Asta-Advances in Statistical Analysis最新文献

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

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":"https://doi.org/10.1007/s10182-024-00507-0","url":null,"abstract":"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.","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"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":"https://doi.org/10.1007/s10182-024-00504-3","url":null,"abstract":"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.","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Editorial special issue: Bridging the gap between AI and Statistics 编辑特刊：缩小人工智能与统计学之间的差距

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-06-21 DOI: 10.1007/s10182-024-00503-4

Benjamin Säfken, David Rügamer

引用次数: 0

Markov-switching decision trees 马尔可夫转换决策树

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-05-29 DOI: 10.1007/s10182-024-00501-6

Timo Adam, Marius Ötting, Rouven Michels

引用次数: 0

Markov switching stereotype logit models for longitudinal ordinal data affected by unobserved heterogeneity in responding behavior 受反应行为中未观察到的异质性影响的纵向序数数据的马尔可夫转换定型 Logit 模型

IF 1.4 4区数学

Asta-Advances in Statistical Analysis Pub Date : 2024-05-15 DOI: 10.1007/s10182-024-00500-7

Roberto Colombi, Sabrina Giordano

{"title":"Markov switching stereotype logit models for longitudinal ordinal data affected by unobserved heterogeneity in responding behavior","authors":"Roberto Colombi, Sabrina Giordano","doi":"10.1007/s10182-024-00500-7","DOIUrl":"https://doi.org/10.1007/s10182-024-00500-7","url":null,"abstract":"When asked to assess their opinion about attitudes or perceptions on Likert-scale, respondents often endorse the midpoint or extremes of the scale and agree or disagree regardless of the content. These responding behaviors are known in the psychometric literature as middle, extremes, aquiescence and disacquiescence response styles that generally introduce bias in the results. One of the key motivations behind our approach is to account for these attitudes and how they evolve over time. The novelty of our proposal, in the context of longitudinal ordered categorical data, is in considering simultaneously the temporal dynamics of the responses (observable ordinal variables) and unobservable answering behaviors, possibly influenced by response styles, through a Markov switching logit model with two latent components. One component accommodates serial dependence and respondent’s unobserved heterogeneity, the other component determines the responding attitude (due to response styles or not). The dependence of the responses on covariates is modelled by a stereotype logit model with parameters varying according to the two latent components. The stereotype logit model is adopted because it is a flexible extension of the proportional odds logit model that retains the advantage of using a single parameter to describe a regressor effect. In the paper, a new interpretation of the parameters of the stereotype model is given by defining the allocation sets as intervals of values of the linear predictor that identify the most probable response. Unobserved heterogeneity, serial dependence and tendency to response style are modelled through our approach on longitudinal data, collected by the Bank of Italy.","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141059332","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