Statistics Surveys最新文献_第4页

Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain^*† 分析复杂的脑功能网络:融合统计学和网络科学来理解大脑*†

IF 3.3

Statistics Surveys Pub Date : 2013-01-01 DOI: 10.1214/13-SS103

Sean L Simpson, F DuBois Bowman, Paul J Laurienti

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引用次数: 122

Prediction in several conventional contexts 几种常规情况下的预测

IF 3.3

Statistics Surveys Pub Date : 2012-12-01 DOI: 10.1214/12-SS100

B. Clarke, J. Clarke

引用次数: 3

Statistical inference for dynamical systems: A review 动力系统的统计推断:综述

IF 3.3

Statistics Surveys Pub Date : 2012-04-27 DOI: 10.1214/15-SS111

K. Mcgoff, S. Mukherjee, N. Pillai

引用次数: 38

A survey of Bayesian predictive methods for model assessment, selection and comparison 贝叶斯预测方法在模型评估、选择和比较中的应用综述

IF 3.3

Statistics Surveys Pub Date : 2012-01-01 DOI: 10.1214/12-SS102

Aki Vehtari, Janne Ojanen

引用次数: 318

A review of survival trees 生存树的回顾

IF 3.3

Statistics Surveys Pub Date : 2011-08-01 DOI: 10.1214/09-SS047

Imad Bou-Hamad, Denis Larocque, H. Ben-Ameur

引用次数: 179

Curse of dimensionality and related issues in nonparametric functional regression 非参数泛函回归中的维数诅咒及相关问题

IF 3.3

Statistics Surveys Pub Date : 2011-01-01 DOI: 10.1214/09-SS049

G. Geenens

{"title":"Curse of dimensionality and related issues in nonparametric functional regression","authors":"G. Geenens","doi":"10.1214/09-SS049","DOIUrl":"https://doi.org/10.1214/09-SS049","url":null,"abstract":"Recently, some nonparametric regression ideas have been extended to the case of functional regression. Within that framework, the main concern arises from the infinite dimensional nature of the explanatory objects. Specifically, in the classical multivariate regression context, it is well-known that any nonparametric method is affected by the socalled “curse of dimensionality”, caused by the sparsity of data in highdimensional spaces, resulting in a decrease in fastest achievable rates of convergence of regression function estimators toward their target curve as the dimension of the regressor vector increases. Therefore, it is not surprising to find dramatically bad theoretical properties for the nonparametric functional regression estimators, leading many authors to condemn the methodology. Nevertheless, a closer look at the meaning of the functional data under study and on the conclusions that the statistician would like to draw from it allows to consider the problem from another point-of-view, and to justify the use of slightly modified estimators. In most cases, it can be entirely legitimate to measure the proximity between two elements of the infinite dimensional functional space via a semi-metric, which could prevent those estimators suffering from what we will call the “curse of infinite dimensionality”. AMS 2000 subject classifications: Primary 62G08; secondary 62M40.","PeriodicalId":46627,"journal":{"name":"Statistics Surveys","volume":"65 1","pages":"30-43"},"PeriodicalIF":3.3,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89097433","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: 91

Data confidentiality: A review of methods for statistical disclosure limitation and methods for assessing privacy 数据保密:统计披露方法、限制和隐私评估方法综述

IF 3.3

Statistics Surveys Pub Date : 2011-01-01 DOI: 10.1214/11-SS074

Gregory J. Matthews, O. Harel

引用次数: 88

Primal and dual model representations in kernel-based learning 基于核的学习中的原始和对偶模型表示

IF 3.3

Statistics Surveys Pub Date : 2010-01-01 DOI: 10.1214/09-SS052

J. Suykens, C. Alzate, K. Pelckmans

引用次数: 31

Finite mixture models and model-based clustering 有限混合模型和基于模型的聚类