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Item type:Publication, Comparing penalized regression analysis of logistic regression model with multicollinearity(2019-07-08); Kuharatanachai, ChoojaiThe goal of this research is to estimate the parameter of the logistic regression model by penalized regression analysis which consisted of ridge regression, lasso, and elastic net method. The logistic regression is considered between a binary dependent variable and 3 and 5 independent variables. The independent variables are generated from normal distribution, contaminated normal distribution, and t distribution on correlation coefficient at 0.1, 0.5, and 0.99 or called multicollinearity problem. The maximum likelihood estimator has used as the classical method by differential the log likelihood function with respect to the coefficients. Ridge regression is to choose the unknown ridge parameter by cross-validation, so ridge estimator is evaluated by the adding ridge parameter on penalty term. Lasso (least absolute shrinkage and selection operator) is added the penalty term on scales sum of the absolute value of the coefficients. The elastic net can be mixed between ridge regression and lasso on the penalty term. The criterion of these methods is compared by percentage of predicted accuracy value. The results are found that lasso is satisfied when the independent variables are simulated from normal and t distribution in most cases, and the lasso outperforms on the contaminated normal distribution. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The higher-order of adaptive lasso and elastic net methods for classification on high dimensional data(2021-05-02)The lasso and elastic net methods are the popular technique for parameter estimation and variable selection. Moreover, the adaptive lasso and elastic net methods use the adaptive weights on the penalty function based on the lasso and elastic net estimates. The adaptive weight is related to the power order of the estimator. Normally, these methods focus to estimate parameters in terms of linear regression models that are based on the dependent variable and independent variable as a continuous scale. In this paper, we compare the lasso and elastic net methods and the higher-order of the adaptive lasso and adaptive elastic net methods for classification on high dimensional data. The classification is used to classify the categorical data for dependent variable dependent on the independent variables, which is called the logistic regression model. The categorical data are considered a binary variable, and the independent variables are used as the continuous variable. The high dimensional data are represented when the number of independent variables is higher than the sample sizes. For this research, the simulation of the logistic regression is considered as the binary dependent variable and 20, 30, 40, and 50 as the independent variables when the sample sizes are less than the number of the independent variables. The independent variables are generated from normal distribution on several variances, and the dependent variables are obtained from the probability of logit function and transforming it to predict the binary data. For application in real data, we express the classification of the type of leukemia as the dependent variables and the subset of gene expression as the independent variables. The criterion of these methods is to compare by the average percentage of predicted accuracy value. The results are found that the higher-order of adaptive lasso method is satisfied with large dispersion, but the higher-order of adaptive elastic net method outperforms on small dispersion.