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Showing 4 results for Lasso
H Movaghari, S.m.e Hosseininasab,
Volume 15, Issue 2 (3-2011)
Abstract
Ali Aghmohammadi, Sakine Mohammadi,
Volume 21, Issue 2 (3-2017)
Abstract
Dynamic panel data models include the important part of medicine, social and economic studies. Existence of the lagged dependent variable as an explanatory variable is a sensible trait of these models. The estimation problem of these models arises from the correlation between the lagged depended variable and the current disturbance. Recently, quantile regression to analyze dynamic panel data has been taken in to consideration. In this paper, quantile regression model by adding an adaptive Lasso penalty term to the random effects for dynamic panel data is introduced by assuming correlation between the random effects and initial observations. Also, this model is illustrated by assuming that the random effects and initial values are independent. These two models are analyzed from a Bayesian point of view. Since, in these models posterior distributions of the parameters are not in explicit form, the full conditional posterior distributions of the parameters are calculated and the Gibbs sampling algorithm is used to deduction. To compare the performance of the proposed method with the conventional methods, a simulation study was conducted and at the end, applications to a real data set are illustrated.
Miss Zahra Eslami, Miss Mina Norouzirad, Mr Mohammad Arashi,
Volume 25, Issue 1 (1-2021)
Abstract
The proportional hazard Cox regression models play a key role in analyzing censored survival data. We use penalized methods in high dimensional scenarios to achieve more efficient models. This article reviews the penalized Cox regression for some frequently used penalty functions. Analysis of medical data namely ”mgus2” confirms the penalized Cox regression performs better than the cox regression model. Among all penalty functions, LASSO provides the best fit.
Dr Mahdi Roozbeh, Ms Monireh Maanavi,
Volume 27, Issue 1 (3-2023)
Abstract
Analysis and modeling the high-dimensional data is one of the most challenging problems faced by the world nowaday. Interpretation of such data is not easy and needs to be applied to modern methods. The penalized methods are one of the most popular ways to analyze the high-dimensional data. Also, the regression models and their analysis are affected by the outliers seriously. The least trimmed squares method is one of the best robust approaches to solve the corruptive influence of the outliers. Semiparametric models, which are a combination of both parametric and nonparametric models, are very flexible models. They are useful when the model contains both parametric and nonparametric parts. The main purpose of this paper is to analyze semiparametric models in high-dimensional data with the presence of outliers using the robust sparse Lasso approach. Finally, the performance of the proposed estimator is examined using a real data analysis about production of vitamin B2.
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