Add to ORKGIn this article, we describe an iterative approach for the estimation of linear regression models with high-dimensional fixed effects. This approach is computationally intensive but imposes minimum memory requirements. We also show that the approach can be extended to nonlinear models and to more than two high-dimensional fixed effects. Copyright 2010 by StataCorp LP.fixed effects, panel data
This dissertation consists of three chapters related to high dimensional econometrics dealing with t...
This thesis considers estimation and statistical inference for high dimensional model with constrain...
Abstract The problem of approximating high-dimensional data with a low-dimensional representa-tion i...
In this article, we describe an iterative approach for the estimation of linear regression models wi...
In this paper we describe an alternative iterative approach for the estimation of linear regression ...
In this article, I describe an alternative approach for fitting linear models with multiple high-ord...
High-dimensional linear models play an important role in the analysis of modern data sets. Although ...
This thesis presents a new approach to fitting linear models, called “pace regression”, which also o...
High-dimensional linear models play an important role in the analysis of modern data sets. Although ...
This article proposes a memory-saving decomposition of the design matrix to facilitate the estimatio...
We consider the problem of structurally con-strained high-dimensional linear regression. This has at...
We propose a new method of estimation in high-dimensional linear regression model. It allows for ver...
This dissertation considers the problem of estimation and inference in four high-dimensional models:...
<p>We propose a methodology for testing linear hypothesis in high-dimensional linear models. The pro...
In high-dimensional regression problems, a key aim is to identify a sparse model that fits the data...
This dissertation consists of three chapters related to high dimensional econometrics dealing with t...
This thesis considers estimation and statistical inference for high dimensional model with constrain...
Abstract The problem of approximating high-dimensional data with a low-dimensional representa-tion i...
In this article, we describe an iterative approach for the estimation of linear regression models wi...
In this paper we describe an alternative iterative approach for the estimation of linear regression ...
In this article, I describe an alternative approach for fitting linear models with multiple high-ord...
High-dimensional linear models play an important role in the analysis of modern data sets. Although ...
This thesis presents a new approach to fitting linear models, called “pace regression”, which also o...
High-dimensional linear models play an important role in the analysis of modern data sets. Although ...
This article proposes a memory-saving decomposition of the design matrix to facilitate the estimatio...
We consider the problem of structurally con-strained high-dimensional linear regression. This has at...
We propose a new method of estimation in high-dimensional linear regression model. It allows for ver...
This dissertation considers the problem of estimation and inference in four high-dimensional models:...
<p>We propose a methodology for testing linear hypothesis in high-dimensional linear models. The pro...
In high-dimensional regression problems, a key aim is to identify a sparse model that fits the data...
This dissertation consists of three chapters related to high dimensional econometrics dealing with t...
This thesis considers estimation and statistical inference for high dimensional model with constrain...
Abstract The problem of approximating high-dimensional data with a low-dimensional representa-tion i...