Regularized Estimation with Variable Exponential Forgetting

2020 ◽  
Author(s):  
Jakub Dokoupil ◽  
Pavel Vaclavek
Keyword(s):  
Regularized Estimation

Regularized estimation in GINAR(p) process

2017 ◽  
Vol 46 (4) ◽  
pp. 502-517 ◽  
Author(s):  
Haixiang Zhang ◽  
Dehui Wang ◽  
Liuquan Sun
Keyword(s):  
Regularized Estimation

scholarly journals Choosing Among Regularized Estimators in Empirical Economics: The Risk of Machine Learning

2019 ◽  
Vol 101 (5) ◽  
pp. 743-762 ◽  
Author(s):  
Alberto Abadie ◽  
Maximilian Kasy
Keyword(s):  
Machine Learning ◽  
Relative Performance ◽  
Data Driven ◽  
Regularized Estimation ◽  
Empirical Economics ◽  
Regularization Parameters ◽  
Data Generating Process ◽  
Selection Of

Many settings in empirical economics involve estimation of a large number of parameters. In such settings, methods that combine regularized estimation and data-driven choices of regularization parameters are useful. We provide guidance to applied researchers on the choice between regularized estimators and data-driven selection of regularization parameters. We characterize the risk and relative performance of regularized estimators as a function of the data-generating process and show that data-driven choices of regularization parameters yield estimators with risk uniformly close to the risk attained under the optimal (unfeasible) choice of regularization parameters. We illustrate using examples from empirical economics.

scholarly journals Regularized Estimation of the Four-Parameter Logistic Model

Psych ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 269-278
Author(s):  
Michela Battauz
Keyword(s):  
Logistic Model ◽  
Likelihood Function ◽  
Latent Trait ◽  
Real Data ◽  
Theory Model ◽  
Simulation Studies ◽  
Data Set ◽  
Penalty Term ◽  
Regularized Estimation ◽  
Item Parameters

The four-parameter logistic model is an Item Response Theory model for dichotomous items that limit the probability of giving a positive response to an item into a restricted range, so that even people at the extremes of a latent trait do not have a probability close to zero or one. Despite the literature acknowledging the usefulness of this model in certain contexts, the difficulty of estimating the item parameters has limited its use in practice. In this paper we propose a regularized estimation approach for the estimation of the item parameters based on the inclusion of a penalty term in the log-likelihood function. Simulation studies show the good performance of the proposal, which is further illustrated through an application to a real-data set.

scholarly journals Regularized estimation of large covariance matrices

2008 ◽  
Vol 36 (1) ◽  
pp. 199-227 ◽  
Author(s):  
Peter J. Bickel ◽  
Elizaveta Levina
Keyword(s):  
Covariance Matrices ◽  
Regularized Estimation
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