Asta-Advances in Statistical Analysis最新文献

Nowcasting GDP using machine learning methods

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

引用次数: 0

Change point detection in high dimensional covariance matrix using Pillai’s statistics

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, Minsup Shin, Young Hyun Cho, 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

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

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

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

引用次数: 0

Classes of probability measures built on the properties of Benford’s law 基于本福德定律性质的概率度量类别

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-08-08 DOI: 10.1007/s10182-024-00505-2

Roy Cerqueti, Mario Maggi

{"title":"Classes of probability measures built on the properties of Benford’s law","authors":"Roy Cerqueti, Mario Maggi","doi":"10.1007/s10182-024-00505-2","DOIUrl":"10.1007/s10182-024-00505-2","url":null,"abstract":"<div><p>Benford’s law is a particular discrete probability distribution that is often satisfied by the significant digits of a dataset. The nonconformity with Benford’s law suggests the possible presence of data manipulation. This paper introduces two novel generalized versions of Benford’s law that are less restrictive than the original Benford’s law—hence, leading to more probable conformity of a given dataset. Such generalizations are grounded on the existing mathematical relations between Benford’s law probability distribution elements. Moreover, one of them leads to a set of probability distributions that is a proper subset of that of the other one. We show that the considered versions of Benford’s law have a geometric representation on the three-dimensional Euclidean space. Through suitable optimization models, we show that all the probability distributions satisfying the more restrictive generalization exhibit at least acceptable conformity with Benford’s law, according to the most popular distance measures. We also present some examples to highlight the practical usefulness of the introduced devices.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 1","pages":"197 - 216"},"PeriodicalIF":1.4,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00505-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948728","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

Publisher Correction: Deducing neighborhoods of classes from a fitted model 出版商更正：从拟合模型推导类邻域

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-07-30 DOI: 10.1007/s10182-024-00508-z

Alexander Gerharz, Andreas Groll, Gunther Schauberger

引用次数: 0

Wasserstein barycenter regression: application to the joint dynamics of regional GDP and life expectancy in Italy 瓦瑟施泰因原点回归：应用于意大利地区国内生产总值和预期寿命的联合动态变化

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-07-16 DOI: 10.1007/s10182-024-00506-1

Susanna Levantesi, Andrea Nigri, Paolo Pagnottoni, Alessandro Spelta

引用次数: 0

A spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread 二元数据时空模型及其在分析 COVID-19 传播方向中的应用

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-07-08 DOI: 10.1007/s10182-024-00507-0

Anagh Chattopadhyay, Soudeep Deb

{"title":"A spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread","authors":"Anagh Chattopadhyay, Soudeep Deb","doi":"10.1007/s10182-024-00507-0","DOIUrl":"10.1007/s10182-024-00507-0","url":null,"abstract":"<div><p>It is often of primary interest to analyze and forecast the levels of a continuous phenomenon as a categorical variable. In this paper, we propose a new spatio-temporal model to deal with this problem in a binary setting, with an interesting application related to the COVID-19 pandemic, a phenomena that depends on both spatial proximity and temporal auto-correlation. Our model is defined through a hierarchical structure for the latent variable, which corresponds to the probit-link function. The mean of the latent variable in the proposed model is designed to capture the trend and the seasonal pattern as well as the lagged effects of relevant regressors. The covariance structure of the model is defined as an additive combination of a zero-mean spatio-temporally correlated process and a white noise process. The parameters associated with the space-time process enable us to analyze the effect of proximity of two points with respect to space or time and its influence on the overall process. For estimation and prediction, we adopt a complete Bayesian framework along with suitable prior specifications and utilize the concepts of Gibbs sampling. Using the county-level data from the state of New York, we show that the proposed methodology provides superior performance than benchmark techniques. We also use our model to devise a novel mechanism for predictive clustering which can be leveraged to develop localized policies.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 4","pages":"823 - 851"},"PeriodicalIF":1.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141567508","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

Artwork pricing model integrating the popularity and ability of artists 整合艺术家人气和能力的艺术品定价模式

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-07-02 DOI: 10.1007/s10182-024-00504-3

Jinsu Park, Yoonjin Lee, Daewon Yang, Jongho Park, Hohyun Jung

{"title":"Artwork pricing model integrating the popularity and ability of artists","authors":"Jinsu Park, Yoonjin Lee, Daewon Yang, Jongho Park, Hohyun Jung","doi":"10.1007/s10182-024-00504-3","DOIUrl":"10.1007/s10182-024-00504-3","url":null,"abstract":"<div><p>Considerable research has been devoted to understanding the popularity effect on the art market dynamics, meaning that artworks by popular artists tend to have high prices. The hedonic pricing model has employed artists’ reputation attributes, such as survey results, to understand the popularity effect, but the reputation attributes are constant and not properly defined at the point of artwork sales. Moreover, the artist’s ability has been measured via random effect in the hedonic model, which fails to reflect ability changes. To remedy these problems, we present a method to define the popularity measure using the artwork sales dataset without relying on the artist’s reputation attributes. Also, we propose a novel pricing model to appropriately infer the time-dependent artist’s abilities using the presented popularity measure. An inference algorithm is presented using the EM algorithm and Gibbs sampling to estimate model parameters and artist abilities. We use the Artnet dataset to investigate the size of the rich-get-richer effect and the variables affecting artwork prices in real-world art market dynamics. We further conduct inferences about artists’ abilities under the popularity effect and examine how ability changes over time for various artists with remarkable interpretations.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"108 4","pages":"889 - 913"},"PeriodicalIF":1.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10182-024-00504-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141509883","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