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

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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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

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

{"title":"Robust Bayesian small area estimation using the sub-Gaussian $$alpha$$ -stable distribution for measurement error in covariates","authors":"","doi":"10.1007/s10182-024-00493-3","DOIUrl":"https://doi.org/10.1007/s10182-024-00493-3","url":null,"abstract":"<h3>Abstract</h3> <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> <span>(alpha)</span> </span>-stable (SG<span> <span>(alpha)</span> </span>S) distribution, a flexible distribution that accommodates different types of outlying observations and also Gaussian data as a special case when <span> <span>(alpha =2)</span> </span>. The SG<span> <span>(alpha)</span> </span>S distribution is a generalization of the Gaussian distribution that allows for skewness and heavy tails by adding an extra parameter, <span> <span>(alpha in (0,2])</span> </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>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"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