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Beyond catastrophic payments: modeling household health expenditure shares with endogenous selection 超越灾难性支付：基于内生选择的家庭医疗支出分担模型
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-12-21 DOI: 10.1007/s10182-024-00519-w
Antonello Maruotti, Pierfrancesco Alaimo Di Loro, Cathleen Johnson
{"title":"Beyond catastrophic payments: modeling household health expenditure shares with endogenous selection","authors":"Antonello Maruotti,&nbsp;Pierfrancesco Alaimo Di Loro,&nbsp;Cathleen Johnson","doi":"10.1007/s10182-024-00519-w","DOIUrl":"10.1007/s10182-024-00519-w","url":null,"abstract":"<div><p>The primary purpose of this paper is to assess households’ burden due to out-of-pocket healthcare expenditures. These payments are modeled on a representative sample of 25668 Italian households as the fraction of out-of-pocket healthcare expenditures over the households’ capacity to pay. For this purpose, we propose extending the analysis of the so-called catastrophic payments by looking at the entire distribution of this ratio. We introduce a novel finite mixture regression able to capture different levels of heterogeneity in the data. By using such a model specification, the fairness of the Italian National Health Service and its determinants are investigated.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 2","pages":"363 - 386"},"PeriodicalIF":1.4,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145167896","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
A novel bootstrap goodness-of-fit test for normal linear regression models 一种新的正态线性回归模型的自举拟合优度检验
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-11-14 DOI: 10.1007/s10182-024-00517-y
Scott H. Koeneman, Joseph E. Cavanaugh
{"title":"A novel bootstrap goodness-of-fit test for normal linear regression models","authors":"Scott H. Koeneman,&nbsp;Joseph E. Cavanaugh","doi":"10.1007/s10182-024-00517-y","DOIUrl":"10.1007/s10182-024-00517-y","url":null,"abstract":"<div><p>In this work, the distributional properties of the goodness-of-fit term in likelihood-based information criteria are explored. These properties are then leveraged to construct a novel goodness-of-fit test for normal linear regression models that relies on a nonparametric bootstrap. Several simulation studies are performed to investigate the properties and efficacy of the developed procedure, with these studies demonstrating that the bootstrap test offers distinct advantages as compared to other methods of assessing the goodness-of-fit of a normal linear regression model. Our inferential technique can be employed using the <span>DBModelSelect</span> R package, available freely via the Comprehensive R Archive Network.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 3","pages":"443 - 461"},"PeriodicalIF":1.4,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00517-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145384849","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
Nowcasting GDP using machine learning methods 使用机器学习方法预测GDP
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-11-13 DOI: 10.1007/s10182-024-00515-0
Dennis Kant, Andreas Pick, Jasper de Winter
{"title":"Nowcasting GDP using machine learning methods","authors":"Dennis Kant,&nbsp;Andreas Pick,&nbsp;Jasper de Winter","doi":"10.1007/s10182-024-00515-0","DOIUrl":"10.1007/s10182-024-00515-0","url":null,"abstract":"<div><p>This paper compares the ability of several econometric and machine learning methods to nowcast GDP in (pseudo) real-time. The analysis takes the example of Dutch GDP over the period 1992Q1–2018Q4 using a broad data set of monthly indicators. It discusses the forecast accuracy but also analyzes the use of information from the large data set of macroeconomic and financial predictors. We find that, on average, the random forest provides the most accurate forecast and nowcasts, whilst the dynamic factor model provides the most accurate backcasts.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 1","pages":"1 - 24"},"PeriodicalIF":1.4,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00515-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530002","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
Change point detection in high dimensional covariance matrix using Pillai’s statistics 基于Pillai统计量的高维协方差矩阵变点检测
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-11-09 DOI: 10.1007/s10182-024-00516-z
Seonghun Cho, Minsup Shin, Young Hyun Cho, Johan Lim
{"title":"Change point detection in high dimensional covariance matrix using Pillai’s statistics","authors":"Seonghun Cho,&nbsp;Minsup Shin,&nbsp;Young Hyun Cho,&nbsp;Johan Lim","doi":"10.1007/s10182-024-00516-z","DOIUrl":"10.1007/s10182-024-00516-z","url":null,"abstract":"<div><p>This research proposes a method to test and estimate change points in the covariance structure of high-dimensional multivariate series data. Our method uses the trace of the beta matrix, known as Pillai’s statistics, to test the change in covariance matrix at each time point. We study the asymptotic normality of Pillai’s statistics for testing the equality of two covariance matrices when both sample size and dimension increase at the same rate. We test the existence of a single change point in a given time period using Cauchy combination test, the test using an weighted sum of Cauchy transformed <i>p</i>-values, and estimate the change point as the point whose statistic is the greatest. To test and estimate multiple change points, we use the idea of the wild binary segmentation and repeatedly apply the procedure for a single change point to each segmented period until no significant change point exists. We numerically provide the size and power of our method. We finally apply our procedure to finding abnormal behavior in the investment of a private equity fund.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 1","pages":"53 - 84"},"PeriodicalIF":1.4,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00516-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143530001","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
On the equivalence of two mixture models for rating data 分级数据两种混合模型的等价性
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-10-28 DOI: 10.1007/s10182-024-00513-2
Matteo Ventura, Ambra Macis, Marica Manisera, Paola Zuccolotto
{"title":"On the equivalence of two mixture models for rating data","authors":"Matteo Ventura,&nbsp;Ambra Macis,&nbsp;Marica Manisera,&nbsp;Paola Zuccolotto","doi":"10.1007/s10182-024-00513-2","DOIUrl":"10.1007/s10182-024-00513-2","url":null,"abstract":"<div><p>Questionnaires are useful tool for exploring respondents’ perceptions through ratings, assumed to result from a latent decision process (DP). The DP varies when respondents rate on Likert or Semantic Differential scales. A possible paradigm to formalize the DP is based on the presence of a feeling and an uncertainty latent component, originally proposed as the foundations of the CUB (Combination of Uniform and shifted Binomial) class. It can be assumed that with Likert scales, respondents begin reasoning from the bottom, progressing upwards based on their sensations. Conversely, Semantic Differential scale users are assumed to start from the middle and move either upward or downward. The CUM (Combination of Uniform and Multinomial), a new model in the CUB class, derived from this DP, analyzes rating data on a Semantic Differential scale. This paper defines the concept of local and global unidirectional equivalence and studies, from an analytical point of view, the conditions under which CUB and CUM models generate identical theoretical probabilities, in order to enhance the interpretative understanding of the models.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 2","pages":"387 - 411"},"PeriodicalIF":1.4,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145170213","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
Hidden-Markov models for ordinal time series 有序时间序列的隐马尔可夫模型
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-10-15 DOI: 10.1007/s10182-024-00514-1
Christian H. Weiß, Osama Swidan
{"title":"Hidden-Markov models for ordinal time series","authors":"Christian H. Weiß,&nbsp;Osama Swidan","doi":"10.1007/s10182-024-00514-1","DOIUrl":"10.1007/s10182-024-00514-1","url":null,"abstract":"<div><p>A common approach for modeling categorical time series is Hidden-Markov models (HMMs), where the actual observations are assumed to depend on hidden states in their behavior and transitions. Such categorical HMMs are even applicable to nominal data but suffer from a large number of model parameters. In the ordinal case, however, the natural order among the categorical outcomes offers the potential to reduce the number of parameters while improving their interpretability at the same time. The class of ordinal HMMs proposed in this article link a latent-variable approach with categorical HMMs. They are characterized by parametric parsimony and allow the easy calculation of relevant stochastic properties, such as marginal and bivariate probabilities. These points are illustrated by numerical examples and simulation experiments, where the performance of maximum likelihood estimation is analyzed in finite samples. The developed methodology is applied to real-world data from a health application.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 2","pages":"217 - 239"},"PeriodicalIF":1.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00514-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145165788","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
Spillovers effects and temporal dynamics on the impact of renewables on labour force: a world perspective 可再生能源对劳动力影响的溢出效应和时间动态：世界视角
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-09-25 DOI: 10.1007/s10182-024-00511-4
Anna Gloria Billé, Marco Rogna
{"title":"Spillovers effects and temporal dynamics on the impact of renewables on labour force: a world perspective","authors":"Anna Gloria Billé,&nbsp;Marco Rogna","doi":"10.1007/s10182-024-00511-4","DOIUrl":"10.1007/s10182-024-00511-4","url":null,"abstract":"<div><p>Prompted by the need to reduce the concentration of <span>({hbox {CO}}_2)</span> in the atmosphere in order to limit global warming, several countries are adopting policies to incentivize the production of clean energy. In this context, a relevant aspect to be examined is the effect of expanding renewable resources on employment. Despite the large use of panel and time series analysis to investigate the topic, most of the econometric models generally consider a very small number of regressors. Furthermore, the spatial component, a potentially important determinant of employment, has been always neglected. By making use of a relatively large dataset of 59 countries spanning for 19 years (from 1996 to 2014), the present paper tries to fill these gaps by specifying a dynamic spatial panel data (SDPD) model with fixed effects and spatial error autocorrelation. The specification of both the individual and time fixed effects allows us to consider both spatial and temporal heterogeneity. Moreover, their presence and the long panel dimension avoid spurious correlations (Granger and Hyung 1999). Proper marginal effects are then calculated also to reveal different impacts among countries worldwide. Our results confirm the positive role of expanding renewable energy production on employment, at the 5% significant level, leading also to significant total and direct short-term and long-terms marginal effects.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 4","pages":"637 - 665"},"PeriodicalIF":1.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915724","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
Goodness-of-fit testing in bivariate count time series based on a bivariate dispersion index 基于双变量离散指数的双变量计数时间序列拟合优度测试
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-09-17 DOI: 10.1007/s10182-024-00512-3
Huiqiao Wang, Christian H. Weiß, Mingming Zhang
{"title":"Goodness-of-fit testing in bivariate count time series based on a bivariate dispersion index","authors":"Huiqiao Wang,&nbsp;Christian H. Weiß,&nbsp;Mingming Zhang","doi":"10.1007/s10182-024-00512-3","DOIUrl":"10.1007/s10182-024-00512-3","url":null,"abstract":"<div><p>A common choice for the marginal distribution of a bivariate count time series is the bivariate Poisson distribution. In practice, however, when the count data exhibit zero inflation, overdispersion or non-stationarity features, such that a marginal bivariate Poisson distribution is not suitable. To test the discrepancy between the actual count data and the bivariate Poisson distribution, we propose a new goodness-of-fit test based on a bivariate dispersion index. The asymptotic distribution of the test statistic under the null hypothesis of a first-order bivariate integer-valued autoregressive model with marginal bivariate Poisson distribution is derived, and the finite-sample performance of the goodness-of-fit test is analyzed by simulations. A real-data example illustrate the application and usefulness of the test in practice.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 2","pages":"241 - 279"},"PeriodicalIF":1.4,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00512-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253230","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
Bayesian joint relatively quantile regression of latent ordinal multivariate linear models with application to multirater agreement analysis 贝叶斯联合相对量子回归潜序多元线性模型在多方一致分析中的应用
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-08-20 DOI: 10.1007/s10182-024-00509-y
YuZhu Tian, ChunHo Wu, ManLai Tang, MaoZai Tian
{"title":"Bayesian joint relatively quantile regression of latent ordinal multivariate linear models with application to multirater agreement analysis","authors":"YuZhu Tian,&nbsp;ChunHo Wu,&nbsp;ManLai Tang,&nbsp;MaoZai Tian","doi":"10.1007/s10182-024-00509-y","DOIUrl":"10.1007/s10182-024-00509-y","url":null,"abstract":"<div><p>In this paper, we propose a Bayesian quantile regression (QR) approach to jointly model multivariate ordinal data. Firstly, a multivariate latent variable model is used to link the multivariate ordinal data and latent continuous responses and the multivariate asymmetric Laplace (MAL) distribution is employed to construct the joint QR-based working likelihood for the considered model. Secondly, adaptive-<span>(L_{1/2})</span> penalization priors of regression parameters are incorporated into the working likelihood to implement high-dimensional Bayesian joint QR inference. Markov Chain Monte Carlo (MCMC) algorithm is utilized to derive the fully conditional posterior distributions of all parameters. Thirdly, Bayesian joint relatively QR estimation approach is recommended to result in more efficient estimation results. Finally, Monte Carlo simulation studies and a real instance analysis of multirater agreement data are presented to illustrate the performance of the proposed Bayesian joint relatively QR approach.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 1","pages":"85 - 116"},"PeriodicalIF":1.4,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202706","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
A Finite-sample bias correction method for general linear model in the presence of differential measurement errors 差异测量误差下一般线性模型的有限样本偏差校正方法
IF 1.4 4区 数学
Asta-Advances in Statistical Analysis Pub Date : 2024-08-14 DOI: 10.1007/s10182-024-00510-5
Ali Al-Sharadqah, Karine Bagdasaryan, Ola Nusierat
{"title":"A Finite-sample bias correction method for general linear model in the presence of differential measurement errors","authors":"Ali Al-Sharadqah,&nbsp;Karine Bagdasaryan,&nbsp;Ola Nusierat","doi":"10.1007/s10182-024-00510-5","DOIUrl":"10.1007/s10182-024-00510-5","url":null,"abstract":"<div><p>This paper focuses on the general linear measurement error model, in which some or all predictors are measured with error, while others are measured precisely. We propose a semi-parametric estimator that works under general mechanisms of measurement error, including differential and non-differential errors. Other popular methods, such as the corrected score and conditional score methods, only work for non-differential measurement error models, but our estimator works in all scenarios. We develop our estimator by considering a family of objective functions that depend on an unspecified weight function. Using statistical error analysis and perturbation theory, we derive the optimal weight function under the small-sigma regime. The resulting estimator is statistically optimal in all senses. Even though we develop it under the small-sigma regime, we also establish its consistency and asymptotic normality under the large sample regime. Finally, we conduct a series of numerical experiments to confirm that the proposed estimator outperforms other existing methods.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 1","pages":"149 - 195"},"PeriodicalIF":1.4,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142202707","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
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