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

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

Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion 非等分散计数数据的灵活模型:处理欠分散的参数模型的比较性能

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-02-03 DOI: 10.1007/s10182-021-00432-6

Douglas Toledo, Cristiane Akemi Umetsu, Antonio Fernando Monteiro Camargo, Idemauro Antonio Rodrigues de Lara

{"title":"Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion","authors":"Douglas Toledo, Cristiane Akemi Umetsu, Antonio Fernando Monteiro Camargo, Idemauro Antonio Rodrigues de Lara","doi":"10.1007/s10182-021-00432-6","DOIUrl":"10.1007/s10182-021-00432-6","url":null,"abstract":"<div><p>Count data as response variables are commonly modeled using Poisson regression models, which require equidispersion, i.e., equal mean and variance. However, this relationship does not always occur, and the variance may be higher or lower than the mean, phenomena are known as overdispersion and underdispersion, respectively. Non-equidispersion, when disregarded, can lead to a number of misinterpretations and inadequate predictions. Here, we compare the use of the COM-Poisson, double Poisson, Gamma-count, and restricted generalized Poisson models as a more flexible class for count problems associated with over- and underdispersion, since they have an additional parameter that allows more flexible analysis. The proposed method is useful in different applications, but here we provide an example using an underdispersed dataset concerning ecological invasion. For validation of the models, we use half-normal plots. The COM-Poisson, double Poisson, and Gamma-count performed best and properly modeled the underdispersion. The use of correct statistical models is recommended to handle this data property using objective criteria to ensure accurate statistical inferences.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-021-00432-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41916220","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: 3

Ranked sparsity: a cogent regularization framework for selecting and estimating feature interactions and polynomials 排序稀疏性：用于选择和估计特征交互和多项式的令人信服的正则化框架

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-01-25 DOI: 10.1007/s10182-021-00431-7

Ryan A. Peterson, Joseph E. Cavanaugh

{"title":"Ranked sparsity: a cogent regularization framework for selecting and estimating feature interactions and polynomials","authors":"Ryan A. Peterson, Joseph E. Cavanaugh","doi":"10.1007/s10182-021-00431-7","DOIUrl":"10.1007/s10182-021-00431-7","url":null,"abstract":"<div><p>We explore and illustrate the concept of ranked sparsity, a phenomenon that often occurs naturally in modeling applications when an expected disparity exists in the quality of information between different feature sets. Its presence can cause traditional and modern model selection methods to fail because such procedures commonly presume that each potential parameter is equally worthy of entering into the final model—we call this presumption “covariate equipoise.” However, this presumption does not always hold, especially in the presence of derived variables. For instance, when all possible interactions are considered as candidate predictors, the premise of covariate equipoise will often produce over-specified and opaque models. The sheer number of additional candidate variables grossly inflates the number of false discoveries in the interactions, resulting in unnecessarily complex and difficult-to-interpret models with many (truly spurious) interactions. We suggest a modeling strategy that requires a stronger level of evidence in order to allow certain variables (e.g., interactions) to be selected in the final model. This ranked sparsity paradigm can be implemented with the sparsity-ranked lasso (SRL). We compare the performance of SRL relative to competing methods in a series of simulation studies, showing that the SRL is a very attractive method because it is fast and accurate and produces more transparent models (with fewer false interactions). We illustrate its utility in an application to predict the survival of lung cancer patients using a set of gene expression measurements and clinical covariates, searching in particular for gene–environment interactions.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-021-00431-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49651637","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: 1

Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections 区域现在和延迟报告的数据预测:监测COVID-19感染

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-01-18 DOI: 10.1007/s10182-021-00433-5

Giacomo De Nicola, Marc Schneble, Göran Kauermann, Ursula Berger

{"title":"Regional now- and forecasting for data reported with delay: toward surveillance of COVID-19 infections","authors":"Giacomo De Nicola, Marc Schneble, Göran Kauermann, Ursula Berger","doi":"10.1007/s10182-021-00433-5","DOIUrl":"10.1007/s10182-021-00433-5","url":null,"abstract":"<div><p>Governments around the world continue to act to contain and mitigate the spread of COVID-19. The rapidly evolving situation compels officials and executives to continuously adapt policies and social distancing measures depending on the current state of the spread of the disease. In this context, it is crucial for policymakers to have a firm grasp on what the current state of the pandemic is, and to envision how the number of infections is going to evolve over the next days. However, as in many other situations involving compulsory registration of sensitive data, cases are reported with delay to a central register, with this delay deferring an up-to-date view of the state of things. We provide a stable tool for monitoring current infection levels as well as predicting infection numbers in the immediate future at the regional level. We accomplish this through nowcasting of cases that have not yet been reported as well as through predictions of future infections. We apply our model to German data, for which our focus lies in predicting and explain infectious behavior by district.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-021-00433-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39942283","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: 11