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Targeted estimation of state occupation probabilities for the non‐Markov illness‐death model 非马尔可夫疾病-死亡模型的状态职业概率的目标估计
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-03-30 DOI: 10.1111/sjos.12644
Anders Munch, Marie Skov Breum, T. Martinussen, T. Gerds
{"title":"Targeted estimation of state occupation probabilities for the non‐Markov illness‐death model","authors":"Anders Munch, Marie Skov Breum, T. Martinussen, T. Gerds","doi":"10.1111/sjos.12644","DOIUrl":"https://doi.org/10.1111/sjos.12644","url":null,"abstract":"We use semi‐parametric efficiency theory to derive a class of estimators for the state occupation probabilities of the continuous‐time irreversible illness‐death model. We consider both the setting with and without additional baseline information available, where we impose no specific functional form on the intensity functions of the model. We show that any estimator in the class is asymptotically linear under suitable assumptions about the estimators of the intensity functions. In particular, the assumptions are weak enough to allow the use of data‐adaptive methods, which is important for making the identifying assumption of coarsening at random plausible in realistic settings. We suggest a flexible method for estimating the transition intensity functions of the illness‐death model based on penalized Poisson regression. We apply this method to estimate the nuisance parameters of an illness‐death model in a simulation study and a real‐world application.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"1532 - 1551"},"PeriodicalIF":1.0,"publicationDate":"2023-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47243862","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 Historical Overview of Textbook Presentations of Statistical Science 统计科学教材介绍的历史考察
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-03-27 DOI: 10.1111/sjos.12641
A. Agresti
{"title":"A Historical Overview of Textbook Presentations of Statistical Science","authors":"A. Agresti","doi":"10.1111/sjos.12641","DOIUrl":"https://doi.org/10.1111/sjos.12641","url":null,"abstract":"We discuss the evolution in the presentation of statistical science in English‐language textbooks, focusing on the period 1900–1970 as the field became increasingly influenced by research contributions of R. A. Fisher and Jerzy Neyman. George Udny Yule authored an early popular book that had 14 editions. Methods books authored by Fisher and George Snedecor guided scientists in implementing modern statistical methods. In the World War 2 era, textbooks authored by Maurice Kendall, Samuel Wilks, and Harald Cramér presented a dramatically different “mathematical statistics” portrayal that centered on theoretical foundations. The textbook emergence of the Bayesian approach occurred later, influenced by books by Harold Jeffreys and Leonard J. Savage. The quarter century after World War 2 saw an explosion of books in mathematical statistics and in particular topic areas. In addition to his highly cited research contributions, Sir David Cox was a prolific author of books on a great variety of topics. Most were published after the 1900–1970 period considered in this article, but we also summarize them as part of this special issue to honor his memory. We conclude by discussing the future of textbooks on the foundations of statistical science in the emerging, ever‐broader, era of data science.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45893190","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: 2
Daisee: Adaptive importance sampling by balancing exploration and exploitation Daisee：通过平衡探索和开发进行适应性重要性抽样
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-03-23 DOI: 10.1111/sjos.12637
Xiaoyu Lu, Tom Rainforth, Y. Teh
{"title":"Daisee: Adaptive importance sampling by balancing exploration and exploitation","authors":"Xiaoyu Lu, Tom Rainforth, Y. Teh","doi":"10.1111/sjos.12637","DOIUrl":"https://doi.org/10.1111/sjos.12637","url":null,"abstract":"We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade‐off between exploration and exploitation in this adaptation. Borrowing ideas from the online learning literature, we propose Daisee, a partition‐based AIS algorithm. We further introduce a notion of regret for AIS and show that Daisee has 𝒪(T(logT)34) cumulative pseudo‐regret, where T$$ T $$ is the number of iterations. We then extend Daisee to adaptively learn a hierarchical partitioning of the sample space for more efficient sampling and confirm the performance of both algorithms empirically.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"1298 - 1324"},"PeriodicalIF":1.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47118543","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
Robust Inference for High‐Dimensional Single Index Models 高维单指标模型的鲁棒推断
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-03-13 DOI: 10.1111/sjos.12638
Dongxiao Han, Miao Han, Jian Huang, Yuanyuan Lin
{"title":"Robust Inference for\u0000 High‐Dimensional\u0000 Single Index Models","authors":"Dongxiao Han, Miao Han, Jian Huang, Yuanyuan Lin","doi":"10.1111/sjos.12638","DOIUrl":"https://doi.org/10.1111/sjos.12638","url":null,"abstract":"We propose a robust inference method for high‐dimensional single index models with an unknown link function and elliptically symmetrically distributed covariates, focusing on signal recovery and inference. The proposed method is built on the Huber loss and the estimation of the unknown link function is avoided. The ℓ1$$ {ell}_1 $$ and ℓ2$$ {ell}_2 $$ consistency of a Lasso estimator up to a multiplicative scalar is established. When the covariance matrix of the predictors satisfies the irrepresentable condition, our method is shown to recover the signed support of the true parameter under mild conditions. Based on a debiased Lasso estimator, we study component‐wise and group inference for the high‐dimensional index parameter. The finite‐sample performance of our method is evaluated through extensive simulation studies. An application to a riboflavin production dataset is provided to illustrate the proposed method.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43396597","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
Remove unwanted variation retrieves unknown experimental designs 去除不需要的变异，检索未知的实验设计
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-03-01 DOI: 10.1111/sjos.12633
Ingrid Lönnstedt, T. Speed
{"title":"Remove unwanted variation retrieves unknown experimental designs","authors":"Ingrid Lönnstedt, T. Speed","doi":"10.1111/sjos.12633","DOIUrl":"https://doi.org/10.1111/sjos.12633","url":null,"abstract":"Remove unwanted variation (RUV) is an estimation and normalization system in which the underlying correlation structure of a multivariate dataset is estimated from negative control measurements, typically gene expression values, which are assumed to stay constant across experimental conditions. In this paper we derive the weight matrix which is estimated and incorporated into the generalized least squares estimates of RUV‐inverse, and show that this weight matrix estimates the average covariance matrix across negative control measurements. RUV‐inverse can thus be viewed as an estimation method adjusting for an unknown experimental design. We show that for a balanced incomplete block design (BIBD), RUV‐inverse recovers intra‐ and interblock estimates of the relevant parameters and combines them as a weighted sum just like the best linear unbiased estimator (BLUE), except that the weights are globally estimated from the negative control measurements instead of being individually optimized to each measurement as in the classical, single measurement BIBD BLUE.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"101 - 89"},"PeriodicalIF":1.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42340017","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
Finite sample inference for empirical Bayesian methods 经验贝叶斯方法的有限样本推理
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-02-28 DOI: 10.1111/sjos.12643
H. Nguyen, Mayetri Gupta
{"title":"Finite sample inference for empirical Bayesian methods","authors":"H. Nguyen, Mayetri Gupta","doi":"10.1111/sjos.12643","DOIUrl":"https://doi.org/10.1111/sjos.12643","url":null,"abstract":"In recent years, empirical Bayesian (EB) inference has become an attractive approach for estimation in parametric models arising in a variety of real-life problems, especially in complex and high-dimensional scientific applications. However, compared to the relative abundance of available general methods for computing point estimators in the EB framework, the construction of confidence sets and hypothesis tests with good theoretical properties remains difficult and problem specific. Motivated by the universal inference framework of Wasserman et al. (2020), we propose a general and universal method, based on holdout likelihood ratios, and utilizing the hierarchical structure of the specified Bayesian model for constructing confidence sets and hypothesis tests that are finite sample valid. We illustrate our method through a range of numerical studies and real data applications, which demonstrate that the approach is able to generate useful and meaningful inferential statements in the relevant contexts.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44494191","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
Professor Elja Arjas: A prominent figure in establishing statistics in Finland Elja Arjas教授:在芬兰建立统计学的杰出人物
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-02-21 DOI: 10.1111/sjos.12631
S. Kulathinal, Jaakko Peltonen, M. Sillanpää
{"title":"Professor Elja Arjas: A prominent figure in establishing statistics in Finland","authors":"S. Kulathinal, Jaakko Peltonen, M. Sillanpää","doi":"10.1111/sjos.12631","DOIUrl":"https://doi.org/10.1111/sjos.12631","url":null,"abstract":"","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48459555","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
Frequentist model averaging for envelope models 包络模型的频域模型平均
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-01-27 DOI: 10.1111/sjos.12634
Ziwen Gao, Jiahui Zou, Xinyu Zhang, Yanyuan Ma
{"title":"Frequentist model averaging for envelope models","authors":"Ziwen Gao, Jiahui Zou, Xinyu Zhang, Yanyuan Ma","doi":"10.1111/sjos.12634","DOIUrl":"https://doi.org/10.1111/sjos.12634","url":null,"abstract":"The envelope method produces efficient estimation in multivariate linear regression, and is widely applied in biology, psychology, and economics. This paper estimates parameters through a model averaging methodology and promotes the predicting abilities of the envelope models. We propose a frequentist model averaging method by minimizing a cross‐validation criterion. When all the candidate models are misspecified, the proposed model averaging estimator is proved to be asymptotically optimal. When correct candidate models exist, the coefficient estimator is proved to be consistent, and the sum of the weights assigned to the correct models, in probability, converges to one. Simulations and an empirical application demonstrate the effectiveness of the proposed method.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"1325 - 1364"},"PeriodicalIF":1.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45126662","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: 1
Variable selection for high‐dimensional generalized linear model with block‐missing data 具有块缺失数据的高维广义线性模型的变量选择
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-01-23 DOI: 10.1111/sjos.12632
Yifan He, Yang Feng, Xinyuan Song
{"title":"Variable selection for high‐dimensional generalized linear model with block‐missing data","authors":"Yifan He, Yang Feng, Xinyuan Song","doi":"10.1111/sjos.12632","DOIUrl":"https://doi.org/10.1111/sjos.12632","url":null,"abstract":"In modern scientific research, multiblock missing data emerges with synthesizing information across multiple studies. However, existing imputation methods for handling block‐wise missing data either focus on the single‐block missing pattern or heavily rely on the model structure. In this study, we propose a single regression‐based imputation algorithm for multiblock missing data. First, we conduct a sparse precision matrix estimation based on the structure of block‐wise missing data. Second, we impute the missing blocks with their means conditional on the observed blocks. Theoretical results about variable selection and estimation consistency are established in the context of a generalized linear model. Moreover, simulation studies show that compared with existing methods, the proposed imputation procedure is robust to various missing mechanisms because of the good properties of regression imputation. An application to Alzheimer's Disease Neuroimaging Initiative data also confirms the superiority of our proposed method.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"1279 - 1297"},"PeriodicalIF":1.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43310881","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
Use of multiple imputation in supersampled nested case‐control and case‐cohort studies 在超样本嵌套病例对照和病例队列研究中使用多重输入
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2022-12-20 DOI: 10.1111/sjos.12624
Ørnulf Borgan, R. Keogh, A. Njøs
{"title":"Use of multiple imputation in supersampled nested case‐control and case‐cohort studies","authors":"Ørnulf Borgan, R. Keogh, A. Njøs","doi":"10.1111/sjos.12624","DOIUrl":"https://doi.org/10.1111/sjos.12624","url":null,"abstract":"Nested case‐control and case‐cohort studies are useful for studying associations between covariates and time‐to‐event when some covariates are expensive to measure. Full covariate information is collected in the nested case‐control or case‐cohort sample only, while cheaply measured covariates are often observed for the full cohort. Standard analysis of such case‐control samples ignores any full cohort data. Previous work has shown how data for the full cohort can be used efficiently by multiple imputation of the expensive covariate(s), followed by a full‐cohort analysis. For large cohorts this is computationally expensive or even infeasible. An alternative is to supplement the case‐control samples with additional controls on which cheaply measured covariates are observed. We show how multiple imputation can be used for analysis of such supersampled data. Simulations show that this brings efficiency gains relative to a traditional analysis and that the efficiency loss relative to using the full cohort data is not substantial.","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":"50 1","pages":"13 - 37"},"PeriodicalIF":1.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42358767","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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