发布求助
文献互助 智能选刊 最新文献

Scandinavian Journal of Statistics最新文献

筛选
英文 中文
Nonparametric estimation of densities on the hypersphere using a parametric guide 使用参数指南对超球面上的密度进行非参数估计
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-07-03 DOI: 10.1111/sjos.12737
María Alonso‐Pena, Gerda Claeskens, Irène Gijbels
{"title":"Nonparametric estimation of densities on the hypersphere using a parametric guide","authors":"María Alonso‐Pena, Gerda Claeskens, Irène Gijbels","doi":"10.1111/sjos.12737","DOIUrl":"https://doi.org/10.1111/sjos.12737","url":null,"abstract":"Hyperspherical kernel density estimators (KDE), which use a parametric distribution as a guide, are studied in this paper. The main benefit is that these estimators improve the bias of nonguided kernel density estimators when the parametric guiding distribution is not too far from the true density, while preserving the variance. When using a von Mises‐Fisher density as guide, the proposal performs as well as the classical KDE, even when the guiding model is incorrect, and far from the true distribution. This benefit is particular for the hyperspherical setting given its compact support, and is in contrast to similar methods for real valued data. Moreover, we deal with the important issue of data‐driven selection of the smoothing parameter. Simulations and real data examples illustrate the finite‐sample performance of the proposed method, also in comparison with other recently proposed estimation methods.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"24 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548569","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
Lancaster correlation: A new dependence measure linked to maximum correlation 兰开斯特相关性：与最大相关性相关联的一种新的依赖性测量方法
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-07-03 DOI: 10.1111/sjos.12733
Hajo Holzmann, Bernhard Klar
{"title":"Lancaster correlation: A new dependence measure linked to maximum correlation","authors":"Hajo Holzmann, Bernhard Klar","doi":"10.1111/sjos.12733","DOIUrl":"https://doi.org/10.1111/sjos.12733","url":null,"abstract":"We suggest novel correlation coefficients which equal the maximum correlation for a class of bivariate Lancaster distributions while being only slightly smaller than maximum correlation for a variety of further bivariate distributions. In contrast to maximum correlation, however, our correlation coefficients allow for rank and moment‐based estimators which are simple to compute and have tractable asymptotic distributions. Confidence intervals resulting from these asymptotic approximations and the covariance bootstrap show good finite‐sample coverage. In a simulation, the power of asymptotic as well as permutation tests for independence based on our correlation measures compares favorably with competing methods based on distance correlation or rank coefficients for functional dependence, among others. Moreover, for the bivariate normal distribution, our correlation coefficients equal the absolute value of the Pearson correlation, an attractive feature for practitioners which is not shared by various competitors. We illustrate the practical usefulness of our methods in applications to two real data sets.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"56 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548568","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
Validation of point process predictions with proper scoring rules 用适当的评分规则验证点流程预测
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2024-07-02 DOI: 10.1111/sjos.12736
Claudio Heinrich‐Mertsching, Thordis L. Thorarinsdottir, Peter Guttorp, Max Schneider
{"title":"Validation of point process predictions with proper scoring rules","authors":"Claudio Heinrich‐Mertsching, Thordis L. Thorarinsdottir, Peter Guttorp, Max Schneider","doi":"10.1111/sjos.12736","DOIUrl":"https://doi.org/10.1111/sjos.12736","url":null,"abstract":"We introduce a class of proper scoring rules for evaluating spatial point process forecasts based on summary statistics. These scoring rules rely on Monte Carlo approximations of expectations and can therefore easily be evaluated for any point process model that can be simulated. In this regard, they are more flexible than the commonly used logarithmic score and other existing proper scores for point process predictions. The scoring rules allow for evaluating the calibration of a model to specific aspects of a point process, such as its spatial distribution or tendency toward clustering. Using simulations, we analyze the sensitivity of our scoring rules to different aspects of the forecasts and compare it to the logarithmic score. Applications to earthquake occurrences in northern California, United States and the spatial distribution of Pacific silver firs in Findley Lake Reserve in Washington highlight the usefulness of our scores for scientific model selection.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"57 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141504887","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
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":"27 1","pages":""},"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":"6 1","pages":""},"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
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":"209 1","pages":""},"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":"2016 1","pages":""},"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":"36 1","pages":""},"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":"105 1","pages":""},"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":"101 1","pages":""},"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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书