Statistics Surveys最新文献_第2页

Statistical inference for stationary linear models with tapered data 具有锥形数据的平稳线性模型的统计推断

IF 3.3

Statistics Surveys Pub Date : 2021-01-01 DOI: 10.1214/21-ss134

M. Ginovyan, A. A. Sahakyan

引用次数: 0

Flexible, boundary adapted, nonparametric methods for the estimation of univariate piecewise-smooth functions 单变量分段光滑函数估计的柔性、边界自适应非参数方法

IF 3.3

Statistics Surveys Pub Date : 2020-01-01 DOI: 10.1214/20-ss128

U. Amato, A. Antoniadis, I. Feis

{"title":"Flexible, boundary adapted, nonparametric methods for the estimation of univariate piecewise-smooth functions","authors":"U. Amato, A. Antoniadis, I. Feis","doi":"10.1214/20-ss128","DOIUrl":"https://doi.org/10.1214/20-ss128","url":null,"abstract":": We present and compare some nonparametric estimation meth- ods (wavelet and/or spline-based) designed to recover a one-dimensional piecewise-smooth regression function in both a ﬁxed equidistant or not equidistant design regression model and a random design model. Wavelet methods are known to be very competitive in terms of denois- ing and compression, due to the simultaneous localization property of a function in time and frequency. However, boundary assumptions, such as periodicity or symmetry, generate bias and artiﬁcial wiggles which degrade overall accuracy. Simple methods have been proposed in the literature for reducing the bias at the boundaries. We introduce new ones based on adaptive combinations of two estimators. The underlying idea is to combine a highly accurate method for non-regular functions, e.g., wavelets, with one well behaved at boundaries, e.g., Splines or Local Polynomial. We provide some asymptotic optimal results supporting our approach. All the methods can handle data with a random design. We also sketch some generalization to the multidimensional setting. the performance of the proposed approaches we have an extensive set of simulations on synthetic data. An interesting regression analysis of two real data applications using these procedures unambiguously demonstrates their eﬀectiveness.","PeriodicalId":46627,"journal":{"name":"Statistics Surveys","volume":"25 6 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79741865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Can $p$-values be meaningfully interpreted without random sampling? $p$值可以在没有随机抽样的情况下进行有意义的解释吗?

IF 3.3

Statistics Surveys Pub Date : 2019-08-16 DOI: 10.31235/osf.io/yazr8

N. Hirschauer, Sven Gruener, O. Musshoff, C. Becker, Antje Jantsch

引用次数: 23

Additive monotone regression in high and lower dimensions 高维和低维的加性单调回归

IF 3.3

Statistics Surveys Pub Date : 2019-01-01 DOI: 10.1214/19-SS124

S. Engebretsen, I. Glad

引用次数: 2

Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists. 从密集收集的纵向数据预测远端结果的标量函数回归：应用科学家的可解释性。

IF 11

Statistics Surveys Pub Date : 2019-01-01 Epub Date: 2019-11-06 DOI: 10.1214/19-SS126

John J Dziak, Donna L Coffman, Matthew Reimherr, Justin Petrovich, Runze Li, Saul Shiffman, Mariya P Shiyko

引用次数: 0

PLS for Big Data: A unified parallel algorithm for regularised group PLS 面向大数据的PLS:正则化群PLS的统一并行算法

IF 3.3

Statistics Surveys Pub Date : 2019-01-01 DOI: 10.1214/19-ss125

P. L. D. Micheaux, B. Liquet, Matthew Sutton

{"title":"PLS for Big Data: A unified parallel algorithm for regularised group PLS","authors":"P. L. D. Micheaux, B. Liquet, Matthew Sutton","doi":"10.1214/19-ss125","DOIUrl":"https://doi.org/10.1214/19-ss125","url":null,"abstract":"Partial Least Squares (PLS) methods have been heavily exploited to analyse the association between two blocks of data. These powerful approaches can be applied to data sets where the number of variables is greater than the number of observations and in the presence of high collinearity between variables. Different sparse versions of PLS have been developed to integrate multiple data sets while simultaneously selecting the contributing variables. Sparse modeling is a key factor in obtaining better estimators and identifying associations between multiple data sets. The cornerstone of the sparse PLS methods is the link between the singular value decomposition (SVD) of a matrix (constructed from deflated versions of the original data) and least squares minimization in linear regression. We review four popular PLS methods for two blocks of data. A unified algorithm is proposed to perform all four types of PLS including their regularised versions. We present various approaches to decrease the computation time and show how the whole procedure can be scalable to big data sets. The bigsgPLS R package implements our unified algorithm and is available at https://github.com/matt-sutton/bigsgPLS. MSC 2010 subject classifications: Primary 6202, 62J99.","PeriodicalId":46627,"journal":{"name":"Statistics Surveys","volume":"148 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75176757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Halfspace depth and floating body 半空间深度和浮体

IF 3.3

Statistics Surveys Pub Date : 2018-09-28 DOI: 10.1214/19-SS123

Stanislav Nagy, C. Schuett, E. Werner

引用次数: 34

A design-sensitive approach to fitting regression models with complex survey data 复杂调查数据回归模型拟合的设计敏感方法

IF 3.3

Statistics Surveys Pub Date : 2018-01-16 DOI: 10.1214/17-SS118

P. Kott

引用次数: 5

A review of dynamic network models with latent variables. 具有潜在变量的动态网络模型综述。

IF 3.3

Statistics Surveys Pub Date : 2018-01-01 Epub Date: 2018-09-03 DOI: 10.1214/18-SS121

Bomin Kim, Kevin H Lee, Lingzhou Xue, Xiaoyue Niu

引用次数: 106

Pitfalls of significance testing and $p$-value variability: An econometrics perspective 显著性检验和p值变异性的陷阱:计量经济学视角

IF 3.3

Statistics Surveys Pub Date : 2018-01-01 DOI: 10.1214/18-SS122

N. Hirschauer, Sven Grüner, O. Musshoff, C. Becker

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引用次数: 10