5 research outputs found
Likelihood Adaptively Modified Penalties
A new family of penalty functions, adaptive to likelihood, is introduced for
model selection in general regression models. It arises naturally through
assuming certain types of prior distribution on the regression parameters. To
study stability properties of the penalized maximum likelihood estimator, two
types of asymptotic stability are defined. Theoretical properties, including
the parameter estimation consistency, model selection consistency, and
asymptotic stability, are established under suitable regularity conditions. An
efficient coordinate-descent algorithm is proposed. Simulation results and real
data analysis show that the proposed method has competitive performance in
comparison with existing ones.Comment: 42 pages, 4 figure
Least angle regression for time series forecasting with many predictors.
Least Angle Regression(LARS)is a variable selection method with proven performance for cross-sectional data. In this paper, it is extended to time series forecasting with many predictors. The new method builds parsimonious forecast models,taking the time series dynamics into account. It is a exible method that allows for ranking the different predictors according to their predictive content. The time series LARS shows good forecast performance, as illustrated in a simulation study and two real data applications, where it is compared with the standard LARS algorithm and forecasting using diffusion indices.macro-econometrics; model selection; penalized regression; variable ranking;
Least angle and penalized regression: A review
Least Angle Regression is a promising technique for variable selection
applications, offering a nice alternative to stepwise regression. It provides
an explanation for the similar behavior of LASSO (-penalized
regression) and forward stagewise regression, and provides a fast
implementation of both. The idea has caught on rapidly, and sparked a great
deal of research interest. In this paper, we give an overview of Least Angle
Regression and the current state of related research.Comment: Published in at http://dx.doi.org/10.1214/08-SS035 the Statistics
Surveys (http://www.i-journals.org/ss/) by the Institute of Mathematical
Statistics (http://www.imstat.org
A Fast Tracking ALgorithm for Generalized LARS/LASSO
This paper gives an efficient algorithm for tracking the solution curve of sparse logistic regression with respect to the L_1 regularization parameter. The algorithm is based on approximating the logistic regression loss by a piecewise quadratic function, using Rosset and Zhu’s path tracking algorithm on the approximate problem, and then applying a correction to get to the true path. Application of the algorithm to text classification and sparse kernel logistic regression shows that the algorithm is efficient