Estimation in high-dimensional linear models with deterministic design matrices
نویسندگان
چکیده
منابع مشابه
Estimation in High - Dimensional Linear Models with Deterministic Design Matrices
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 selection in these high-dimensional problems with deterministic design points is very different from those in the case of random covariates, due to the identifiability of the high-dimensional regressio...
متن کاملRobust Estimation in Linear Regression with Molticollinearity and Sparse Models
One of the factors affecting the statistical analysis of the data is the presence of outliers. The methods which are not affected by the outliers are called robust methods. Robust regression methods are robust estimation methods of regression model parameters in the presence of outliers. Besides outliers, the linear dependency of regressor variables, which is called multicollinearity...
متن کاملOptimal Estimation of Co-heritability in High-dimensional Linear Models
Co-heritability is an important concept that characterizes the genetic associations within pairs of quantitative traits. There has been significant recent interest in estimating the co-heritability based on data from the genome-wide association studies (GWAS). This paper introduces two measures of co-heritability in the highdimensional linear model framework, including the inner product of the ...
متن کاملHeritability estimation in high dimensional sparse linear mixed models
Abstract: Motivated by applications in genetic fields, we propose to estimate the heritability in high-dimensional sparse linear mixed models. The heritability determines how the variance is shared between the different random components of a linear mixed model. The main novelty of our approach is to consider that the random effects can be sparse, that is may contain null components, but we do ...
متن کاملMaximum Likelihood for Variance Estimation in High-Dimensional Linear Models
The plots in Figure 1 from the main text were generated using various estimators of σ 0 , each of which was computed for 500 independent datasets (y, X). The datasets were generated according to the linear model (1)– (2) (equation references refer to the main text), with n = 500, p = 1000, σ 0 = 1, and η 2 0 = 4. We considered settings where β had various sparsity levels, indicated by a paramet...
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: The Annals of Statistics
سال: 2012
ISSN: 0090-5364
DOI: 10.1214/12-aos982