It is shown that in the context of generalized linear models, such methods possess model selection consistency with oracle properties even for dimensionality of nonpolynomial order of sample size, for a class of penalized likelihood approaches using folded-concave penalty functions, which were introduced to ameliorate the bias problems of convex penalty functions.
Abstract:
Penalized likelihood methods are fundamental to ultrahigh dimensional variable selection. How high dimensionality such methods can handle remains largely unknown. In this paper, we show that in the context of generalized linear models, such methods possess model selection consistency with oracle properties even for dimensionality of nonpolynomial (NP) order of sample size, for a class of penalized likelihood approaches using folded-concave penalty functions, which were introduced to ameliorate the bias problems of convex penalty functions. This fills a long-standing gap in the literature where the dimensionality is allowed to grow slowly with the sample size. Our results are also applicable to penalized likelihood with the L1-penalty, which is a convex function at the boundary of the class of folded-concave penalty functions under consideration. The coordinate optimization is implemented for finding the solution paths, whose performance is evaluated by a few simulation examples and the real data analysis.
TL;DR: In this paper, a brief account of the recent developments of theory, methods, and implementations for high-dimensional variable selection is presented, with emphasis on independence screening and two-scale methods.
TL;DR: This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level, and includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices.
TL;DR: Zhang et al. as mentioned in this paper proposed a unified framework named detecting contiguous outliers in the LOw-rank representation (DECOLOR), which integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm.
TL;DR: This paper presents a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR), which integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently.
TL;DR: In this article, a discrete extension of modern first-order continuous optimization methods is proposed to find high quality feasible solutions that are used as warm starts to a MIO solver that finds provably optimal solutions.
TL;DR: In this article, penalized likelihood approaches are proposed to handle variable selection problems, and it is shown that the newly proposed estimators perform as well as the oracle procedure in variable selection; namely, they work as well if the correct submodel were known.
TL;DR: A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.
TL;DR: In this paper, instead of selecting factors by stepwise backward elimination, the authors focus on the accuracy of estimation and consider extensions of the lasso, the LARS algorithm and the non-negative garrotte for factor selection.
TL;DR: A new version of the lasso is proposed, called the adaptive lasso, where adaptive weights are used for penalizing different coefficients in the ℓ1 penalty, and the nonnegative garotte is shown to be consistent for variable selection.
TL;DR: In this article, the authors define the Ball Sigma-Field and Measurability of Suprema and show that it is possible to achieve convergence almost surely and in probability.
Q1. What are the contributions mentioned in the paper "Nonconcave penalized likelihood with np-dimensionality" ?
In this paper, the authors show that in the context of generalized linear models, such methods possess model selection consistency with oracle properties even for dimensionality of nonpolynomial ( NP ) order of sample size, for a class of penalized likelihood approaches using folded-concave penalty functions, which were introduced to ameliorate the bias problems of convex penalty functions.
Q2. What is the condition for the local maximizer of the nonconcave penalized likelihood?
Then is a strict local maximizer of the nonconcave penalized likelihood defined by (3) if(7)(8)(9)where and respectively denote the submatrices of formed by columns in and its complement, ,is a subvector of formed by all nonzero components, and .
Q3. What is the dimensionality of the penalized least squares?
In this case, the dimensionality that the penalized least-squares can handle is as high as when, which is usually smaller than that for the case of .
Q4. What is the condition of the Gaussian linear regression model?
Condition (16) controls the uniform growth rate of the -norm of these multiple regression coefficients, a notion of weak correlation between and .
Q5. What is the definition of a coordinate subspace?
A subspace of is called coordinate subspace if it is spanned by a subset of the natural basis , where each is the -vector with th component 1 and 0 elsewhere.
Q6. what is the maxi-mizer of the penalized likelihood?
Then there exists a strict local maxi-mizer of the penalized likelihood such that with probability tending to 1 as and, where is a subvector of formed by components in .
Q7. What is the simplest way to show that the second derivative of the penalty function does not exist?
More generally, when the second derivative of the penalty function does not necessarily exist, it is easy to show that the second part of the matrix can be replaced by a diagonal matrix with maximum absolute element bounded by .
Q8. What is the concavity of the convex set?
By the concavity of , the authors can easily show that for , is a closed convex set with and being its interior points and the level set is its boundary.
Q9. What is the second order approximation in ICA?
When is quadratic in , e.g., for the Gaussian linear regression model, the second order approximation in ICA is exact at each step.
Q10. Why do the authors examine the implications of Theorem 2?
Due to its popularity, the authors now examine the implications of Theorem 2 in the context of penalized least-squares and penalized likelihood.