/s10182-022-00456-6

Manlio Migliorati, Marica Manisera, Paola Zuccolotto

{"title":"Integration of model-based recursive partitioning with bias reduction estimation: a case study assessing the impact of Oliver’s four factors on the probability of winning a basketball game","authors":"Manlio Migliorati, Marica Manisera, Paola Zuccolotto","doi":"10.1007/s10182-022-00456-6","DOIUrl":"10.1007/s10182-022-00456-6","url":null,"abstract":"<div><p>In this contribution, we investigate the importance of Oliver’s Four Factors, proposed in the literature to identify a basketball team’s strengths and weaknesses in terms of shooting, turnovers, rebounding and free throws, as success drivers of a basketball game. In order to investigate the role of each factor in the success of a team in a match, we applied the MOdel-Based recursive partitioning (MOB) algorithm to real data concerning 19,138 matches of 16 National Basketball Association (NBA) regular seasons (from 2004–2005 to 2019–2020). MOB, instead of fitting one global Generalized Linear Model (GLM) to all observations, partitions the observations according to selected partitioning variables and estimates several ad hoc local GLMs for subgroups of observations. The manuscript’s aim is twofold: (1) in order to deal with (quasi) separation problems leading to convergence problems in the numerical solution of Maximum Likelihood (ML) estimation in MOB, we propose a methodological extension of GLM-based recursive partitioning from standard ML estimation to bias-reduced (BR) estimation; and (2) we apply the BR-based GLM trees to basketball analytics. The results show models very easy to interpret that can provide useful support to coaching staff’s decisions.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"107 1-2","pages":"271 - 293"},"PeriodicalIF":1.4,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-022-00456-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9114892","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 to: Local spatial log-Gaussian Cox processes for seismic data 校正:地震数据的局部空间对数-高斯Cox过程

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-06-30 DOI: 10.1007/s10182-022-00455-7

Nicoletta D’Angelo, Marianna Siino, Antonino D’Alessandro, Giada Adelfio

引用次数: 0