High-dimensional analysis of variance in multivariate linear regression

نویسندگان

چکیده

Summary In this paper, we develop a systematic theory for high-dimensional analysis of variance in multivariate linear regression, where the dimension and number coefficients can both grow with sample size. We propose new U-type statistic to test hypotheses establish Gaussian approximation result under fairly mild moment assumptions. Our general framework be used deal classical one-way variance, nonparametric high dimensions. To implement procedure, introduce sample-splitting-based estimator second error covariance discuss its properties. A simulation study shows that our proposed outperforms some existing tests various settings.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Boosting 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 کامل

Variance Estimation in High Dimensional Regression Models

We treat the problem of variance estimation of the least squares estimate of the parameter in high dimensional linear regression models by using the Uncorrelated Weights Bootstrap (UBS). We find a representation of the UBS dispersion matrix and show that the bootstrap estimator is consistent if p/n → 0 where p is the dimension of the parameter and n is the sample size. For fixed dimension we sh...

متن کامل

Methods 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 کامل

High-dimensional regression with unknown variance

We review recent results for high-dimensional sparse linear regression in the practical case of unknown variance. Different sparsity settings are covered, including coordinate-sparsity, group-sparsity and variation-sparsity. The emphasis is put on non-asymptotic analyses and feasible procedures. In addition, a small numerical study compares the practical performance of three schemes for tuning ...

متن کامل

ذخیره 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: Biometrika

سال: 2023

ISSN: ['0006-3444', '1464-3510']

DOI: https://doi.org/10.1093/biomet/asad001