Asta-Advances in Statistical Analysis最新文献_第9页

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":"106 3","pages":"473 - 497"},"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":"106 3","pages":"427 - 454"},"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":"106 3","pages":"407 - 426"},"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

Model-based clustering via new parsimonious mixtures of heavy-tailed distributions 基于模型的重尾分布新简约混合聚类

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-01-14 DOI: 10.1007/s10182-021-00430-8

Salvatore D. Tomarchio, Luca Bagnato, Antonio Punzo

引用次数: 6

Diagnostic checking of multiple imputation models 多种输入模型的诊断检查

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-01-14 DOI: 10.1007/s10182-021-00429-1

Yang Zhao

引用次数: 0

Diagnostic checking of multiple imputation models 多种输入模型的诊断检查

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-01-14 DOI: 10.1007/s10182-021-00429-1

Yang Zhao

{"title":"Diagnostic checking of multiple imputation models","authors":"Yang Zhao","doi":"10.1007/s10182-021-00429-1","DOIUrl":"10.1007/s10182-021-00429-1","url":null,"abstract":"<div><p>Model checking in multiple imputation (MI, Rubin in Multiple imputation for nonresponse in surveys, Wiley, New York, 1987) becomes increasingly important with the recent developments in MI and its widespread use in statistical analysis with missing data (e.g. van Buuren et al. in J Stat Comput Simul 76(12):1049–1064, 2006; van Buuren and Groothuis-Oudshoorn in J Stat Soft 45(3):1–67, 2011; Chen et al. in Biometrics 67:799–809, 2011; Nguyen et al. in Emerg Themes Epidemiol 14(8):1–12, 2017). The currently recommended posterior predictive checking method (He and Zaslavsky in Stat Med 31:1–18, 2012; Nguyen et al. in Biom J 4:676–694, 2015) is less effective when the proportion of missing values increases and its produced posterior predictive <i>p</i> value is not supported by a null distribution as a standard <i>p</i> value (Meng in Annu Stat 22:1142–1160, 1994). This research develops a new diagnostic method for checking MI models and proposes a test statistic with a standard <i>p</i> value. The new diagnostic checking method is effective and flexible. It does not depend on the proportion of missing values and can deal with data sets with arbitrary nonmonotone missing data patterns. We examine the performance of the proposed method in a simulation study and illustrate the method in a study of coronary disease and associated factors.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"106 2","pages":"271 - 286"},"PeriodicalIF":1.4,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-021-00429-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50050052","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: 2

A spatial randomness test based on the box-counting dimension 基于盒计数维度的空间随机性检验

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-01-05 DOI: 10.1007/s10182-021-00434-4

Yolanda Caballero, Ramón Giraldo, Jorge Mateu

引用次数: 1

An integrated local depth measure 一种综合的局部深度测量

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-01-03 DOI: 10.1007/s10182-021-00424-6

Lucas Fernandez-Piana, Marcela Svarc

引用次数: 0

Improving the causal treatment effect estimation with propensity scores by the bootstrap 利用bootstrap改进倾向评分的因果治疗效果估计

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2021-12-21 DOI: 10.1007/s10182-021-00427-3

Maeregu W. Arisido, Fulvia Mecatti, Paola Rebora

{"title":"Improving the causal treatment effect estimation with propensity scores by the bootstrap","authors":"Maeregu W. Arisido, Fulvia Mecatti, Paola Rebora","doi":"10.1007/s10182-021-00427-3","DOIUrl":"10.1007/s10182-021-00427-3","url":null,"abstract":"<div><p>When observational studies are used to establish the causal effects of treatments, the estimated effect is affected by treatment selection bias. The inverse propensity score weight (IPSW) is often used to deal with such bias. However, IPSW requires strong assumptions whose misspecifications and strategies to correct the misspecifications were rarely studied. We present a bootstrap bias correction of IPSW (BC-IPSW) to improve the performance of propensity score in dealing with treatment selection bias in the presence of failure to the ignorability and overlap assumptions. The approach was motivated by a real observational study to explore the potential of anticoagulant treatment for reducing mortality in patients with end-stage renal disease. The benefit of the treatment to enhance survival was demonstrated; the suggested BC-IPSW method indicated a statistically significant reduction in mortality for patients receiving the treatment. Using extensive simulations, we show that BC-IPSW substantially reduced the bias due to the misspecification of the ignorability and overlap assumptions. Further, we showed that IPSW is still useful to account for the lack of treatment randomization, but its advantages are stringently linked to the satisfaction of ignorability, indicating that the existence of relevant though unmeasured or unused covariates can worsen the selection bias.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"106 3","pages":"455 - 471"},"PeriodicalIF":1.4,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-021-00427-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46055305","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

Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models 足球运动损伤的预测:使用正则化Cox模型的重复时间-事件方法

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2021-11-20 DOI: 10.1007/s10182-021-00428-2

Lore Zumeta-Olaskoaga, Maximilian Weigert, Jon Larruskain, Eder Bikandi, Igor Setuain, Josean Lekue, Helmut Küchenhoff, Dae-Jin Lee

{"title":"Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models","authors":"Lore Zumeta-Olaskoaga, Maximilian Weigert, Jon Larruskain, Eder Bikandi, Igor Setuain, Josean Lekue, Helmut Küchenhoff, Dae-Jin Lee","doi":"10.1007/s10182-021-00428-2","DOIUrl":"10.1007/s10182-021-00428-2","url":null,"abstract":"<div><p>Data-based methods and statistical models are given special attention to the study of sports injuries to gain in-depth understanding of its risk factors and mechanisms. The objective of this work is to evaluate the use of shared frailty Cox models for the prediction of occurring sports injuries, and to compare their performance with different sets of variables selected by several regularized variable selection approaches. The study is motivated by specific characteristics commonly found for sports injury data, that usually include reduced sample size and even fewer number of injuries, coupled with a large number of potentially influential variables. Hence, we conduct a simulation study to address these statistical challenges and to explore regularized Cox model strategies together with shared frailty models in different controlled situations. We show that predictive performance greatly improves as more player observations are available. Methods that result in sparse models and favour interpretability, e.g. Best Subset Selection and Boosting, are preferred when the sample size is small. We include a real case study of injuries of female football players of a Spanish football club.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"107 1-2","pages":"101 - 126"},"PeriodicalIF":1.4,"publicationDate":"2021-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-021-00428-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46564718","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: 2