Austrian Journal of Statistics最新文献_第3页

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-19 DOI: 10.17713/ajs.v52i4.1503

D. Nguyen

{"title":"Accelerated Iterated Filtering","authors":"D. Nguyen","doi":"10.17713/ajs.v52i4.1503","DOIUrl":"https://doi.org/10.17713/ajs.v52i4.1503","url":null,"abstract":"Simulation-based inferences have attracted much attention in recent years, as the direct computation of the likelihood function in many real-world problems is difficult or even impossible. Iterated filtering (Ionides, Bretó, and King 2006; Ionides, Bhadra, Atchadé,and King 2011) enables maximization of likelihood function via model perturbations and approximation of the gradient of loglikelihood through sequential Monte Carlo filtering. By an application of Stein’s identity, Doucet, Jacob, and Rubenthaler (2013) developed asecond-order approximation of the gradient of log-likelihood using sequential Monte Carlo smoothing. Based on these gradient approximations, we develop a new algorithm for maximizing the likelihood using the Nesterov accelerated gradient. We adopt the accelerated inexact gradient algorithm (Ghadimi and Lan 2016) to iterated filtering framework, relaxing the unbiased gradient approximation condition. We devise a perturbation policy for iterated filtering, allowing the new algorithm to converge at an optimal rate for both concave and non-concave log-likelihood functions. It is comparable to the recently developed Bayes map iterated filtering approach and outperforms the original iterated filtering approach.","PeriodicalId":51761,"journal":{"name":"Austrian Journal of Statistics","volume":"1 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85926700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Generalization of LASSO Modeling via Bayesian Interpretation 基于贝叶斯解释的LASSO模型推广

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-19 DOI: 10.17713/ajs.v52i4.1455

Gayan Warahena-Liyanage, F. Famoye, Carl Lee

{"title":"A Generalization of LASSO Modeling via Bayesian Interpretation","authors":"Gayan Warahena-Liyanage, F. Famoye, Carl Lee","doi":"10.17713/ajs.v52i4.1455","DOIUrl":"https://doi.org/10.17713/ajs.v52i4.1455","url":null,"abstract":"The aim of this paper is to introduce a generalized LASSO regression model that is derived using a generalized Laplace (GL) distribution. Five different GL distributions are obtained through the T -R{Y } framework with quantile functions of standard uniform, Weibull, log-logistic, logistic, and extreme value distributions. The properties, including quantile function, mode, and Shannon entropy of these GL distributions are derived. A particular case of GL distributions called the beta-Laplace distribution is explored. Some additional components to the constraint in the ordinary LASSO regression model are obtained through the Bayesian interpretation of LASSO with beta-Laplace priors. The geometric interpretations of these additional components are presented. The effects of the parameters from beta-Laplace distribution in the generalized LASSO regression model are also discussed. Two real data sets are analyzed to illustrate the flexibility and usefulness of the generalized LASSO regression model in the process of variable selection with better prediction performance. Consequently, this research study demonstrates that more flexible statistical distributions can be used to enhance LASSO in terms of flexibility in variable selection and shrinkage with better prediction.","PeriodicalId":51761,"journal":{"name":"Austrian Journal of Statistics","volume":"7 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73071828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monitoring Convergence in the European Union with the convergEU Package for R 用convergEU软件包监测欧洲联盟的趋同

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-19 DOI: 10.17713/ajs.v52i4.1468

F. Stefanini, N. D. Nikiforova, Eleonora Peruffo, Martina Bisello, Chiara Litardi, Massimiliano Mascherini

{"title":"Monitoring Convergence in the European Union with the convergEU Package for R","authors":"F. Stefanini, N. D. Nikiforova, Eleonora Peruffo, Martina Bisello, Chiara Litardi, Massimiliano Mascherini","doi":"10.17713/ajs.v52i4.1468","DOIUrl":"https://doi.org/10.17713/ajs.v52i4.1468","url":null,"abstract":"Upward convergence, that is an improvement of Members States’ economic and social indicators’ performance, is a core policy target of the European Union. This concept isembodied in the European Pillar of social rights (EPSR) proclaimed by EU leaders in 2017. In 2018 Eurofound has developed a methodology to measure convergence, which has beenimplemented in the convergEU R package described in this work (rel. 0.5.0). The package extends the original STATA toolbox developed by Eurofound beyond the calculations ofthe four main measures of convergence as it provides functions to download, filter, impute, smooth indicators. Country and indicator fiches are automatically prepared and compiledin HTML format. Graphical output includes qualitative patterns of change along time to emphasize the key behaviour of an indicator with respect to the average of an aggregationof Member States. Besides EU Member States, the analysis of other collections of regions is supported for general indicators if context information is provided. A user-friendly,no coding required, shiny-based web application provides policy makers with a tool to produce convergence reports on selected indicators and countries.","PeriodicalId":51761,"journal":{"name":"Austrian Journal of Statistics","volume":"42 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85120063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

amIcompositional: Simple Tests for Compositional Behaviour of High Throughput Data with Common Transformations amIcompositional:具有公共转换的高吞吐量数据的组合行为的简单测试

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-19 DOI: 10.17713/ajs.v52i4.1617

G. Gloor

{"title":"amIcompositional: Simple Tests for Compositional Behaviour of High Throughput Data with Common Transformations","authors":"G. Gloor","doi":"10.17713/ajs.v52i4.1617","DOIUrl":"https://doi.org/10.17713/ajs.v52i4.1617","url":null,"abstract":"\u0000\u0000\u0000Compositional approaches are beginning to permeate high throughput biomedical sciences in the areas of microbiome, genomics, transcriptomics and proteomics. Yet non-compositional approaches are still commonly observed. Non-compositional approaches are particularly problematic in network analysis based on correlation, ordination and exploratory data analysis based on distance, and differential abundance analysis based on normalization. Here we describe the aIc R package, a simple tool that answers the fundamental question: does the dataset or normalization exhibit compositional artefacts that will skew interpretations when analyzing high throughput biomedical data? The aIc R package includes options for several of the most widely used normalizations and filtering methods. The R package includes tests for subcompositional dominance and coherence along with perturbation and scale invariance. Exploratory analysis is facilitated by an R Shiny app that makes the process simple for those not wishing to use an R console. This simple approach will allow research groups to acknowledge and account for potential artefacts in data analysis resulting in more robust and reliable inferences.\u0000\u0000\u0000","PeriodicalId":51761,"journal":{"name":"Austrian Journal of Statistics","volume":"40 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76733072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multivariate Asymmetric Distributions of Copula Related Random Variables Copula相关随机变量的多元不对称分布

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-19 DOI: 10.17713/ajs.v52i4.1446

A. Sheikhi, Freshteh Arad, R. Mesiar

引用次数: 0

The Waves and Cycles of COVID-19 Pandemic: A Phase Synchronization Approach COVID-19大流行的波动和周期:一个阶段同步方法

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-18 DOI: 10.17713/ajs.v52i3.1450

Carmen Borrego-Salcido, R. Juárez-Del-Toro, Alejandro Steven Fonseca-Zendejas

{"title":"The Waves and Cycles of COVID-19 Pandemic: A Phase Synchronization Approach","authors":"Carmen Borrego-Salcido, R. Juárez-Del-Toro, Alejandro Steven Fonseca-Zendejas","doi":"10.17713/ajs.v52i3.1450","DOIUrl":"https://doi.org/10.17713/ajs.v52i3.1450","url":null,"abstract":"This study pretends to contribute to a better understanding of the COVID-19 dynamics through the non-parametric technique of phase synchronization by comparing the fifteen most affected countries by the number of positive cases plus China, where the firstoutbreak took place in December 2019. It was possible to state the number of cycles and waves for each one of the studied countries and to determine periods of synchronization between them. The results also showed the average duration of the cycles and some coincidences regarding Nason (2020); Bontempi (2021); Coccia (2021); Rusiñol, Zammit, Itarte, Forés, Martı́nez-Puchol, Girones, Borrego, Corominas, and Bofill-Mas (2021). This study is limited by the reliability of the number of positive cases reported by national governments and health authorities because of an insufficient number of tests and a great number of asymptomatic persons but presents a legit alternative to predict the evolution of the pandemic in a country due to the forward looking behavior of another one, therefore studies like this could be useful to implement contention measures and to prepare the health systems in advance.","PeriodicalId":51761,"journal":{"name":"Austrian Journal of Statistics","volume":"117 1","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79466902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Easily Changeable Kurtosis Distribution 容易变化的峰度分布

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-18 DOI: 10.17713/ajs.v52i3.1434

P. Sulewski

引用次数: 0

Power Modified Lindley Distribution: Properties, Classical and Bayesian Estimation and Regression Model with Applications 功率修正林德利分布:性质、经典和贝叶斯估计及回归模型及其应用

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-18 DOI: 10.17713/ajs.v52i3.1386

O. Kharazmi, D. Kumar, S. Dey, St. Anthony’s College

引用次数: 0

Taxicab Correspondence Analysis and Taxicab Logratio Analysis: A Comparison on Contingency Tables and Compositional Data 出租车对应分析与出租车对数分析:列联表与成分数据的比较

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-18 DOI: 10.17713/ajs.v52i3.1302

V. Choulakian, J. Allard, S. Mahdi

引用次数: 2

Using the Discrete Lindley Distribution to Deal with Over-dispersion in Count Data 用离散林德利分布处理计数数据中的过色散

IF 0.6

Austrian Journal of Statistics Pub Date : 2023-07-18 DOI: 10.17713/ajs.v52i3.1465

M. Nguyen, M. Nguyen, N. Le

引用次数: 0