Results 1 to 10 of about 4,586 (136)
Penalty and Shrinkage Strategies Based on Local Polynomials for Right-Censored Partially Linear Regression [PDF]
Entropy, 2022 This study aims to propose modified semiparametric estimators based on six different penalty and shrinkage strategies for the estimation of a right-censored semiparametric regression model.
Syed Ejaz Ahmed +2 more
doaj +2 more sources
A New Quantile-Based Approach for LASSO Estimation
Mathematics, 2023 Regularization regression techniques are widely used to overcome a model’s parameter estimation problem in the presence of multicollinearity. Several biased techniques are available in the literature, including ridge, Least Angle Shrinkage Selection ...
Ismail Shah +4 more
doaj +1 more source
Frontiers in Forests and Global Change, 2021 Forest information is requested at many levels and for many purposes. Sampling-based national forest inventories (NFIs) can provide reliable estimates on national and regional levels.
Magnus Ekström +2 more
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Survival prediction based on compound covariate under Cox proportional hazard models. [PDF]
PLoS ONE, 2012 Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models.
Takeshi Emura +2 more
doaj +1 more source
A lava attack on the recovery of sums of dense and sparse signals [PDF]
, 2015 Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero parameters that are large in magnitude, or a dense signal model, a model with ...
Chernozhukov, Victor +2 more
core +3 more sources
Improved Penalty Strategies in Linear Regression Models
Revstat Statistical Journal, 2017 We suggest pretest and shrinkage ridge estimation strategies for linear regression models. We investigate the asymptotic properties of suggested estimators.
Bahadır Yüzbaşı +2 more
doaj +1 more source
Performance of LASSO and Elastic net estimators in Misspecified Linear Regression Model
Ceylon Journal of Science, 2019 Ridge Estimator (RE) has been used as an alternative estimator for Ordinary Least Squared Estimator (OLSE) to handle multicollinearity problem in the linear regression model. However, it introduces heavy bias when the number of predictors is high, and it
M. Kayanan, P. Wijekoon
doaj +1 more source
a simulation comparison of Ridge regression estimators with Lars
پژوهشهای ریاضی, 2022 Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters.
Roshanak Alimohammadi, Jaleh Bahari
doaj
Regularized brain reading with shrinkage and smoothing [PDF]
, 2016 Functional neuroimaging measures how the brain responds to complex stimuli. However, sample sizes are modest, noise is substantial, and stimuli are high dimensional. Hence, direct estimates are inherently imprecise and call for regularization. We compare
Ramdas, Aaditya +3 more
core +1 more source
Feature Extraction in Signal Regression: A Boosting Technique for Functional Data Regression [PDF]
, 2007 Main objectives of feature extraction in signal regression are the improvement of accuracy of prediction on future data and identification of relevant parts of the signal. A feature extraction procedure is proposed that uses boosting techniques to select
Gertheiss, Jan, Tutz, Gerhard
core +2 more sources