CHEN MING-HUI, DEY D.K., Variable selection for multivariate logistic regression models, Journal of Statistical Planning & Inference, 2003, Vol. 111, Issue 1/2, s. 37-55.Chen M-H, Dey DK. Variable selection for multivariate logistic regression models. Journal of Statistical Planning and ...
In regression analysis, L1 regularizations such as the lasso or the SCAD provide sparse solutions, which leads to variable selection. We consider the variable selection problem where variables are given as functional forms, using L1 regularization. In order to select functional variables each of whic...
Finite mixture regression (FMR) models are frequently used in statistical modeling, often with many covariates with low significance. Variable selection techniques can be employed to identify the covariates with little influence on the response. The problem of variable selection in FMR models is studied...
Variable Selection Using a Smooth Information Criterion for Multi-Parameter Regression ModelsModern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the ... M O'Neill,K Burke 被引量: 0发表: 2021年 Variable Selec...
Table 1. All Possible Hierarchically Well-Formulated Models Obtained From the Set of Predictors Table 2. All Possible Hierarchically Well-Formulated Models Obtained From the Set of Predictors Stepwise regression and other related one-step procedures such as backward elimination and forward selection can ...
Using different methods, you can construct a variety of regression models from the same set of variables. Enter (Regression). A procedure for variable selection in which all variables in a block are entered in a single step. Stepwise. At each step, the independent variable not in the ...
Therefore, determining the variables that are important for the location and the scale is as important as estimating the parameters of these models. From this point of view, a combine robust estimation and variable selection method is proposed to simultaneously estimate the parameters and select the...
Simulation was used to evaluate the performances of several methods of variable selection in regression modeling: stepwise regression based on partial F-tests, stepwise minimization of Mallows’ C p statistic and Schwarz’s Bayes Information Criterion (BIC), and regression trees constructed with two ki...
The problem of variable selection in neural network regression models with dependent data is considered. In this framework, a test procedure based on the introduction of a measure for the variable relevance to the model is discussed. The main difficulty in using this procedure is related to the ...
aI keep silence, doesn't mean I don't know anything.! 我保留沈默,不意味我不知道什么。![translate] aVariable Selection and Covariance Selection in Multivariate Regression Models 易变的选择和协变性选择在多维分布的回归模型[translate]