Asta-Advances in Statistical Analysis最新文献_第8页

A quantile regression perspective on external preference mapping 外部偏好映射的分位数回归分析

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-04-12 DOI: 10.1007/s10182-022-00440-0

Cristina Davino, Tormod Næs, Rosaria Romano, Domenico Vistocco

{"title":"A quantile regression perspective on external preference mapping","authors":"Cristina Davino, Tormod Næs, Rosaria Romano, Domenico Vistocco","doi":"10.1007/s10182-022-00440-0","DOIUrl":"10.1007/s10182-022-00440-0","url":null,"abstract":"<div><p>External preference mapping is widely used in marketing and R&D divisions to understand the consumer behaviour. The most common preference map is obtained through a two-step procedure that combines principal component analysis and least squares regression. The standard approach exploits classical regression and therefore focuses on the conditional mean. This paper proposes the use of quantile regression to enrich the preference map looking at the whole distribution of the consumer preference. The enriched maps highlight possible different consumer behaviour with respect to the less or most preferred products. This is pursued by exploring the variability of liking along the principal components as well as focusing on the direction of preference. The use of different aesthetics (colours, shapes, size, arrows) equips standard preference map with additional information and does not force the user to change the standard tool she/he is used to. The proposed methodology is shown in action on a case study pertaining yogurt preferences.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"106 4","pages":"545 - 571"},"PeriodicalIF":1.4,"publicationDate":"2022-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-022-00440-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44379862","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

Group sparse recovery via group square-root elastic net and the iterative multivariate thresholding-based algorithm 基于群平方根弹性网和迭代多元阈值算法的群稀疏恢复

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-04-08 DOI: 10.1007/s10182-022-00443-x

Wanling Xie, Hu Yang

{"title":"Group sparse recovery via group square-root elastic net and the iterative multivariate thresholding-based algorithm","authors":"Wanling Xie, Hu Yang","doi":"10.1007/s10182-022-00443-x","DOIUrl":"10.1007/s10182-022-00443-x","url":null,"abstract":"<div><p>In this work, we propose a novel group selection method called Group Square-Root Elastic Net. It is based on square-root regularization with a group elastic net penalty, i.e., a <span>(ell _{2,1}+ell _2)</span> penalty. As a type of square-root-based procedure, one distinct feature is that the estimator is independent of the unknown noise level <span>(sigma )</span>, which is non-trivial to estimate under the high-dimensional setting, especially when <span>(pgg n)</span>. In many applications, the estimator is expected to be sparse, not in an irregular way, but rather in a structured manner. It makes the proposed method very attractive to tackle both high-dimensionality and structured sparsity. We study the correct subset recovery under a Group Elastic Net Irrepresentable Condition. Both the slow rate bounds and fast rate bounds are established, the latter under the Restricted Eigenvalue assumption and Gaussian noise assumption. To implement, a fast algorithm based on the scaled multivariate thresholding-based iterative selection idea is introduced with proved convergence. A comparative study examines the superiority of our approach against alternatives.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"107 3","pages":"469 - 507"},"PeriodicalIF":1.4,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49272710","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

On the role of data, statistics and decisions in a pandemic 关于数据、统计和决策在大流行中的作用

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-04-07 DOI: 10.1007/s10182-022-00439-7

Beate Jahn, Sarah Friedrich, Joachim Behnke, Joachim Engel, Ursula Garczarek, Ralf Münnich, Markus Pauly, Adalbert Wilhelm, Olaf Wolkenhauer, Markus Zwick, Uwe Siebert, Tim Friede

{"title":"On the role of data, statistics and decisions in a pandemic","authors":"Beate Jahn, Sarah Friedrich, Joachim Behnke, Joachim Engel, Ursula Garczarek, Ralf Münnich, Markus Pauly, Adalbert Wilhelm, Olaf Wolkenhauer, Markus Zwick, Uwe Siebert, Tim Friede","doi":"10.1007/s10182-022-00439-7","DOIUrl":"10.1007/s10182-022-00439-7","url":null,"abstract":"<div><p>A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"106 3","pages":"349 - 382"},"PeriodicalIF":1.4,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-022-00439-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50012409","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: 12

Imputation-based empirical likelihood inferences for partially nonlinear quantile regression models with missing responses 缺失响应部分非线性分位数回归模型的基于假设的经验似然推断

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-04-06 DOI: 10.1007/s10182-022-00441-z

Xiaoshuang Zhou, Peixin Zhao, Yujie Gai

引用次数: 1

Correction to: Assessment of agricultural sustainability in European Union countries: a group-based multivariate trajectory approach 修正:欧盟国家农业可持续性评估:基于群体的多元轨迹方法

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-03-17 DOI: 10.1007/s10182-022-00438-8

Alessandro Magrini

引用次数: 2

On the Gaussian representation of the Riesz probability distribution on symmetric matrices 对称矩阵上Riesz概率分布的高斯表示

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-03-06 DOI: 10.1007/s10182-022-00436-w

Abdelhamid Hassairi, Fatma Ktari, Raoudha Zine

引用次数: 0

Assessment of agricultural sustainability in European Union countries: a group-based multivariate trajectory approach 欧盟国家农业可持续性评估:基于群体的多元轨迹方法

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-03-05 DOI: 10.1007/s10182-022-00437-9

Alessandro Magrini

{"title":"Assessment of agricultural sustainability in European Union countries: a group-based multivariate trajectory approach","authors":"Alessandro Magrini","doi":"10.1007/s10182-022-00437-9","DOIUrl":"10.1007/s10182-022-00437-9","url":null,"abstract":"<div><p>Sustainability of agriculture is difficult to measure and assess because it is a multidimensional concept that involves economic, social and environmental aspects and is subjected to temporal evolution and geographical differences. Existing studies assessing agricultural sustainability in the European Union (EU) are affected by several shortcomings that limit their relevance for policy makers. Specifically, most of them focus on farm level or cover a small set of countries, and the few exceptions covering a broad set of countries consider only a subset of the sustainable dimensions or rely on cross-sectional data. In this paper, we consider yearly data on 12 indicators (5 for the economic, 3 for the social and 4 for the environmental dimension) measured on 26 EU countries in the period 2004–2018 (15 years), and apply group-based multivariate trajectory modeling to identify groups of countries with common trends of sustainable objectives. An expectation-maximization algorithm is proposed to perform maximum likelihood estimation from incomplete data without relying on an explicit imputation procedure. Our results highlight three groups of countries with distinguished strong and weak sustainable objectives. Strong objectives common to all the three groups include improvement of productivity, increase of personal income in rural areas, reduction of poverty in rural areas, increase of production of renewable energy, rise of organic farming and reduction of nitrogen balance. Instead, enhancement of manager turnover and reduction of greenhouse gas emissions are weak objectives common to all the three groups of countries. Our findings represent a valuable resource to formulate new schemes for the attribution of subsidies within the Common Agricultural Policy (CAP).</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"106 4","pages":"673 - 703"},"PeriodicalIF":1.4,"publicationDate":"2022-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-022-00437-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50009738","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: 13

Correction to: Assessment of agricultural sustainability in European Union countries: a group-based multivariate trajectory approach 修正:欧盟国家农业可持续性评估:基于群体的多元轨迹方法

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-03-05 DOI: 10.1007/s10182-022-00437-9

Alessandro Magrini

引用次数: 13

Action rate models for predicting actions in soccer 预测足球动作的动作率模型

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-03-02 DOI: 10.1007/s10182-022-00435-x

Uwe Dick, Ulf Brefeld

引用次数: 1

Scoring predictions at extreme quantiles 以极端分位数对预测进行评分

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-02-14 DOI: 10.1007/s10182-021-00421-9

Axel Gandy, Kaushik Jana, Almut E. D. Veraart

{"title":"Scoring predictions at extreme quantiles","authors":"Axel Gandy, Kaushik Jana, Almut E. D. Veraart","doi":"10.1007/s10182-021-00421-9","DOIUrl":"10.1007/s10182-021-00421-9","url":null,"abstract":"<div><p>Prediction of quantiles at extreme tails is of interest in numerous applications. Extreme value modelling provides various competing predictors for this point prediction problem. A common method of assessment of a set of competing predictors is to evaluate their predictive performance in a given situation. However, due to the extreme nature of this inference problem, it can be possible that the predicted quantiles are not seen in the historical records, particularly when the sample size is small. This situation poses a problem to the validation of the prediction with its realization. In this article, we propose two non-parametric scoring approaches to assess extreme quantile prediction mechanisms. The proposed assessment methods are based on predicting a sequence of equally extreme quantiles on different parts of the data. We then use the quantile scoring function to evaluate the competing predictors. The performance of the scoring methods is compared with the conventional scoring method and the superiority of the former methods are demonstrated in a simulation study. The methods are then applied to analyze cyber Netflow data from Los Alamos National Laboratory and daily precipitation data at a station in California available from Global Historical Climatology Network.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"106 4","pages":"527 - 544"},"PeriodicalIF":1.4,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46149253","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: 3