Estimation in high-dimensional linear models with deterministic design matrices [PDF]
, 2012 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.
Deng, Xinwei, Shao, Jun
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Majorization-Minimization algorithms for nonsmoothly penalized objective functions
, 2010 The use of penalization, or regularization, has become common in high-dimensional statistical analysis, where an increasingly frequent goal is to simultaneously select important variables and estimate their effects.
E. Schifano, R. Strawderman, M. Wells
semanticscholar +1 more source
Skellam shrinkage: Wavelet-based intensity estimation for inhomogeneous Poisson data
, 2009 The ubiquity of integrating detectors in imaging and other applications implies that a variety of real-world data are well modeled as Poisson random variables whose means are in turn proportional to an underlying vector-valued signal of interest. In this
Hirakawa, Keigo, Wolfe, Patrick J.
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, 2010 A popular approach to smooth models for longitudinal data is to express the model as a mixed model, since this often leads to immediate model fitting with standard procedures. This approach is particularly appealing when truncated polynomials are used as
V. Djeundje, I. Currie
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Selecting the number of principal components: estimation of the true rank of a noisy matrix [PDF]
, 2015 Principal component analysis (PCA) is a well-known tool in multivariate statistics. One significant challenge in using PCA is the choice of the number of components.
Choi, Yunjin +2 more
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Estimadores compuestos en estadística regional: una aplicación a la estimación de la tasa de variación de la ocupación en la industria [PDF]
, 2002 Este trabajo es parte de un proyecto que estudia la aplicación de estimadores compuestos (combinación de estimadores directos e indirectos) para áreas pequeñas en estadística regional.
Costa, Àlex, Satorra, A., Ventura, Eva
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Adaptive robust variable selection
, 2014 Heavy-tailed high-dimensional data are commonly encountered in various scientific fields and pose great challenges to modern statistical analysis. A natural procedure to address this problem is to use penalized quantile regression with weighted $L_1 ...
Barut, Emre +2 more
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An extended class of minimax generalized Bayes estimators of regression coefficients
, 2008 We derive minimax generalized Bayes estimators of regression coefficients in the general linear model with spherically symmetric errors under invariant quadratic loss for the case of unknown scale. The class of estimators generalizes the class considered
Abramowitz +11 more
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Improving the Robustness of Variable Selection and Predictive Performance of Regularized Generalized Linear Models and Cox Proportional Hazard Models. [PDF]
Mathematics (Basel), 2023 Hong F, Tian L, Devanarayan V.
europepmc +1 more source
Confidence Sets Based on Penalized Maximum Likelihood Estimators in Gaussian Regression
, 2010 Confidence intervals based on penalized maximum likelihood estimators such as the LASSO, adaptive LASSO, and hard-thresholding are analyzed. In the known-variance case, the finite-sample coverage properties of such intervals are determined and it is ...
Pötscher, Benedikt M. +1 more
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