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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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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
Multiply robust matching estimators of average and quantile treatment effects. 平均治疗效果和量化治疗效果的乘法稳健匹配估计值。
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-03-01 Epub Date: 2022-03-13 DOI: 10.1111/sjos.12585
Shu Yang, Yunshu Zhang
{"title":"Multiply robust matching estimators of average and quantile treatment effects.","authors":"Shu Yang, Yunshu Zhang","doi":"10.1111/sjos.12585","DOIUrl":"10.1111/sjos.12585","url":null,"abstract":"<p><p>Propensity score matching has been a long-standing tradition for handling confounding in causal inference, however requiring stringent model assumptions. In this article, we propose novel double score matching (DSM) utilizing both the propensity score and prognostic score. To gain the protection of possible model misspecification, we posit multiple candidate models for each score. We show that the de-biasing DSM estimator achieves the multiple robustness property in that it is consistent if any one of the score models is correctly specified. We characterize the asymptotic distribution for the DSM estimator requiring only one correct model specification based on the martingale representations of the matching estimators and theory for local Normal experiments. We also provide a two-stage replication method for variance estimation and extend DSM for quantile estimation. Simulation demonstrates DSM outperforms single score matching and prevailing multiply robust weighting estimators in the presence of extreme propensity scores.</p>","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949738/pdf/nihms-1780199.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10860145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Nonparametric bounds for the survivor function under general dependent truncation. 一般依赖截断条件下存活函数的非参数边界。
IF 1 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-03-01 Epub Date: 2022-03-07 DOI: 10.1111/sjos.12582
Jing Qian, Rebecca A Betensky
{"title":"Nonparametric bounds for the survivor function under general dependent truncation.","authors":"Jing Qian, Rebecca A Betensky","doi":"10.1111/sjos.12582","DOIUrl":"10.1111/sjos.12582","url":null,"abstract":"<p><p>Truncation occurs in cohort studies with complex sampling schemes. When truncation is ignored or incorrectly assumed to be independent of the event time in the observable region, bias can result. We derive completely nonparametric bounds for the survivor function under truncation and censoring; these extend prior nonparametric bounds derived in the absence of truncation. We also define a hazard ratio function that links the unobservable region in which event time is less than truncation time, to the observable region in which event time is greater than truncation time, under dependent truncation. When this function can be bounded, and the probability of truncation is known approximately, it yields narrower bounds than the purely nonparametric bounds. Importantly, our approach targets the true marginal survivor function over its entire support, and is not restricted to the observable region, unlike alternative estimators. We evaluate the methods in simulations and in clinical applications.</p>","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181817/pdf/nihms-1869785.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9467555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Minimax Powerful Functional Analysis of Covariance Tests: with Application to Longitudinal Genome-Wide Association Studies. 协方差检验的Minimax强大函数分析及其在纵向全基因组关联研究中的应用
IF 0.8 4区 数学
Scandinavian Journal of Statistics Pub Date : 2023-03-01 Epub Date: 2022-03-13 DOI: 10.1111/sjos.12583
Weicheng Zhu, Sheng Xu, Catherine Liu, Yehua Li
{"title":"Minimax Powerful Functional Analysis of Covariance Tests: with Application to Longitudinal Genome-Wide Association Studies.","authors":"Weicheng Zhu, Sheng Xu, Catherine Liu, Yehua Li","doi":"10.1111/sjos.12583","DOIUrl":"10.1111/sjos.12583","url":null,"abstract":"<p><p>We model the Alzheimer's Disease-related phenotype response variables observed on irregular time points in longitudinal Genome-Wide Association Studies as sparse functional data and propose nonparametric test procedures to detect functional genotype effects while controlling the confounding effects of environmental covariates. Our new functional analysis of covariance tests are based on a seemingly unrelated kernel smoother, which takes into account the within-subject temporal correlations, and thus enjoy improved power over existing functional tests. We show that the proposed test combined with a uniformly consistent nonparametric covariance function estimator enjoys the Wilks phenomenon and is minimax most powerful. Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, where an application of the proposed test lead to the discovery of new genes that may be related to Alzheimer's Disease.</p>","PeriodicalId":49567,"journal":{"name":"Scandinavian Journal of Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11286231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43836122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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
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