Asta-Advances in Statistical Analysis最新文献_第2页

Editorial special issue: Bridging the gap between AI and Statistics 编辑特刊：缩小人工智能与统计学之间的差距

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-06-21 DOI: 10.1007/s10182-024-00503-4

Benjamin Säfken, David Rügamer

引用次数: 0

Markov-switching decision trees 马尔可夫转换决策树

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-05-29 DOI: 10.1007/s10182-024-00501-6

Timo Adam, Marius Ötting, Rouven Michels

引用次数: 0

Markov switching stereotype logit models for longitudinal ordinal data affected by unobserved heterogeneity in responding behavior 受反应行为中未观察到的异质性影响的纵向序数数据的马尔可夫转换定型 Logit 模型

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-05-15 DOI: 10.1007/s10182-024-00500-7

Roberto Colombi, Sabrina Giordano

{"title":"Markov switching stereotype logit models for longitudinal ordinal data affected by unobserved heterogeneity in responding behavior","authors":"Roberto Colombi, Sabrina Giordano","doi":"10.1007/s10182-024-00500-7","DOIUrl":"10.1007/s10182-024-00500-7","url":null,"abstract":"<div><p>When asked to assess their opinion about attitudes or perceptions on Likert-scale, respondents often endorse the midpoint or extremes of the scale and agree or disagree regardless of the content. These responding behaviors are known in the psychometric literature as middle, extremes, aquiescence and disacquiescence response styles that generally introduce bias in the results. One of the key motivations behind our approach is to account for these attitudes and how they evolve over time. The novelty of our proposal, in the context of longitudinal ordered categorical data, is in considering simultaneously the temporal dynamics of the responses (observable ordinal variables) and unobservable answering behaviors, possibly influenced by response styles, through a Markov switching logit model with two latent components. One component accommodates serial dependence and respondent’s unobserved heterogeneity, the other component determines the responding attitude (due to response styles or not). The dependence of the responses on covariates is modelled by a stereotype logit model with parameters varying according to the two latent components. The stereotype logit model is adopted because it is a flexible extension of the proportional odds logit model that retains the advantage of using a single parameter to describe a regressor effect. In the paper, a new interpretation of the parameters of the stereotype model is given by defining the allocation sets as intervals of values of the linear predictor that identify the most probable response. Unobserved heterogeneity, serial dependence and tendency to response style are modelled through our approach on longitudinal data, collected by the Bank of Italy.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 1","pages":"117 - 147"},"PeriodicalIF":1.4,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00500-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059332","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

Deducing neighborhoods of classes from a fitted model 从拟合模型中推断类别邻域

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-05-08 DOI: 10.1007/s10182-024-00502-5

Alexander Gerharz, Andreas Groll, Gunther Schauberger

引用次数: 0

Testing distributional assumptions in CUB models for the analysis of rating data 测试用于分析评级数据的 CUB 模型中的分布假设

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-04-13 DOI: 10.1007/s10182-024-00498-y

Francesca Di Iorio, Riccardo Lucchetti, Rosaria Simone

引用次数: 0

Testing for periodicity at an unknown frequency under cyclic long memory, with applications to respiratory muscle training 在循环长记忆下测试未知频率的周期性，并应用于呼吸肌训练

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-04-12 DOI: 10.1007/s10182-024-00499-x

Jan Beran, Jeremy Näscher, Fabian Pietsch, Stephan Walterspacher

引用次数: 0

Bernstein flows for flexible posteriors in variational Bayes 变异贝叶斯中灵活后验的伯恩斯坦流

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-04-03 DOI: 10.1007/s10182-024-00497-z

Oliver Dürr, Stefan Hörtling, Danil Dold, Ivonne Kovylov, Beate Sick

{"title":"Bernstein flows for flexible posteriors in variational Bayes","authors":"Oliver Dürr, Stefan Hörtling, Danil Dold, Ivonne Kovylov, Beate Sick","doi":"10.1007/s10182-024-00497-z","DOIUrl":"10.1007/s10182-024-00497-z","url":null,"abstract":"<div><p>Black-box variational inference (BBVI) is a technique to approximate the posterior of Bayesian models by optimization. Similar to MCMC, the user only needs to specify the model; then, the inference procedure is done automatically. In contrast to MCMC, BBVI scales to many observations, is faster for some applications, and can take advantage of highly optimized deep learning frameworks since it can be formulated as a minimization task. In the case of complex posteriors, however, other state-of-the-art BBVI approaches often yield unsatisfactory posterior approximations. This paper presents Bernstein flow variational inference (BF-VI), a robust and easy-to-use method flexible enough to approximate complex multivariate posteriors. BF-VI combines ideas from normalizing flows and Bernstein polynomial-based transformation models. In benchmark experiments, we compare BF-VI solutions with exact posteriors, MCMC solutions, and state-of-the-art BBVI methods, including normalizing flow-based BBVI. We show for low-dimensional models that BF-VI accurately approximates the true posterior; in higher-dimensional models, BF-VI compares favorably against other BBVI methods. Further, using BF-VI, we develop a Bayesian model for the semi-structured melanoma challenge data, combining a CNN model part for image data with an interpretable model part for tabular data, and demonstrate, for the first time, the use of BBVI in semi-structured models.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 2","pages":"375 - 394"},"PeriodicalIF":1.4,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00497-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140582930","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

Variational inference: uncertainty quantification in additive models 变量推理：加法模型中的不确定性量化

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-04-03 DOI: 10.1007/s10182-024-00492-4

Jens Lichter, Paul F V Wiemann, Thomas Kneib

{"title":"Variational inference: uncertainty quantification in additive models","authors":"Jens Lichter, Paul F V Wiemann, Thomas Kneib","doi":"10.1007/s10182-024-00492-4","DOIUrl":"10.1007/s10182-024-00492-4","url":null,"abstract":"<div><p>Markov chain Monte Carlo (MCMC)-based simulation approaches are by far the most common method in Bayesian inference to access the posterior distribution. Recently, motivated by successes in machine learning, variational inference (VI) has gained in interest in statistics since it promises a computationally efficient alternative to MCMC enabling approximate access to the posterior. Classical approaches such as mean-field VI (MFVI), however, are based on the strong mean-field assumption for the approximate posterior where parameters or parameter blocks are assumed to be mutually independent. As a consequence, parameter uncertainties are often underestimated and alternatives such as semi-implicit VI (SIVI) have been suggested to avoid the mean-field assumption and to improve uncertainty estimates. SIVI uses a hierarchical construction of the variational parameters to restore parameter dependencies and relies on a highly flexible implicit mixing distribution whose probability density function is not analytic but samples can be taken via a stochastic procedure. With this paper, we investigate how different forms of VI perform in semiparametric additive regression models as one of the most important fields of application of Bayesian inference in statistics. A particular focus is on the ability of the rivalling approaches to quantify uncertainty, especially with correlated covariates that are likely to aggravate the difficulties of simplifying VI assumptions. Moreover, we propose a method, where we combine both advantages of MFVI and SIVI and compare its performance. The different VI approaches are studied in comparison with MCMC in simulations and an application to tree height models of douglas fir based on a large-scale forestry data set.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 2","pages":"279 - 331"},"PeriodicalIF":1.4,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00492-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140582932","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

Ridge regularization for spatial autoregressive models with multicollinearity issues 具有多重共线性问题的空间自回归模型的岭正则化

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-04-01 DOI: 10.1007/s10182-024-00496-0

Cristina O. Chavez-Chong, Cécile Hardouin, Ana-Karina Fermin

引用次数: 0

Using sequential statistical tests for efficient hyperparameter tuning 利用序列统计检验实现高效超参数调整

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-03-14 DOI: 10.1007/s10182-024-00495-1

Philip Buczak, Andreas Groll, Markus Pauly, Jakob Rehof, Daniel Horn

{"title":"Using sequential statistical tests for efficient hyperparameter tuning","authors":"Philip Buczak, Andreas Groll, Markus Pauly, Jakob Rehof, Daniel Horn","doi":"10.1007/s10182-024-00495-1","DOIUrl":"10.1007/s10182-024-00495-1","url":null,"abstract":"<div><p>Hyperparameter tuning is one of the most time-consuming parts in machine learning. Despite the existence of modern optimization algorithms that minimize the number of evaluations needed, evaluations of a single setting may still be expensive. Usually a resampling technique is used, where the machine learning method has to be fitted a fixed number of <i>k</i> times on different training datasets. The respective mean performance of the <i>k</i> fits is then used as performance estimator. Many hyperparameter settings could be discarded after less than <i>k</i> resampling iterations if they are clearly inferior to high-performing settings. However, resampling is often performed until the very end, wasting a lot of computational effort. To this end, we propose the sequential random search (SQRS) which extends the regular random search algorithm by a sequential testing procedure aimed at detecting and eliminating inferior parameter configurations early. We compared our SQRS with regular random search using multiple publicly available regression and classification datasets. Our simulation study showed that the SQRS is able to find similarly well-performing parameter settings while requiring noticeably fewer evaluations. Our results underscore the potential for integrating sequential tests into hyperparameter tuning.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 2","pages":"441 - 460"},"PeriodicalIF":1.4,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00495-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140124518","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