Results 31 to 40 of about 825 (114)

Sparsity considerations for dependent observations [PDF]

, 2011
The aim of this paper is to provide a comprehensive introduction for the study of L1-penalized estimators in the context of dependent observations. We define a general $\ell_{1}$-penalized estimator for solving problems of stochastic optimization.
Alquier, Pierre, Doukhan, Paul
core   +5 more sources

Some theoretical foundations for the design and analysis of randomized experiments

Journal of Causal Inference
Neyman’s seminal work in 1923 has been a milestone in statistics over the century, which has motivated many fundamental statistical concepts and methodology.
Shi Lei, Li Xinran
doaj   +1 more source

Spectral Condition Numbers of Orthogonal Projections and Full Rank Linear Least Squares Residuals

, 2010
A simple formula is proved to be a tight estimate for the condition number of the full rank linear least squares residual with respect to the matrix of least squares coefficients and scaled 2-norms.
Grcar, Joseph F.
core   +1 more source

Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects

Journal of Causal Inference
Following Efron (2014), we propose an algorithm for estimating treatment effects for use by researchers employing a regression-discontinuity (RD) design.
Long Mark C., Rooklyn Jordan
doaj   +1 more source

A matrix approach to determine optimal predictors in a constrained linear mixed model

Open Mathematics
For a general vector of all unknown vectors in a constrained linear mixed model (CLMM), this study compared the dispersion matrices of the best linear unbiased predictors with any symmetric matrix for determining the optimality of predictors among others.
Güler Nesrin, Büyükkaya Melek Eriş
doaj   +1 more source

Orthogonalized smoothing for rescaled spike and slab models

, 2008
Rescaled spike and slab models are a new Bayesian variable selection method for linear regression models. In high dimensional orthogonal settings such models have been shown to possess optimal model selection properties.
Ishwaran, Hemant, Papana, Ariadni
core   +1 more source

Asymptotic optimality of a cross-validatory predictive approach to linear model selection

, 2008
In this article we study the asymptotic predictive optimality of a model selection criterion based on the cross-validatory predictive density, already available in the literature. For a dependent variable and associated explanatory variables, we consider
Chakrabarti, Arijit, Samanta, Tapas
core   +1 more source

High-dimensional stochastic optimization with the generalized Dantzig estimator [PDF]

, 2008
We propose a generalized version of the Dantzig selector. We show that it satisfies sparsity oracle inequalities in prediction and estimation. We consider then the particular case of high-dimensional linear regression model selection with the Huber loss ...
Lounici, Karim
core   +2 more sources

New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model

مجلة بغداد للعلوم, 2020
This paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model.
Mustafa Ismaeel Naif Alheety
doaj  

On the history and use of some standard statistical models

, 2008
This paper tries to tell the story of the general linear model, which saw the light of day 200 years ago, and the assumptions underlying it. We distinguish three principal stages (ignoring earlier more isolated instances). The model was first proposed in
Lehmann, E. L.
core   +1 more source