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Scandinavian Journal of Statistics最新文献

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Inference for all variants of the multivariate coefficient of variation in factorial designs 因子设计中多元变异系数所有变量的推理
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-06-26 DOI: 10.1111/sjos.12740
Marc Ditzhaus, Łukasz Smaga
{"title":"Inference for all variants of the multivariate coefficient of variation in factorial designs","authors":"Marc Ditzhaus, Łukasz Smaga","doi":"10.1111/sjos.12740","DOIUrl":"https://doi.org/10.1111/sjos.12740","url":null,"abstract":"The multivariate coefficient of variation (MCV) is an attractive and easy‐to‐interpret effect size for the dispersion in multivariate data. Recently, the first inference methods for the MCV were proposed for general factorial designs. However, the inference methods are primarily derived for one special MCV variant while there are several reasonable proposals. Moreover, when rejecting a global null hypothesis, a more in‐depth analysis is of interest to find the significant contrasts of MCV. This paper concerns extending the nonparametric permutation procedure to the other MCV variants and a max‐type test for post hoc analysis. To improve the small sample performance of the latter, we suggest a novel bootstrap strategy and prove its asymptotic validity. The actual performance of all proposed tests is compared in an extensive simulation study and illustrated by real data analysis. All methods are implemented in the R package GFDmcv, available on CRAN.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504888","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 conversation with Nils Lid Hjort 与尼尔斯-利德-希约尔特的对话
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-06-20 DOI: 10.1111/sjos.12732
Ørnulf Borgan, Ingrid K. Glad
{"title":"A conversation with Nils Lid Hjort","authors":"Ørnulf Borgan, Ingrid K. Glad","doi":"10.1111/sjos.12732","DOIUrl":"https://doi.org/10.1111/sjos.12732","url":null,"abstract":"Professor (now emeritus) Nils Lid Hjort has through more than four decades been one of the most original and productive statisticians in Norway, contributing to a wide range of topics such as survival analysis, Bayesian nonparametrics, empirical likelihood, density estimation, focused inference, model selection, and confidence distributions. This conversation, which took place at the University of Oslo in December 2023, sheds light on how Nils Hjort's curious and open mind, coupled with a deep understanding, has enabled him to seamlessly navigate between different fields of statistics and its applications. Our aim is to encourage the statistics community to always be on the lookout for unexpected connections in statistical science and to embrace unexpected encounters with fellow statisticians from around the world.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504889","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
Double debiased transfer learning for adaptive Huber regression 自适应胡贝尔回归的双偏移转移学习
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-05-21 DOI: 10.1111/sjos.12723
Ziyuan Wang, Lei Wang, Heng Lian
{"title":"Double debiased transfer learning for adaptive Huber regression","authors":"Ziyuan Wang, Lei Wang, Heng Lian","doi":"10.1111/sjos.12723","DOIUrl":"https://doi.org/10.1111/sjos.12723","url":null,"abstract":"Through exploiting information from the source data to improve the fit performance on the target data, transfer learning estimations for high‐dimensional linear regression models have drawn much attention recently, but few studies focus on statistical inference and robust learning in the presence of heavy‐tailed/asymmetric errors. Using adaptive Huber regression (AHR) to achieve the bias and robustness tradeoff, in this paper we propose a robust transfer learning algorithm with high‐dimensional covariates, then construct valid confidence intervals and hypothesis tests based on the debiased lasso approach. When the transferable sources are known, a two‐step ‐penalized transfer AHR estimator is firstly proposed and the error bounds are established. To correct the biases caused by the lasso penalty, a unified debiasing framework based on the decorrelated score equations is considered to establish asymptotic normality of the debiased lasso transfer AHR estimator. Confidence intervals and hypothesis tests for each component can be constructed. When the transferable sources are unknown, a data‐driven source detection algorithm is proposed with theoretical guarantee. Numerical studies verify the performance of our proposed estimator and confidence intervals, and an application to Genotype‐Tissue Expression data is also presented.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115316","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
Gradient‐based approach to sufficient dimension reduction with functional or longitudinal covariates 基于梯度的方法，利用功能或纵向协变量充分降维
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-05-19 DOI: 10.1111/sjos.12724
Ming-Yueh Huang, Kwun Chuen Gary Chan
{"title":"Gradient‐based approach to sufficient dimension reduction with functional or longitudinal covariates","authors":"Ming-Yueh Huang, Kwun Chuen Gary Chan","doi":"10.1111/sjos.12724","DOIUrl":"https://doi.org/10.1111/sjos.12724","url":null,"abstract":"In this paper, we focus on the sufficient dimension reduction problem in regression analysis with real‐valued response and functional or longitudinal covariates. We propose a new method based on gradients of the conditional distribution function to estimate the sufficient dimension reduction subspace. While existing inverse‐regression‐type methods relies on a linearity condition, our method is based on the gradient of conditional distribution function and its validity only requires smoothness conditions on the population parameters. Practically, the proposed estimator can be obtained by standard algorithm of functional principal component analysis. The proposed method is demonstrated through extensive simulations and two empirical examples.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124124","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
Semiparametric efficient estimation in high‐dimensional partial linear regression models 高维偏线性回归模型中的半参数高效估计
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-05-15 DOI: 10.1111/sjos.12716
Xinyu Fu, Mian Huang, Weixin Yao
{"title":"Semiparametric efficient estimation in high‐dimensional partial linear regression models","authors":"Xinyu Fu, Mian Huang, Weixin Yao","doi":"10.1111/sjos.12716","DOIUrl":"https://doi.org/10.1111/sjos.12716","url":null,"abstract":"We introduce a novel semiparametric efficient estimation procedure for high‐dimensional partial linear regression models to overcome the challenge of efficiency loss of the traditional least‐squares based estimation procedure under unknown error distributions, while enjoying several appealing theoretical properties. The new estimation procedure provides a sparse estimator for the parametric component and achieves the semiparametric efficiency as the oracle maximum likelihood estimator as if the error distribution was known. By employing the penalized estimation and the semiparametric efficiency theory for ultra‐high‐dimensional partial linear model, the procedure enjoys the oracle variable selection property and offers efficiency gain for non‐Gaussian random errors, while maintaining the same efficiency as the least squares‐based estimator for Gaussian random errors. Extensive simulation studies and an empirical application are conducted to demonstrate the effectiveness of the proposed procedure.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140975672","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
Cox processes driven by transformed Gaussian processes on linear networks—A review and new contributions 线性网络上由变换高斯过程驱动的考克斯过程--回顾与新贡献
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-05-14 DOI: 10.1111/sjos.12720
Jesper Møller, Jakob G. Rasmussen
{"title":"Cox processes driven by transformed Gaussian processes on linear networks—A review and new contributions","authors":"Jesper Møller, Jakob G. Rasmussen","doi":"10.1111/sjos.12720","DOIUrl":"https://doi.org/10.1111/sjos.12720","url":null,"abstract":"There is a lack of point process models on linear networks. For an arbitrary linear network, we consider new models for a Cox process with an isotropic pair correlation function obtained in various ways by transforming an isotropic Gaussian process which is used for driving the random intensity function of the Cox process. In particular, we introduce three model classes given by log Gaussian, interrupted, and permanental Cox processes on linear networks, and consider for the first time statistical procedures and applications for parametric families of such models. Moreover, we construct new simulation algorithms for Gaussian processes on linear networks and discuss whether the geodesic metric or the resistance metric should be used for the kind of Cox processes studied in this paper.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062342","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 maximizing the likelihood function of general geostatistical models 论一般地质统计模型似然函数的最大化
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-05-07 DOI: 10.1111/sjos.12722
Tingjin Chu
{"title":"On maximizing the likelihood function of general geostatistical models","authors":"Tingjin Chu","doi":"10.1111/sjos.12722","DOIUrl":"https://doi.org/10.1111/sjos.12722","url":null,"abstract":"General geostatistical models are powerful tools for analyzing spatial datasets. A two‐step estimation based on the likelihood function is widely used by researchers, but several theoretical and computational challenges remain to be addressed. First, it is unclear whether there is a unique global maximizer of the log‐likelihood function, a seemingly simple but theoretically challenging question. The second challenge is the convexity of the log‐likelihood function. Besides these two challenges in maximizing the likelihood function, we also study the theoretical property of the two‐step estimation. Unlike many previous works, our results can apply to the non‐twice differentiable covariance functions. In the simulation studies, three optimization algorithms are evaluated in terms of maximizing the log‐likelihood functions.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942490","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
Mahalanobis balancing: A multivariate perspective on approximate covariate balancing 马哈拉诺比斯平衡：近似协变量平衡的多变量视角
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-04-26 DOI: 10.1111/sjos.12721
Yimin Dai, Ying Yan
{"title":"Mahalanobis balancing: A multivariate perspective on approximate covariate balancing","authors":"Yimin Dai, Ying Yan","doi":"10.1111/sjos.12721","DOIUrl":"https://doi.org/10.1111/sjos.12721","url":null,"abstract":"In the past decade, various exact balancing‐based weighting methods were introduced to the causal inference literature. It eliminates covariate imbalance by imposing balancing constraints in a certain optimization problem, which can nevertheless be infeasible when there is bad overlap between the covariate distributions in the treated and control groups or when the covariates are high dimensional. Recently, approximate balancing was proposed as an alternative balancing framework. It resolves the feasibility issue by using inequality moment constraints instead. However, it can be difficult to select the threshold parameters. Moreover, moment constraints may not fully capture the discrepancy of covariate distributions. In this paper, we propose Mahalanobis balancing to approximately balance covariate distributions from a multivariate perspective. We use a quadratic constraint to control overall imbalance with a single threshold parameter, which can be tuned by a simple selection procedure. We show that the dual problem of Mahalanobis balancing is an norm‐based regularized regression problem, and establish interesting connection to propensity score models. We derive asymptotic properties, discuss the high‐dimensional scenario, and make extensive numerical comparisons with existing balancing methods.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140801664","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
Asymptotic properties of resampling‐based processes for the average treatment effect in observational studies with competing risks 具有竞争风险的观察性研究中基于重采样过程的平均治疗效果的渐近特性
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-04-25 DOI: 10.1111/sjos.12714
Jasmin Rühl, Sarah Friedrich
{"title":"Asymptotic properties of resampling‐based processes for the average treatment effect in observational studies with competing risks","authors":"Jasmin Rühl, Sarah Friedrich","doi":"10.1111/sjos.12714","DOIUrl":"https://doi.org/10.1111/sjos.12714","url":null,"abstract":"In observational studies with time‐to‐event outcomes, the g‐formula can be used to estimate a treatment effect in the presence of confounding factors. However, the asymptotic distribution of the corresponding stochastic process is complicated and thus not suitable for deriving confidence intervals or time‐simultaneous confidence bands for the average treatment effect. A common remedy are resampling‐based approximations, with Efron's nonparametric bootstrap being the standard tool in practice. We investigate the large sample properties of three different resampling approaches and prove their asymptotic validity in a setting with time‐to‐event data subject to competing risks. The usage of these approaches is demonstrated by an analysis of the effect of physical activity on the risk of knee replacement among patients with advanced knee osteoarthritis.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140801709","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
Minimax estimation of functional principal components from noisy discretized functional data 从噪声离散函数数据中最小估计函数主成分
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-04-24 DOI: 10.1111/sjos.12719
Ryad Belhakem, Franck Picard, Vincent Rivoirard, Angelina Roche
{"title":"Minimax estimation of functional principal components from noisy discretized functional data","authors":"Ryad Belhakem, Franck Picard, Vincent Rivoirard, Angelina Roche","doi":"10.1111/sjos.12719","DOIUrl":"https://doi.org/10.1111/sjos.12719","url":null,"abstract":"Functional Principal Component Analysis is a reference method for dimension reduction of curve data. Its theoretical properties are now well understood in the simplified case where the sample curves are fully observed without noise. However, functional data are noisy and necessarily observed on a finite discretization grid. Common practice consists in smoothing the data and then to compute the functional estimates, but the impact of this denoising step on the procedure's statistical performance are rarely considered. Here we prove new convergence rates for functional principal component estimators. We introduce a double asymptotic framework: one corresponding to the sampling size and a second to the size of the grid. We prove that estimates based on projection onto histograms show optimal rates in a minimax sense. Theoretical results are illustrated on simulated data and the method is applied to the visualization of genomic data.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140801711","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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