Statistical Properties of Multivariate Distance Matrix Regression for High-Dimensional Data Analysis
نویسندگان
چکیده
منابع مشابه
Statistical Properties of Multivariate Distance Matrix Regression for High-Dimensional Data Analysis
Multivariate distance matrix regression (MDMR) analysis is a statistical technique that allows researchers to relate P variables to an additional M factors collected on N individuals, where P ≫ N. The technique can be applied to a number of research settings involving high-dimensional data types such as DNA sequence data, gene expression microarray data, and imaging data. MDMR analysis involves...
متن کاملMethods for regression analysis in high-dimensional data
By evolving science, knowledge and technology, new and precise methods for measuring, collecting and recording information have been innovated, which have resulted in the appearance and development of high-dimensional data. The high-dimensional data set, i.e., a data set in which the number of explanatory variables is much larger than the number of observations, cannot be easily analyzed by ...
متن کاملSupervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data
As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often the number of labeled samples used for supervised classification techniques is limited, thus limiting the precision with which class characteristics can be estimated. As the number of spect...
متن کاملBoosting for High-multivariate Responses in High-dimensional Linear Regression
We propose a boosting method, multivariate L2Boosting, for multivariate linear regression based on some squared error loss for multivariate data. It can be applied to multivariate linear regression with continuous responses and to vector autoregressive time series. We prove, for i.i.d. as well as time series data, that multivariate L2Boosting can consistently recover sparse high-dimensional mul...
متن کاملBayesian models for sparse regression analysis of high dimensional data
This paper considers the task of building efficient regression models for sparse multivariate analysis of high dimensional data sets, in particular it focuses on cases where the numbers q of responses Y = (y k , 1 ≤ k ≤ q) and p of predictors X = (xj , 1 ≤ j ≤ p) to analyse jointly are both large with respect to the sample size n, a challenging bi-directional task. The analysis of such data set...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Genetics
سال: 2012
ISSN: 1664-8021
DOI: 10.3389/fgene.2012.00190