Econometrics and Statistics最新文献_第4页

Rejoinder to the comment of Agostinelli 对阿戈斯蒂内利评论的反驳

IF 1.9

Econometrics and Statistics Pub Date : 2024-02-17 DOI: 10.1016/j.ecosta.2024.02.004

Jakob Raymaekers, Peter J. Rousseeuw

引用次数: 0

Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process 利用分层 Dirichlet 过程对单细胞 RNA 测序数据集进行共享差异聚类

IF 1.9

Econometrics and Statistics Pub Date : 2024-02-17 DOI: 10.1016/j.ecosta.2024.02.001

Jinlu Liu, Sara Wade, Natalia Bochkina

{"title":"Shared Differential Clustering across Single-cell RNA Sequencing Datasets with the Hierarchical Dirichlet Process","authors":"Jinlu Liu, Sara Wade, Natalia Bochkina","doi":"10.1016/j.ecosta.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.ecosta.2024.02.001","url":null,"abstract":"Single-cell RNA sequencing (scRNA-seq) is a powerful technology that allows researchers to understand gene expression patterns at the single-cell level and uncover the heterogeneous nature of cells. Clustering is an important tool in scRNA-seq analysis to discover groups of cells with similar gene expression patterns and identify potential cell types. Integration of multiple scRNA-seq datasets is a pressing challenge, and in this direction, a novel model is developed to extend clustering methods to appropriately combine inference across multiple datasets. The model simultaneously addresses normalization to deal with the inherent noise and uncertainty in scRNA-seq, infers cell types, and integrates multiple datasets for shared clustering in principled manner through a hierarchical Bayesian framework. A Gibbs sampler is developed that copes with the high-dimensionality of scRNA-seq through consensus clustering. The methodological developments are driven by experimental data from embryonic cells, with the aim of understanding the role of PAX6 in prenatal development, and more specifically how cell-subtypes and their proportions change when knocking out this factor.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"56 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139920378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multivariate Hermite polynomials and information matrix tests 多元赫米特多项式和信息矩阵检验

IF 1.9

Econometrics and Statistics Pub Date : 2024-02-06 DOI: 10.1016/j.ecosta.2024.01.005

Dante Amengual, Gabriele Fiorentini, Enrique Sentana

引用次数: 0

Cluster Regularization via a Hierarchical Feature Regression 通过分层特征回归实现集群正规化

IF 1.9

Econometrics and Statistics Pub Date : 2024-01-19 DOI: 10.1016/j.ecosta.2024.01.003

Johann Pfitzinger

引用次数: 0

Inference on Multiple Change Points in High Dimensional Linear Regression Models 高维线性回归模型中多个变化点的推论

IF 1.9

Econometrics and Statistics Pub Date : 2024-01-17 DOI: 10.1016/j.ecosta.2024.01.002

Hongjin Zhang, Abhishek Kaul

引用次数: 0

Highly irregular serial correlation tests 高度不规则的序列相关性测试

IF 1.9

Econometrics and Statistics Pub Date : 2024-01-09 DOI: 10.1016/j.ecosta.2024.01.001

Dante Amengual, Xinyue Bei, Enrique Sentana

引用次数: 0

Forecasting corporate credit spreads: regime-switching in LSTM 预测公司信贷息差：LSTM 中的制度转换

IF 1.9

Econometrics and Statistics Pub Date : 2023-12-30 DOI: 10.1016/j.ecosta.2023.12.002

Christina Erlwein-Sayer, Stefanie Grimm, Alexander Pieper, Rümeysa Alsaç

{"title":"Forecasting corporate credit spreads: regime-switching in LSTM","authors":"Christina Erlwein-Sayer, Stefanie Grimm, Alexander Pieper, Rümeysa Alsaç","doi":"10.1016/j.ecosta.2023.12.002","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.12.002","url":null,"abstract":"<p>A long short-term memory model (LSTM) which utilises regime-switching state information as a feature to predict the change of credit spreads is developed. Latent changes in the market are filtered out from observable credit spread time series. These hidden information of regime changes are incorporated into an LSTM, where the state probability is utilised as a feature for one-step ahead predictions of the credit spreads. Firstly, time series from corporate credit spreads are modelled through a Hidden Markov model (HMM) which is based on a discretised Ornstein-Uhlenbeck process. State-related information of the Markov chain, like the jump frequency and state occupation time hidden in the observed spreads are filtered out and adaptive HMM filters are built to estimate probabilities of hidden market states. The performance of the LSTM with regime-switching information is analysed and compared to the accuracy of a pure LSTM without state features. Furthermore, purely utilising the HMM forecast, the prediction of the credit spread is compared to the prediction within the LSTM. Beyond a simulations study, the HMM-LSTM model is calibrated on corporate credit spreads from three European countries between 2004 and 2019. The findings show that the LSTM forecast error is improved when regime information is added, mostly in cases with stronger market fluctuations.</p>","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"23 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139062651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stein-type control function maximum likelihood estimator for the probit model in the presence of endogeneity 存在内生性的概率模型的斯坦因型控制函数最大似然估计器

IF 1.9

Econometrics and Statistics Pub Date : 2023-12-10 DOI: 10.1016/j.ecosta.2023.12.001

Muhammad Qasim, Kristofer Månsson, Pär Sjölander, B. M. Golam Kibria

引用次数: 0

Large Sample Properties of Entropy Balancing Estimators of Average Causal Effects 平均因果效应熵平衡估计的大样本性质

IF 1.9

Econometrics and Statistics Pub Date : 2023-11-30 DOI: 10.1016/j.ecosta.2023.11.004

David Källberg, Ingeborg Waernbaum

引用次数: 0

A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks 动态时空随机波动模型及其在环境风险中的应用

Econometrics and Statistics Pub Date : 2023-11-01 DOI: 10.1016/j.ecosta.2023.11.002

Philipp Otto, Osman Doğan, Süleyman Taşpınar

{"title":"A Dynamic Spatiotemporal Stochastic Volatility Model with an Application to Environmental Risks","authors":"Philipp Otto, Osman Doğan, Süleyman Taşpınar","doi":"10.1016/j.ecosta.2023.11.002","DOIUrl":"https://doi.org/10.1016/j.ecosta.2023.11.002","url":null,"abstract":"A dynamic spatiotemporal stochastic volatility (SV) model is introduced, incorporating explicit terms accounting for spatial, temporal, and spatiotemporal spillover effects. Alongside these features, the model encompasses time-invariant site-specific factors, allowing for differentiation in volatility levels across locations. The statistical properties of an outcome variable within this model framework are examined, revealing the induction of spatial dependence in the outcome variable. Additionally, a Bayesian estimation procedure employing the Markov Chain Monte Carlo (MCMC) approach, complemented by a suitable data transformation, is presented. Simulation experiments are conducted to assess the performance of the proposed Bayesian estimator. Subsequently, the model is applied in the domain of environmental risk modeling, addressing the scarcity of empirical studies in this field. The significance of climate variation studies is emphasized, illustrated by an analysis of local air quality in Northern Italy during 2021, which underscores pronounced spatial and temporal clusters and increased uncertainties/risks during the winter season compared to the summer season.","PeriodicalId":54125,"journal":{"name":"Econometrics and Statistics","volume":"5 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135564688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2