发布求助
文献互助 智能选刊 最新文献

Asta-Advances in Statistical Analysis最新文献

筛选
英文 中文
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
{"title":"Bayesian generalized additive model selection including a fast variational option","authors":"Virginia X. He,&nbsp;Matt P. Wand","doi":"10.1007/s10182-023-00490-y","DOIUrl":"10.1007/s10182-023-00490-y","url":null,"abstract":"<div><p>We use Bayesian model selection paradigms, such as group least absolute shrinkage and selection operator priors, to facilitate generalized additive model selection. Our approach allows for the effects of continuous predictors to be categorized as either zero, linear or non-linear. Employment of carefully tailored auxiliary variables results in Gibbsian Markov chain Monte Carlo schemes for practical implementation of the approach. In addition, mean field variational algorithms with closed form updates are obtained. Whilst not as accurate, this fast variational option enhances scalability to very large data sets. A package in the <span>R</span> language aids use in practice.\u0000</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138690278","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 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":"https://doi.org/10.1007/s10182-023-00491-x","url":null,"abstract":"<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>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"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
{"title":"Zero-modified count time series modeling with an application to influenza cases","authors":"Marinho G. Andrade,&nbsp;Katiane S. Conceição,&nbsp;Nalini Ravishanker","doi":"10.1007/s10182-023-00488-6","DOIUrl":"10.1007/s10182-023-00488-6","url":null,"abstract":"<div><p>The past few decades have seen considerable interest in modeling time series of counts, with applications in many domains. Classical and Bayesian modeling have primarily focused on conditional Poisson sampling distributions at each time. There is very little research on modeling time series involving Zero-Modified (i.e., Zero Deflated or Inflated) distributions. This paper aims to fill this gap and develop models for count time series involving Zero-Modified distributions, which belong to the Power Series family and are suitable for time series exhibiting both zero-inflation and zero-deflation. A full Bayesian approach via the Hamiltonian Monte Carlo (HMC) technique enables accurate modeling and inference. The paper illustrates our approach using time series on the number of deaths from the influenza virus in the city of São Paulo, Brazil.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506562","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
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":"https://doi.org/10.1007/s10182-023-00487-7","url":null,"abstract":"<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>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","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
{"title":"Mixture of experts distributional regression: implementation using robust estimation with adaptive first-order methods","authors":"David Rügamer,&nbsp;Florian Pfisterer,&nbsp;Bernd Bischl,&nbsp;Bettina Grün","doi":"10.1007/s10182-023-00486-8","DOIUrl":"10.1007/s10182-023-00486-8","url":null,"abstract":"<div><p>In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimization techniques with adaptive learning rate schedulers. We take advantage of the flexibility and scalability of neural network software and implement the proposed framework in <i>mixdistreg</i>, an <span>R</span> software package that allows for the definition of mixtures of many different families, estimation in high-dimensional and large sample size settings and robust optimization based on TensorFlow. Numerical experiments with simulated and real-world data applications show that optimization is as reliable as estimation via classical approaches in many different settings and that results may be obtained for complicated scenarios where classical approaches consistently fail.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-023-00486-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138506564","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 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,&nbsp;Simon Wiegrebe,&nbsp;Paul W. Thurner,&nbsp;Christian Heumann,&nbsp;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":null,"pages":null},"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,&nbsp;Antonino Abbruzzo,&nbsp;Mauro Ferrante,&nbsp;Giada Adelfio,&nbsp;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":null,"pages":null},"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
Conditional sum of squares estimation of k-factor GARMA models k 因子 GARMA 模型的条件平方和估计
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2023-10-31 DOI: 10.1007/s10182-023-00482-y
Paul M. Beaumont, Aaron D. Smallwood
{"title":"Conditional sum of squares estimation of k-factor GARMA models","authors":"Paul M. Beaumont,&nbsp;Aaron D. Smallwood","doi":"10.1007/s10182-023-00482-y","DOIUrl":"10.1007/s10182-023-00482-y","url":null,"abstract":"<div><p>We analyze issues related to estimation and inference for the constrained sum of squares estimator (CSS) of the <i>k</i>-factor Gegenbauer autoregressive moving average (GARMA) model. We present theoretical results for the estimator and show that the parameters that determine the cycle lengths are asymptotically independent, converging at rate <i>T</i>, the sample size, for finite cycles. The remaining parameters lack independence and converge at the standard rate. Analogous with existing literature, some challenges exist for testing the hypothesis of non-cyclical long memory, since the associated parameter lies on the boundary of the parameter space. We present simulation results to explore small sample properties of the estimator, which support most distributional results, while also highlighting areas that merit additional exploration. We demonstrate the applicability of the theory and estimator with an application to IBM trading volume.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135870088","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
Measures of interrater agreement for quantitative data 定量数据间一致性的度量
4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2023-10-10 DOI: 10.1007/s10182-023-00483-x
Daniela Marella, Giuseppe Bove
{"title":"Measures of interrater agreement for quantitative data","authors":"Daniela Marella, Giuseppe Bove","doi":"10.1007/s10182-023-00483-x","DOIUrl":"https://doi.org/10.1007/s10182-023-00483-x","url":null,"abstract":"Abstract In this paper measures of interrater absolute agreement for quantitative measurements based on the standard deviation are proposed. Such indices allow (i) to overcome the limits affecting the intraclass correlation index; (ii) to measure the interrater agreement on single targets. Estimators of the proposed measures are introduced and their sampling properties are investigated for normal and non-normal data. Simulated data are employed to demonstrate the accuracy and practical utility of the new indices for assessing agreement. Finally, an application to assess the consistency of measurements performed by radiologists evaluating tumor size of lung cancer is presented.","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136296350","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
Calibrated imputation for multivariate categorical data 多变量分类数据的校准估算
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2023-10-05 DOI: 10.1007/s10182-023-00481-z
Ton de Waal, Jacco Daalmans
{"title":"Calibrated imputation for multivariate categorical data","authors":"Ton de Waal,&nbsp;Jacco Daalmans","doi":"10.1007/s10182-023-00481-z","DOIUrl":"10.1007/s10182-023-00481-z","url":null,"abstract":"<div><p>Non-response is a major problem for anyone collecting and processing data. A commonly used technique to deal with missing data is imputation, where missing values are estimated and filled in into the dataset. Imputation can become challenging if the variable to be imputed has to comply with a known total. Even more challenging is the case where several variables in the same dataset need to be imputed and, in addition to known totals, logical restrictions between variables have to be satisfied. In our paper, we develop an approach for a broad class of imputation methods for multivariate categorical data such that previously published totals are preserved while logical restrictions on the data are satisfied. The developed approach can be used in combination with any imputation model that estimates imputation probabilities, i.e. the probability that imputation of a certain category for a variable in a certain unit leads to the correct value for this variable and unit.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-023-00481-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135482185","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：481959085
Book学术官方微信