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

Weighted likelihood methods for robust fitting of wrapped models for p-torus data 用加权似然法稳健拟合 p-torus 数据的包裹模型

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-03-11 DOI: 10.1007/s10182-024-00494-2

Claudio Agostinelli, Luca Greco, Giovanni Saraceno

引用次数: 0

Robust Bayesian small area estimation using the sub-Gaussian (alpha)-stable distribution for measurement error in covariates 使用亚高斯$$alpha$$-稳定分布对协变因素中的测量误差进行稳健的贝叶斯小面积估算

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-03-06 DOI: 10.1007/s10182-024-00493-3

Serena Arima, Shaho Zarei

{"title":"Robust Bayesian small area estimation using the sub-Gaussian (alpha)-stable distribution for measurement error in covariates","authors":"Serena Arima, Shaho Zarei","doi":"10.1007/s10182-024-00493-3","DOIUrl":"10.1007/s10182-024-00493-3","url":null,"abstract":"<div><p>In small area estimation, the sample size is so small that direct estimators have seldom enough adequate precision. Therefore, it is common to use auxiliary data via covariates and produce estimators that combine them with direct data. Nevertheless, it is not uncommon for covariates to be measured with error, leading to inconsistent estimators. Area-level models accounting for measurement error (ME) in covariates have been proposed, and they usually assume that the errors are an i.i.d. Gaussian model. However, there might be situations in which this assumption is violated especially when covariates present severe outlying values that cannot be cached by the Gaussian distribution. To overcome this problem, we propose to model the ME through sub-Gaussian <span>(alpha)</span>-stable (SG<span>(alpha)</span>S) distribution, a flexible distribution that accommodates different types of outlying observations and also Gaussian data as a special case when <span>(alpha =2)</span>. The SG<span>(alpha)</span>S distribution is a generalization of the Gaussian distribution that allows for skewness and heavy tails by adding an extra parameter, <span>(alpha in (0,2])</span>, to control tail behaviour. The model parameters are estimated in a fully Bayesian framework. The performance of the proposal is illustrated by applying to real data and some simulation studies.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 4","pages":"777 - 799"},"PeriodicalIF":1.4,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140043971","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

Post-processing for Bayesian analysis of reduced rank regression models with orthonormality restrictions 对具有正交限制的缩减秩回归模型进行贝叶斯分析的后处理

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-12-20 DOI: 10.1007/s10182-023-00489-5

Christian Aßmann, Jens Boysen-Hogrefe, Markus Pape

引用次数: 0

Bayesian generalized additive model selection including a fast variational option 贝叶斯广义加法模型选择，包括快速变异选项

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-12-15 DOI: 10.1007/s10182-023-00490-y

Virginia X. He, Matt P. Wand

引用次数: 0

A note on sufficient dimension reduction with post dimension reduction statistical inference 关于充分降维与降维后统计推断的说明

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-12-13 DOI: 10.1007/s10182-023-00491-x

Kyongwon Kim

{"title":"A note on sufficient dimension reduction with post dimension reduction statistical inference","authors":"Kyongwon Kim","doi":"10.1007/s10182-023-00491-x","DOIUrl":"10.1007/s10182-023-00491-x","url":null,"abstract":"<div><p>Sufficient dimension reduction is a widely used tool to extract core information hidden in high-dimensional data for classifying, clustering, and predicting response variables. Various dimension reduction methods and their applications have been introduced in the past decades. Data analysis using sufficient dimension reduction involves two steps: dimension reduction and model estimation. However, when we implement the two-step modeling process, we consider the estimated sufficient predictor as a true predictor variable and proceed to the model development step, which includes statistical inference such as estimating confidence intervals and performing hypothesis tests. However, the outcome obtained using this method is by no means complete because it contains errors only from the model estimation step. Therefore, post dimension reduction inference is an important topic because it is essential to consider errors from sufficient dimension reduction. In this paper, we review the fundamentals of sufficient dimension reduction methods. Then, we introduce an intuitive and heuristic approach for the recently developed post dimension reduction statistical inference.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 4","pages":"733 - 753"},"PeriodicalIF":1.4,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138581852","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

Zero-modified count time series modeling with an application to influenza cases 零修正计数时间序列模型及其在流感病例中的应用

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-11-27 DOI: 10.1007/s10182-023-00488-6

Marinho G. Andrade, Katiane S. Conceição, Nalini Ravishanker

引用次数: 0

Mixtures of generalized normal distributions and EGARCH models to analyse returns and volatility of ESG and traditional investments 混合广义正态分布和EGARCH模型来分析ESG和传统投资的回报和波动性

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-11-18 DOI: 10.1007/s10182-023-00487-7

Pierdomenico Duttilo, Stefano Antonio Gattone, Barbara Iannone

{"title":"Mixtures of generalized normal distributions and EGARCH models to analyse returns and volatility of ESG and traditional investments","authors":"Pierdomenico Duttilo, Stefano Antonio Gattone, Barbara Iannone","doi":"10.1007/s10182-023-00487-7","DOIUrl":"10.1007/s10182-023-00487-7","url":null,"abstract":"<div><p>Environmental, social and governance (ESG) criteria are increasingly integrated into investment process to contribute to overcoming global sustainability challenges. Focusing on the reaction to turmoil periods, this work analyses returns and volatility of several ESG indices and makes a comparison with their traditional counterparts from 2016 to 2022. These indices comprise the following markets: Global, the US, Europe and emerging markets. Firstly, the two-component mixture of generalized normal distribution was exploited to objectively detect financial market turmoil periods with the Naïve Bayes’ classifier. Secondly, the EGARCH-in-mean model with exogenous dummy variables was applied to capture the turmoil period impact. Results show that returns and volatility are both affected by turmoil periods. The return–risk performance differs by index type and market: the European ESG index is less volatile than its traditional market benchmark, while in the other markets, the estimated volatility is approximately the same. Moreover, ESG and non-ESG indices differ in terms of turmoil periods impact, risk premium and leverage effect.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 4","pages":"755 - 775"},"PeriodicalIF":1.4,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-023-00487-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506566","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

Mixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods 混合专家分布回归:采用自适应一阶方法的稳健估计实现

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-11-15 DOI: 10.1007/s10182-023-00486-8

David Rügamer, Florian Pfisterer, Bernd Bischl, Bettina Grün

引用次数: 0

A Bayesian approach to modeling topic-metadata relationships 贝叶斯方法为主题-元数据关系建模

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-11-03 DOI: 10.1007/s10182-023-00485-9

Patrick Schulze, Simon Wiegrebe, Paul W. Thurner, Christian Heumann, Matthias Aßenmacher

{"title":"A Bayesian approach to modeling topic-metadata relationships","authors":"Patrick Schulze, Simon Wiegrebe, Paul W. Thurner, Christian Heumann, Matthias Aßenmacher","doi":"10.1007/s10182-023-00485-9","DOIUrl":"10.1007/s10182-023-00485-9","url":null,"abstract":"<div><p>The objective of advanced topic modeling is not only to explore latent topical structures, but also to estimate relationships between the discovered topics and theoretically relevant metadata. Methods used to estimate such relationships must take into account that the topical structure is not directly observed, but instead being estimated itself in an unsupervised fashion, usually by common topic models. A frequently used procedure to achieve this is the <i>method of composition</i>, a Monte Carlo sampling technique performing multiple repeated linear regressions of sampled topic proportions on metadata covariates. In this paper, we propose two modifications of this approach: First, we substantially refine the existing implementation of the method of composition from the <span>R</span> package <span>stm</span> by replacing linear regression with the more appropriate Beta regression. Second, we provide a fundamental enhancement of the entire estimation framework by substituting the current blending of frequentist and Bayesian methods with a fully Bayesian approach. This allows for a more appropriate quantification of uncertainty. We illustrate our improved methodology by investigating relationships between Twitter posts by German parliamentarians and different metadata covariates related to their electoral districts, using the structural topic model to estimate topic proportions.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 2","pages":"333 - 349"},"PeriodicalIF":1.4,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-023-00485-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135820119","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

GPS data on tourists: a spatial analysis on road networks 游客 GPS 数据：道路网络的空间分析

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-11-03 DOI: 10.1007/s10182-023-00484-w

Nicoletta D’Angelo, Antonino Abbruzzo, Mauro Ferrante, Giada Adelfio, Marcello Chiodi

{"title":"GPS data on tourists: a spatial analysis on road networks","authors":"Nicoletta D’Angelo, Antonino Abbruzzo, Mauro Ferrante, Giada Adelfio, Marcello Chiodi","doi":"10.1007/s10182-023-00484-w","DOIUrl":"10.1007/s10182-023-00484-w","url":null,"abstract":"<div><p>This paper proposes a spatial point process model on a linear network to analyse cruise passengers’ stop activities. It identifies and models tourists’ stop intensity at the destination as a function of their main determinants. For this purpose, we consider data collected on cruise passengers through the integration of traditional questionnaire-based survey methods and GPS tracking data in two cities, namely Palermo (Italy) and Dubrovnik (Croatia). Firstly, the density-based spatial clustering of applications with noise algorithm is applied to identify stop locations from GPS tracking data. The influence of individual-related variables and itinerary-related characteristics is considered within a framework of a Gibbs point process model. The proposed model describes spatial stop intensity at the destination, accounting for the geometry of the underlying road network, individual-related variables, contextual-level information, and the spatial interaction amongst stop points. The analysis succeeds in quantifying the influence of both individual-related variables and trip-related characteristics on stop intensity. An interaction parameter allows for measuring the degree of dependence amongst cruise passengers in stop location decisions.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 3","pages":"477 - 499"},"PeriodicalIF":1.4,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-023-00484-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819226","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