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Some approximations to the path formula for some nonlinear models 某些非线性模型路径公式的近似值
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-09-18 DOI: 10.1111/sjos.12753
Christiana Kartsonaki
{"title":"Some approximations to the path formula for some nonlinear models","authors":"Christiana Kartsonaki","doi":"10.1111/sjos.12753","DOIUrl":"https://doi.org/10.1111/sjos.12753","url":null,"abstract":"In linear least squares regression there exists a simple decomposition of the effect of an exposure on an outcome into two parts in the presence of an intermediate variable. This decomposition is described and then analogous decompositions for other models are examined, namely for logistic regression and proportional hazards models.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253099","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
Model‐based clustering in simple hypergraphs through a stochastic blockmodel 通过随机块模型在简单超图中进行基于模型的聚类
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-09-18 DOI: 10.1111/sjos.12754
Luca Brusa, Catherine Matias
{"title":"Model‐based clustering in simple hypergraphs through a stochastic blockmodel","authors":"Luca Brusa, Catherine Matias","doi":"10.1111/sjos.12754","DOIUrl":"https://doi.org/10.1111/sjos.12754","url":null,"abstract":"We propose a model to address the overlooked problem of node clustering in simple hypergraphs. Simple hypergraphs are suitable when a node may not appear multiple times in the same hyperedge, such as in co‐authorship datasets. Our model generalizes the stochastic blockmodel for graphs and assumes the existence of latent node groups and hyperedges are conditionally independent given these groups. We first establish the generic identifiability of the model parameters. We then develop a variational approximation Expectation‐Maximization algorithm for parameter inference and node clustering, and derive a statistical criterion for model selection. To illustrate the performance of our <jats:styled-content>R</jats:styled-content> package <jats:styled-content>HyperSBM</jats:styled-content>, we compare it with other node clustering methods using synthetic data generated from the model, as well as from a line clustering experiment and a co‐authorship dataset.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253095","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
Tobit models for count time series 计数时间序列的 Tobit 模型
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-09-13 DOI: 10.1111/sjos.12751
Christian H. Weiß, Fukang Zhu
{"title":"Tobit models for count time series","authors":"Christian H. Weiß, Fukang Zhu","doi":"10.1111/sjos.12751","DOIUrl":"https://doi.org/10.1111/sjos.12751","url":null,"abstract":"Several models for count time series have been developed during the last decades, often inspired by traditional autoregressive moving average (ARMA) models for real‐valued time series, including integer‐valued ARMA (INARMA) and integer‐valued generalized autoregressive conditional heteroscedasticity (INGARCH) models. Both INARMA and INGARCH models exhibit an ARMA‐like autocorrelation function (ACF). To achieve negative ACF values within the class of INGARCH models, log and softplus link functions are suggested in the literature, where the softplus approach leads to conditional linearity in good approximation. However, the softplus approach is limited to the INGARCH family for unbounded counts, that is, it can neither be used for bounded counts, nor for count processes from the INARMA family. In this paper, we present an alternative solution, named the Tobit approach, for achieving approximate linearity together with negative ACF values, which is more generally applicable than the softplus approach. A Skellam–Tobit INGARCH model for unbounded counts is studied in detail, including stationarity, approximate computation of moments, maximum likelihood and censored least absolute deviations estimation for unknown parameters and corresponding simulations. Extensions of the Tobit approach to other situations are also discussed, including underlying discrete distributions, INAR models, and bounded counts. Three real‐data examples are considered to illustrate the usefulness of the new approach.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253096","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
On some publications of Sir David Cox 关于戴维-考克斯爵士的一些出版物
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-09-12 DOI: 10.1111/sjos.12752
Nancy Reid
{"title":"On some publications of Sir David Cox","authors":"Nancy Reid","doi":"10.1111/sjos.12752","DOIUrl":"https://doi.org/10.1111/sjos.12752","url":null,"abstract":"Sir David Cox published four papers in the <jats:italic>Scandinavian Journal of Statistics</jats:italic> and two in the <jats:italic>Scandinavian Actuarial Journal</jats:italic>. This note provides some brief summaries of these papers.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186964","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
Looking back: Selected contributions by C. R. Rao to multivariate analysis 回顾过去：C. R. Rao 对多元分析的部分贡献
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-08-26 DOI: 10.1111/sjos.12749
Dianna Smith
{"title":"Looking back: Selected contributions by C. R. Rao to multivariate analysis","authors":"Dianna Smith","doi":"10.1111/sjos.12749","DOIUrl":"https://doi.org/10.1111/sjos.12749","url":null,"abstract":"Statistician C. R. Rao made many contributions to multivariate analysis over the span of his career. Some of his earliest contributions continue to be used and built upon almost 80 years later, while his more recent contributions spur new avenues of research. The present article discusses these contributions, how they helped shape multivariate analysis as we see it today, and what we may learn from reviewing his works. Topics include his extension of linear discriminant analysis, Rao's perimeter test, Rao's U statistic, his asymptotic expansion of Wilks' statistic, canonical factor analysis, functional principal component analysis, redundancy analysis, canonical coordinates, and correspondence analysis. The examination of his works shows that interdisciplinary collaboration and the utilization of real datasets were crucial in almost all of Rao's impactful contributions.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186965","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
Cutoff for a class of auto‐regressive models with vanishing additive noise 一类具有消失加性噪声的自动回归模型的截止点
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-08-22 DOI: 10.1111/sjos.12748
Balázs Gerencsér, Andrea Ottolini
{"title":"Cutoff for a class of auto‐regressive models with vanishing additive noise","authors":"Balázs Gerencsér, Andrea Ottolini","doi":"10.1111/sjos.12748","DOIUrl":"https://doi.org/10.1111/sjos.12748","url":null,"abstract":"We analyze the convergence rates for a family of auto‐regressive Markov chains on Euclidean space depending on a parameter , where at each step a randomly chosen coordinate is replaced by a noisy damped weighted average of the others. The interest in the model comes from the connection with a certain Bayesian scheme introduced by de Finetti in the analysis of partially exchangeable data. Our main result shows that, when <jats:italic>n</jats:italic> gets large (corresponding to a vanishing noise), a cutoff phenomenon occurs.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186968","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
Conditional quasi‐likelihood inference for mean residual life regression with clustered failure time data 使用聚类故障时间数据进行平均残余寿命回归的条件准似然推理
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-08-22 DOI: 10.1111/sjos.12746
Rui Huang, Liuquan Sun, Liming Xiang
{"title":"Conditional quasi‐likelihood inference for mean residual life regression with clustered failure time data","authors":"Rui Huang, Liuquan Sun, Liming Xiang","doi":"10.1111/sjos.12746","DOIUrl":"https://doi.org/10.1111/sjos.12746","url":null,"abstract":"In the analysis of clustered failure time data, Cox frailty models have been extensively studied by incorporating frailty with a prespecified distribution to address potential correlation of data within clusters. In this paper, we propose a frailty proportional mean residual life regression model to analyze such data. A novel conditional quasi‐likelihood inference procedure is developed, utilizing a stochastic process and the inverse probability of censoring weighting (IPCW) to form estimating equations for regression parameters. Our proposal employs conditional inference based on a penalized quasi‐likelihood to address within‐cluster correlation without need to specify the frailty distribution, bringing the method closer to what suffices for real‐world applications. By adopting the Buckley–James estimator in the IPCW, the method further allows for dependent censoring. We establish asymptotic properties of the proposed estimator and evaluate its finite sample performance via simulation studies. An application to the data from a multi‐institutional breast cancer study is presented for illustration.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186966","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
Structure learning for continuous time Bayesian networks via penalized likelihood 通过惩罚似然法学习连续时间贝叶斯网络的结构
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-08-22 DOI: 10.1111/sjos.12747
Tomasz Ca̧kała, Błażej Miasojedow, Wojciech Rejchel, Maryia Shpak
{"title":"Structure learning for continuous time Bayesian networks via penalized likelihood","authors":"Tomasz Ca̧kała, Błażej Miasojedow, Wojciech Rejchel, Maryia Shpak","doi":"10.1111/sjos.12747","DOIUrl":"https://doi.org/10.1111/sjos.12747","url":null,"abstract":"Continuous time Bayesian networks (CTBNs) represent a class of stochastic processes, which can be used to model complex phenomena, for instance, they can describe interactions occurring in living processes, social science models or medicine. The literature on this topic is usually focused on a case when a dependence structure of a system is known and we are to determine conditional transition intensities (parameters of a network). In the paper, we study a structure learning problem, which is a more challenging task and the existing research on this topic is limited. The approach, which we propose, is based on a penalized likelihood method. We prove that our algorithm, under mild regularity conditions, recognizes a dependence structure of a graph with high probability. We also investigate properties of the procedure in numerical studies.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142186967","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
Testing for time‐varying nonlinear dependence structures: Regime‐switching and local Gaussian correlation 测试时变非线性依赖结构：时序切换和局部高斯相关性
IF 0.8 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-07-28 DOI: 10.1111/sjos.12744
Kristian Gundersen, Timothée Bacri, J. Bulla, S. Hølleland, A. Maruotti, Bård Støve
{"title":"Testing for time‐varying nonlinear dependence structures: Regime‐switching and local Gaussian correlation","authors":"Kristian Gundersen, Timothée Bacri, J. Bulla, S. Hølleland, A. Maruotti, Bård Støve","doi":"10.1111/sjos.12744","DOIUrl":"https://doi.org/10.1111/sjos.12744","url":null,"abstract":"This paper examines nonlinear and time‐varying dependence structures between a pair of stochastic variables, using a novel approach which combines regime‐switching models and local Gaussian correlation (LGC). We propose an LGC‐based bootstrap test for examining whether the dependence structure between two variables is equal across different regimes. We examine this test in a Monte Carlo study, where it shows good level and power properties. We argue that this approach is more intuitive than competing approaches, typically combining regime‐switching models with copula theory. Furthermore, LGC is a semi‐parametric approach, hence avoids any parametric specification of the dependence structure. We illustrate our approach using financial returns from the US–UK stock markets and the US stock and government bond markets, and provide detailed insight into their dependence structures.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141796437","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
Regression‐based network‐flow and inner‐matrix reconstruction 基于回归的网络流和内矩阵重构
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-07-23 DOI: 10.1111/sjos.12742
Michael Lebacher, Göran Kauermann
{"title":"Regression‐based network‐flow and inner‐matrix reconstruction","authors":"Michael Lebacher, Göran Kauermann","doi":"10.1111/sjos.12742","DOIUrl":"https://doi.org/10.1111/sjos.12742","url":null,"abstract":"Network or matrix reconstruction is a general problem that occurs if the row‐ and column sums of a matrix are given, and the matrix entries need to be predicted conditional on the aggregated information. In this paper, we show that the predictions obtained from the iterative proportional fitting procedure (IPFP) or equivalently maximum entropy (ME) can be obtained by restricted maximum likelihood estimation relying on augmented Lagrangian optimization. Based on this equivalence, we extend the framework of network reconstruction, conditional on row and column sums, toward regression, which allows the inclusion of exogenous covariates and bootstrap‐based uncertainty quantification. More specifically, the mean of the regression model leads to the observed row and column margins. To exemplify the approach, we provide a simulation study and investigate interbank lending data, provided by the Bank for International Settlement. This dataset provides full knowledge of the real network and is, therefore, suitable to evaluate the predictions of our approach. It is shown that the inclusion of exogenous information leads to superior predictions in terms of and errors.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141780377","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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