Asta-Advances in Statistical Analysis最新文献_第5页

Left-truncated health insurance claims data: theoretical review and empirical application 左截断医疗保险理赔数据:理论回顾与实证应用

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-02-02 DOI: 10.1007/s10182-023-00471-1

Rafael Weißbach, Achim Dörre, Dominik Wied, Gabriele Doblhammer, Anne Fink

{"title":"Left-truncated health insurance claims data: theoretical review and empirical application","authors":"Rafael Weißbach, Achim Dörre, Dominik Wied, Gabriele Doblhammer, Anne Fink","doi":"10.1007/s10182-023-00471-1","DOIUrl":"10.1007/s10182-023-00471-1","url":null,"abstract":"<div><p>From the inventory of the health insurer AOK in 2004, we draw a sample of a quarter million people and follow each person’s health claims continuously until 2013. Our aim is to estimate the effect of a stroke on the dementia onset probability for Germans born in the first half of the 20th century. People deceased before 2004 are randomly left-truncated, and especially their number is unknown. Filtrations, modelling the missing data, enable circumventing the unknown number of truncated persons by using a conditional likelihood. Dementia onset after 2013 is a fixed right-censoring event. For each observed health history, Jacod’s formula yields its conditional likelihood contribution. Asymptotic normality of the estimated intensities is derived, related to a sample size definition including the number of truncated people. The standard error results from the asymptotic normality and is easily computable, despite the unknown sample size. The claims data reveal that after a stroke, with time measured in years, the intensity of dementia onset increases from 0.02 to 0.07. Using the independence of the two estimated intensities, a 95% confidence interval for their difference is [0.053, 0.057]. The effect halves when we extend the analysis to an age-inhomogeneous model, but does not change further when we additionally adjust for multi-morbidity.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-023-00471-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42282189","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

Statistical guarantees for sparse deep learning 稀疏深度学习的统计保障

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-01-24 DOI: 10.1007/s10182-022-00467-3

Johannes Lederer

引用次数: 0

Addressing non-normality in multivariate analysis using the t-distribution 利用t分布解决多元分析中的非正态性

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2023-01-21 DOI: 10.1007/s10182-022-00468-2

Felipe Osorio, Manuel Galea, Claudio Henríquez, Reinaldo Arellano-Valle

引用次数: 0

Bayesian ridge regression for survival data based on a vine copula-based prior 基于vine copula先验的生存数据贝叶斯脊回归

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-12-30 DOI: 10.1007/s10182-022-00466-4

Hirofumi Michimae, Takeshi Emura

引用次数: 0

Hedonic pricing modelling with unstructured predictors: an application to Italian Fashion Industry 具有非结构化预测因子的Hedonic定价模型在意大利时装业中的应用

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-12-13 DOI: 10.1007/s10182-022-00465-5

Federico Crescenzi

引用次数: 0

Estimating the Impact of Medical Care Usage on Work Absenteeism by a Trivariate Probit Model with Two Binary Endogenous Explanatory Variables 用二元内生解释变量的三元Probit模型估计医疗服务使用对工作缺勤的影响

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-10-18 DOI: 10.1007/s10182-022-00464-6

Panagiota Filippou, Giampiero Marra, Rosalba Radice, David Zimmer

{"title":"Estimating the Impact of Medical Care Usage on Work Absenteeism by a Trivariate Probit Model with Two Binary Endogenous Explanatory Variables","authors":"Panagiota Filippou, Giampiero Marra, Rosalba Radice, David Zimmer","doi":"10.1007/s10182-022-00464-6","DOIUrl":"10.1007/s10182-022-00464-6","url":null,"abstract":"<div><p>The aim of this paper is to estimate the effects of seeking medical care on missing work. Specifically, our case study explores the question: Does visiting a medical provider cause an employee to miss work? To address this, we employ a model that can consistently estimate the impacts of two endogenous binary regressors. The model is based on three equations connected via a multivariate Gaussian distribution, which makes it possible to model the correlations among the equations, hence accounting for unobserved heterogeneity. Parameter estimation is reliably carried out via a trust region algorithm with analytical derivative information. We find that, observationally, having a curative visit associates with a nearly 80% increase in the probability of missing work, while having a preventive visit correlates with a smaller 13% increase in the likelihood of missing work. However, after addressing potential endogeneity, neither type of visit appears to significantly relate to missing work. That finding also applies to visits that occur during the previous year. Therefore, we conclude that the observed links between medical usage and absenteeism derive from unobserved heterogeneity, rather than direct causal channels. The modeling framework is available through the <span>R</span> package <span>GJRM</span>.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42881312","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

Control charts for measurement error models 测量误差模型控制图

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-10-05 DOI: 10.1007/s10182-022-00462-8

Vasyl Golosnoy, Benno Hildebrandt, Steffen Köhler, Wolfgang Schmid, Miriam Isabel Seifert

引用次数: 0

Sieve bootstrapping the memory parameter in long-range dependent stationary functional time series 筛网自举的记忆参数在长期依赖平稳函数时间序列

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-10-01 DOI: 10.1007/s10182-022-00463-7

Han Lin Shang

{"title":"Sieve bootstrapping the memory parameter in long-range dependent stationary functional time series","authors":"Han Lin Shang","doi":"10.1007/s10182-022-00463-7","DOIUrl":"10.1007/s10182-022-00463-7","url":null,"abstract":"<div><p>We consider a sieve bootstrap procedure to quantify the estimation uncertainty of long-memory parameters in stationary functional time series. We use a semiparametric local Whittle estimator to estimate the long-memory parameter. In the local Whittle estimator, discrete Fourier transform and periodogram are constructed from the first set of principal component scores via a functional principal component analysis. The sieve bootstrap procedure uses a general vector autoregressive representation of the estimated principal component scores. It generates bootstrap replicates that adequately mimic the dependence structure of the underlying stationary process. We first compute the estimated first set of principal component scores for each bootstrap replicate and then apply the semiparametric local Whittle estimator to estimate the memory parameter. By taking quantiles of the estimated memory parameters from these bootstrap replicates, we can nonparametrically construct confidence intervals of the long-memory parameter. As measured by coverage probability differences between the empirical and nominal coverage probabilities at three levels of significance, we demonstrate the advantage of using the sieve bootstrap compared to the asymptotic confidence intervals based on normality.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-022-00463-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46807934","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

Distributional properties of continuous time processes: from CIR to bates 连续时间过程的分布性质:从CIR到bates

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-08-25 DOI: 10.1007/s10182-022-00459-3

Ostap Okhrin, Michael Rockinger, Manuel Schmid

引用次数: 1

Hierarchical disjoint principal component analysis 层次不相交主成分分析

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2022-08-24 DOI: 10.1007/s10182-022-00458-4

Carlo Cavicchia, Maurizio Vichi, Giorgia Zaccaria

{"title":"Hierarchical disjoint principal component analysis","authors":"Carlo Cavicchia, Maurizio Vichi, Giorgia Zaccaria","doi":"10.1007/s10182-022-00458-4","DOIUrl":"10.1007/s10182-022-00458-4","url":null,"abstract":"<div><p>Dimension reduction, by means of Principal Component Analysis (PCA), is often employed to obtain a reduced set of components preserving the largest possible part of the total variance of the observed variables. Several methodologies have been proposed either to improve the interpretation of PCA results (e.g., by means of orthogonal, oblique rotations, shrinkage methods), or to model oblique components or factors with a hierarchical structure, such as in Bi-factor and High-Order Factor analyses. In this paper, we propose a new methodology, called Hierarchical Disjoint Principal Component Analysis (HierDPCA), that aims at building a hierarchy of disjoint principal components of maximum variance associated with disjoint groups of observed variables, from <i>Q</i> up to a unique, general one. HierDPCA also allows choosing the type of the relationship among disjoint principal components of two sequential levels, from the lowest upwards, by testing the component correlation per level and changing from a reflective to a formative approach when this correlation turns out to be not statistically significant. The methodology is formulated in a semi-parametric least-squares framework and a coordinate descent algorithm is proposed to estimate the model parameters. A simulation study and two real applications are illustrated to highlight the empirical properties of the proposed methodology.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-022-00458-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42994839","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