Statistics and Computing最新文献_第5页

A review on the Adaptive-Ridge Algorithm with several extensions 自适应脊算法及若干扩展功能综述

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-25 DOI: 10.1007/s11222-024-10440-6

Rémy Abergel, Olivier Bouaziz, Grégory Nuel

{"title":"A review on the Adaptive-Ridge Algorithm with several extensions","authors":"Rémy Abergel, Olivier Bouaziz, Grégory Nuel","doi":"10.1007/s11222-024-10440-6","DOIUrl":"https://doi.org/10.1007/s11222-024-10440-6","url":null,"abstract":"The Adaptive Ridge Algorithm is an iterative algorithm designed for variable selection. It is also known under the denomination of Iteratively Reweighted Least-Squares Algorithm in the communities of Compressed Sensing and Sparse Signals Recovery. Besides, it can also be interpreted as an optimization algorithm dedicated to the minimization of possibly nonconvex (ell ^q) penalized energies (with (0<q<2)). In the literature, this algorithm can be derived using various mathematical approaches, namely Half Quadratic Minimization, Majorization-Minimization, Alternating Minimization or Local Approximations. In this work, we will show how the Adaptive Ridge Algorithm can be simply derived and analyzed from a single equation, corresponding to a variational reformulation of the (ell ^q) penalty. We will describe in detail how the Adaptive Ridge Algorithm can be numerically implemented and we will perform a thorough experimental study of its parameters. We will also show how the variational formulation of the (ell ^q) penalty combined with modern duality principles can be used to design an interesting variant of the Adaptive Ridge Algorithm dedicated to the minimization of quadratic functions over (nonconvex) (ell ^q) balls.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"26 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Enhancing cure rate analysis through integration of machine learning models: a comparative study 通过整合机器学习模型加强治愈率分析：一项比较研究

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-25 DOI: 10.1007/s11222-024-10456-y

Wisdom Aselisewine, Suvra Pal

{"title":"Enhancing cure rate analysis through integration of machine learning models: a comparative study","authors":"Wisdom Aselisewine, Suvra Pal","doi":"10.1007/s11222-024-10456-y","DOIUrl":"https://doi.org/10.1007/s11222-024-10456-y","url":null,"abstract":"Cure rate models have been thoroughly investigated across various domains, encompassing medicine, reliability, and finance. The merging of machine learning (ML) with cure models is emerging as a promising strategy to improve predictive accuracy and gain profound insights into the underlying mechanisms influencing the probability of cure. The current body of literature has explored the benefits of incorporating a single ML algorithm with cure models. However, there is a notable absence of a comprehensive study that compares the performances of various ML algorithms in this context. This paper seeks to address and bridge this gap. Specifically, we focus on the well-known mixture cure model and examine the incorporation of five distinct ML algorithms: extreme gradient boosting, neural networks, support vector machines, random forests, and decision trees. To bolster the robustness of our comparison, we also include cure models with logistic and spline-based regression. For parameter estimation, we formulate an expectation maximization algorithm. A comprehensive simulation study is conducted across diverse scenarios to compare various models based on the accuracy and precision of estimates for different quantities of interest, along with the predictive accuracy of cure. The results derived from both the simulation study, as well as the analysis of real cutaneous melanoma data, indicate that the incorporation of ML models into cure model provides a beneficial contribution to the ongoing endeavors aimed at improving the accuracy of cure rate estimation.\u0000","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"111 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141517772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Gaussian processes for Bayesian inverse problems associated with linear partial differential equations 与线性偏微分方程相关的贝叶斯逆问题的高斯过程

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-24 DOI: 10.1007/s11222-024-10452-2

Tianming Bai, Aretha L. Teckentrup, Konstantinos C. Zygalakis

引用次数: 0

Bounded-memory adjusted scores estimation in generalized linear models with large data sets 具有大型数据集的广义线性模型中的限界内存调整分数估计

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-21 DOI: 10.1007/s11222-024-10447-z

Patrick Zietkiewicz, Ioannis Kosmidis

{"title":"Bounded-memory adjusted scores estimation in generalized linear models with large data sets","authors":"Patrick Zietkiewicz, Ioannis Kosmidis","doi":"10.1007/s11222-024-10447-z","DOIUrl":"https://doi.org/10.1007/s11222-024-10447-z","url":null,"abstract":"The widespread use of maximum Jeffreys’-prior penalized likelihood in binomial-response generalized linear models, and in logistic regression, in particular, are supported by the results of Kosmidis and Firth (Biometrika 108:71–82, 2021. https://doi.org/10.1093/biomet/asaa052), who show that the resulting estimates are always finite-valued, even in cases where the maximum likelihood estimates are not, which is a practical issue regardless of the size of the data set. In logistic regression, the implied adjusted score equations are formally bias-reducing in asymptotic frameworks with a fixed number of parameters and appear to deliver a substantial reduction in the persistent bias of the maximum likelihood estimator in high-dimensional settings where the number of parameters grows asymptotically as a proportion of the number of observations. In this work, we develop and present two new variants of iteratively reweighted least squares for estimating generalized linear models with adjusted score equations for mean bias reduction and maximization of the likelihood penalized by a positive power of the Jeffreys-prior penalty, which eliminate the requirement of storing O(n) quantities in memory, and can operate with data sets that exceed computer memory or even hard drive capacity. We achieve that through incremental QR decompositions, which enable IWLS iterations to have access only to data chunks of predetermined size. Both procedures can also be readily adapted to fit generalized linear models when distinct parts of the data is stored across different sites and, due to privacy concerns, cannot be fully transferred across sites. We assess the procedures through a real-data application with millions of observations.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"16 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

An efficient workflow for modelling high-dimensional spatial extremes 建立高维空间极值模型的高效工作流程

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-19 DOI: 10.1007/s11222-024-10448-y

Silius M. Vandeskog, Sara Martino, Raphaël Huser

引用次数: 0

Model-based clustering with missing not at random data 基于模型的非随机数据缺失聚类

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-18 DOI: 10.1007/s11222-024-10444-2

Aude Sportisse, Matthieu Marbac, Fabien Laporte, Gilles Celeux, Claire Boyer, Julie Josse, Christophe Biernacki

{"title":"Model-based clustering with missing not at random data","authors":"Aude Sportisse, Matthieu Marbac, Fabien Laporte, Gilles Celeux, Claire Boyer, Julie Josse, Christophe Biernacki","doi":"10.1007/s11222-024-10444-2","DOIUrl":"https://doi.org/10.1007/s11222-024-10444-2","url":null,"abstract":"Model-based unsupervised learning, as any learning task, stalls as soon as missing data occurs. This is even more true when the missing data are informative, or said missing not at random (MNAR). In this paper, we propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data. To do so, we introduce a mixture model for different types of data (continuous, count, categorical and mixed) to jointly model the data distribution and the MNAR mechanism, remaining vigilant to the relative degrees of freedom of each. Several MNAR models are discussed, for which the cause of the missingness can depend on both the values of the missing variable themselves and on the class membership. However, we focus on a specific MNAR model, called MNARz, for which the missingness only depends on the class membership. We first underline its ease of estimation, by showing that the statistical inference can be carried out on the data matrix concatenated with the missing mask considering finally a standard MAR mechanism. Consequently, we propose to perform clustering using the Expectation Maximization algorithm, specially developed for this simplified reinterpretation. Finally, we assess the numerical performances of the proposed methods on synthetic data and on the real medical registry TraumaBase as well.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"46 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

An efficient method to simulate diffusion bridges 模拟扩散桥的高效方法

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-12 DOI: 10.1007/s11222-024-10439-z

H. Chau, J. L. Kirkby, D. H. Nguyen, D. Nguyen, N. Nguyen, T. Nguyen

引用次数: 0

Efficient estimation and correction of selection-induced bias with order statistics 利用阶次统计有效估计和修正选择诱导偏差

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-12 DOI: 10.1007/s11222-024-10442-4

Yann McLatchie, Aki Vehtari

{"title":"Efficient estimation and correction of selection-induced bias with order statistics","authors":"Yann McLatchie, Aki Vehtari","doi":"10.1007/s11222-024-10442-4","DOIUrl":"https://doi.org/10.1007/s11222-024-10442-4","url":null,"abstract":"Model selection aims to identify a sufficiently well performing model that is possibly simpler than the most complex model among a pool of candidates. However, the decision-making process itself can inadvertently introduce non-negligible bias when the cross-validation estimates of predictive performance are marred by excessive noise. In finite data regimes, cross-validated estimates can encourage the statistician to select one model over another when it is not actually better for future data. While this bias remains negligible in the case of few models, when the pool of candidates grows, and model selection decisions are compounded (as in step-wise selection), the expected magnitude of selection-induced bias is likely to grow too. This paper introduces an efficient approach to estimate and correct selection-induced bias based on order statistics. Numerical experiments demonstrate the reliability of our approach in estimating both selection-induced bias and over-fitting along compounded model selection decisions, with specific application to forward search. This work represents a light-weight alternative to more computationally expensive approaches to correcting selection-induced bias, such as nested cross-validation and the bootstrap. Our approach rests on several theoretic assumptions, and we provide a diagnostic to help understand when these may not be valid and when to fall back on safer, albeit more computationally expensive approaches. The accompanying code facilitates its practical implementation and fosters further exploration in this area.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"24 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Jittering and clustering: strategies for the construction of robust designs 抖动和聚类：构建稳健设计的策略

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-04 DOI: 10.1007/s11222-024-10436-2

Douglas P. Wiens

引用次数: 0

Testing the goodness-of-fit of the stable distributions with applications to German stock index data and Bitcoin cryptocurrency data 应用德国股票指数数据和比特币加密货币数据检验稳定分布的拟合优度

IF 2.2 2区数学

Statistics and Computing Pub Date : 2024-06-03 DOI: 10.1007/s11222-024-10441-5

Ruhul Ali Khan, Ayan Pal, Debasis Kundu

{"title":"Testing the goodness-of-fit of the stable distributions with applications to German stock index data and Bitcoin cryptocurrency data","authors":"Ruhul Ali Khan, Ayan Pal, Debasis Kundu","doi":"10.1007/s11222-024-10441-5","DOIUrl":"https://doi.org/10.1007/s11222-024-10441-5","url":null,"abstract":"Outlier-prone data sets are of immense interest in diverse areas including economics, finance, statistical physics, signal processing, telecommunications and so on. Stable laws (also known as (alpha )- stable laws) are often found to be useful in modeling outlier-prone data containing important information and exhibiting heavy tailed phenomenon. In this article, an asymptotic distribution of a unbiased and consistent estimator of the stability index (alpha ) is proposed based on jackknife empirical likelihood (JEL) and adjusted JEL method. Next, using the sum-preserving property of stable random variables and exploiting U-statistic theory, we have developed a goodness-of-fit test procedure for (alpha )-stable distributions where the stability index (alpha ) is specified. Extensive simulation studies are performed in order to assess the finite sample performance of the proposed test. Finally, two appealing real life data examples related to the daily closing price of German Stock Index and Bitcoin cryptocurrency are analysed in detail for illustration purposes.","PeriodicalId":22058,"journal":{"name":"Statistics and Computing","volume":"75 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141259103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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