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Testing the Diagonality of a Large Covariance Matrix in a Regression Setting
Bias-Corrected Test Covariance Diagonality Test High Di- mensional Data
2016/1/26
In multivariate analysis, the covariance matrix associated with a set of vari-ables of interest (namely response variables) commonly contains valuable infor-mation about the dataset. When the dimensio...
A Strong Law of Large Numbers for Super-stable Processes
Strong Law Large Numbers Super-stable Processes
2016/1/20
A Strong Law of Large Numbers for Super-stable Processes.
A Large Deviations Perspective on Ordinal Optimization
Large Deviations Perspective Ordinal Optimization
2015/7/6
We consider the problem of optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studied in the literature ...
Large deviation principles and complete equivalence and nonequivalence results for pure and mixed ensembles
Statistics turbulent coherent structures mechanical model the model of two-dimensional fluid motion
2014/12/29
We consider a general class of statistical mechanical models of coherent structures in turbulence, which includes models of two-dimensional fluid motion, quasi-geostrophic flows, and dispersive waves....
Large deviations in quantum lattice systems: One-phase region
Gibbs - KMS quantum spin systems Fermi gases
2014/12/29
We give large deviation upper bounds, and discuss lower bounds, for the Gibbs-KMS state of a system of quantum spins or an interacting Fermi gas on the lattice. We cover general interactions and gener...
A Note on Central Limit Theorems for Linear Spectral Statistics of Large Dimensional F-matrix
Linear spectral statistics central limit theorem centralized sample covari-ance matrix centralizedF-matrix simplified sample covariance matrix simplified F-matrix
2013/6/13
Sample covariance matrix and multivariate $F$-matrix play important roles in multivariate statistical analysis. The central limit theorems {\sl (CLT)} of linear spectral statistics associated with the...
Quantile Regression for Large-scale Applications
Quantile Regression Large-scale Applications
2013/6/14
Quantile regression is a method to estimate the quantiles of the conditional distribution of a response variable, and as such it permits a much more accurate portrayal of the relationship between the ...
Efficiently Using Second Order Information in Large l1 Regularization Problems
Efficiently Using Second Order Information Large l1 Regularization Problems
2013/4/28
We propose a novel general algorithm LHAC that efficiently uses second-order information to train a class of large-scale l1-regularized problems. Our method executes cheap iterations while achieving f...
Large-Margin Metric Learning for Partitioning Problems
Large-Margin Metric Learning Partitioning Problems
2013/4/28
In this paper, we consider unsupervised partitioning problems, such as clustering, image segmentation, video segmentation and other change-point detection problems. We focus on partitioning problems b...
An Improved Bound for the Nystrom Method for Large Eigengap
An Improved Bound the Nystrom Method Large Eigengap
2012/11/23
We develop an improved bound for the approximation error of the Nystr\"{o}m method under the assumption that there is a large eigengap in the spectrum of kernel matrix. This is based on the empirical ...
Parallelism, Uniqueness, and Large-Sample Asymptotics for the Dantzig Selector
Lasso,Regularizedregression Variableselectionandestimation.
2012/11/21
The Dantzig selector (Candes and Tao, 2007) is a popular l1-regularization method for variable selection and estimation in linear regression. We present a very weak geometric condition on the observed...
On Set Size Distribution Estimation and the Characterization of Large Networks via Sampling
On Set Size Distribution Estimation Characterization large Networks via Sampling
2012/11/22
In this work we study the set size distribution estimation problem, where elements are randomly sampled from a collection of non-overlapping sets and we seek to recover the original set size distribut...
Distributed Detection/Isolation Procedures for Quickest Event Detection in Large Extent Wireless Sensor Networks
Disorder problem distributed quickest change detection detection with distance dependent sensing fusion of CUSUMs multi–hypothesis change detection
2011/6/20
We study a problem of distributed detection of an event in a large extent wireless sensor network
(WSN), where the event influences the observations of the sensors only in the vicinity of where it oc...
Linear1 Support Vector Machines (e.g., SVMperf, Pegasos,
LIBLINEAR) are powerful and extremely efficient classification
tools when the datasets are very large and/or highdimensional,
which is commo...
Delta method in large deviations and moderate deviations for estimators
Delta method hypothesis testing Kaplan–Meier estimator large deviations L-statistics M-estimator moderate deviations
2011/6/20
The delta method is a popular and elementary tool for deriving
limiting distributions of transformed statistics, while applications of
asymptotic distributions do not allow one to obtain desirable a...