搜索结果: 1-15 共查到“数理统计学 High-dimensional”相关记录17条 . 查询时间(0.125 秒)
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Estimating Time-Varying Networks for High-Dimensional Time Series
高维 时间序列 时变网络
2023/4/25
We explore time-varying networks for high-dimensional locally stationary time series, using the large VAR model framework with both the transition and (error) precision matrices evolving smoothly over...
Academy of Mathematics and Systems Science, CAS Colloquia & Seminars：Fixed Effects Bayesian Testing in High-Dimensional Linear Mixed Models
高维 线性混合模型 固定效应 贝叶斯检验
2023/5/5
Hypothesis testing on high-dimensional fixed effects is indispensable for investigating the utility of the predictors on response. In this case, the conventional frequentist methods designed for cases...
2018高维统计模型的贝叶斯计算研讨会（Workshop on Bayesian Computation for High-Dimensional Statistical Models）
2018 高维统计模型的贝叶斯计算 研讨会
2017/12/20
In recent years there has been an explosion of complex data-sets in areas as diverse as Bioinformatics, Ecology, Epidemiology, Finance, subsurface Geophysics, Meteorology, and Population genetics. In ...
2018年复杂网络高维数据统计挑战研讨会（Meeting the Statistical Challenges in High Dimensional Data and Complex Networks）
2018年 复杂网络高维数据统计挑战 研讨会
2017/11/24
The program aims at showing the role of modern statistical methods in complex data and serves to support interactions among mathematicians, statisticians, engineers and scientists working in the inter...
Regularization methods for high-dimensional instrumental variables regression with an application to genetical genomics
Causal inference Confounding Endogeneity Sparse regression
2016/1/25
In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expres...
Tests atternative to higher criticism for high dimensional means under sparsity and column-wise dependence
Large deviation Large p, small n Optimal detection boundary Sparse signal Thresholding Weak dependence
2016/1/25
We consider two alternative tests to the Higher Criticism test of Donoho and Jin (2004) for high dimensional means under the spar-sity of the non-zero means for sub-Gaussian distributed data with unkn...
High Dimensional Influence Measure
Cook’s distance High dimensional diagnosis Influential obser- vation Lasso
2016/1/25
Influence diagnosis is important since presence of influential ob-servations could lead to distorted analysis and misleading interpreta-tions. For high dimensional data, it is particularly so, as the ...
Regularization methods for high-dimensional instrumental variables regression with an application to genetical genomics
Causal inference Confounding Endogeneity Sparse regression
2016/1/20
In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expres...
Margin trees for high-dimensional classification
Maximum margin classifier supprt vector machine decision tree CART
2015/8/21
We propose a method for the classification of more than two classes, from high-dimensional features. Our approach is to build a binary decision tree in a top-down manner, using the optimal margin clas...
“PRECONDITIONING” FOR FEATURE SELECTION AND REGRESSION IN HIGH-DIMENSIONAL PROBLEMS
Model selection prediction error lasso
2015/8/21
We consider regression problems where the number of predictors greatly exceeds the number of observations. We propose a method for variable selection that first estimates the regression function, yiel...
Two sample tests for high-dimensional covariance matrices
High-dimensional covariance large p small n likelihood ratio test testing for gene-sets
2012/6/21
We propose two tests for the equality of covariance matrices between two high-dimensional populations. One test is on the whole variance--covariance matrices, and the other is on off-diagonal sub-matr...
Estimation in high-dimensional linear models with deterministic design matrices
Identifiability projection ridge regression sparsity thresholding variable selection
2012/6/21
Because of the advance in technologies, modern statistical studies often encounter linear models with the number of explanatory variables much larger than the sample size. Estimation and variable sele...
Orthogonal Matching Pursuit with Noisy and Missing Data: Low and High Dimensional Results
Orthogonal Matching Pursuit Noisy and Missing Data High Dimensional Results Statistics Theory
2012/6/21
Many models for sparse regression typically assume that the covariates are known completely, and without noise. Particularly in high-dimensional applications, this is often not the case. This paper de...
Non-asymptotic Oracle Inequalities for the Lasso and Group Lasso in high dimensional logistic model
Logistic model Lasso Group Lasso High-dimensional
2012/6/19
We consider the problem of estimating a function $f_{0}$ in logistic regression model. We propose to estimate this function $f_{0}$ by a sparse approximation build as a linear combinaison of elements ...
Factor modeling for high-dimensional time series: Inference for the number of factors
Autocovariance matrices blessing of dimensionality eigenanalysis fast convergence rates multivariate time series
2012/6/19
This paper deals with the factor modeling for high-dimensional time series based on a dimension-reduction viewpoint. Under stationary settings, the inference is simple in the sense that both the numbe...