Adaptive Elastic Net with Distance Correlation on the Grouping Effect and Robust of High Dimensional Stock Market Price

Stock market is found in many financial studies. Nonetheless, many of these literatures do not consider on the highly correlated stock market price. In particular, the studies on variable selection, grouping effects and robust dedicated to high dimension stock market price can be considered as scarce. Penalized linear regression using elastic net is one of the recognized methods to perform variable selection. However, the lack of consistency in variable selection may reduce the model performance. Hence, adaptive elastic net with distance correlation (AEDC) is proposed in this study and compared against elastic net, adaptive elastic net with elastic weight and adaptive elastic net with ridge weight. AEDC had lower mean squared error when the alpha increases from 0.05 to 0.95. Thus, the proposed method has successfully contributed to encouraging grouping effects between the highly correlated variables and also has an improved model performance in the presence of robustness.

Lymph Node Parameters Predict Adjuvant Chemoradiotherapy Efficacy and Disease-Free Survival in Pathologic N2 Non-Small Cell Lung Cancer

Pathologic N2 non-small cell lung cancer (NSCLC) is prominently intrinsically heterogeneous. We aimed to identify homogeneous prognostic subgroups and evaluate the role of different adjuvant treatments. We retrospectively collected patients with resected pathologic T1-3N2M0 NSCLC from the Shanghai Chest Hospital as the primary cohort and randomly allocated them (3:1) to the training set and the validation set 1. We had patients from the Fudan University Shanghai Cancer Center as an external validation cohort (validation set 2) with the same inclusion and exclusion criteria. Variables significantly related to disease-free survival (DFS) were used to build an adaptive Elastic-Net Cox regression model. Nomogram was used to visualize the model. The discriminative and calibration abilities of the model were assessed by time-dependent area under the receiver operating characteristic curves (AUCs) and calibration curves. The primary cohort consisted of 1,312 patients. Tumor size, histology, grade, skip N2, involved N2 stations, lymph node ratio (LNR), and adjuvant treatment pattern were identified as significant variables associated with DFS and integrated into the adaptive Elastic-Net Cox regression model. A nomogram was developed to predict DFS. The model showed good discrimination (the median AUC in the validation set 1: 0.66, range 0.62 to 0.71; validation set 2: 0.66, range 0.61 to 0.73). We developed and validated a nomogram that contains multiple variables describing lymph node status (skip N2, involved N2 stations, and LNR) to predict the DFS of patients with resected pathologic N2 NSCLC. Through this model, we could identify a subtype of NSCLC with a more malignant clinical biological behavior and found that this subtype remained at high risk of disease recurrence after adjuvant chemoradiotherapy.

Variable selection via the adaptive elastic net: mathematics success of the students in Singapore and Turkey

The quality of education is crucial for its competitiveness in the developing world. International tests are organized at regular intervals to measure the quality of education and to see the place in the ranking of countries. The surveys on these examinations have provided a large number of variables that can be effective on the scores of the tests, including family, teacher, school and course equipment and information communication technologies, etc. The important question is which variables are relevant for the students' achievement in these tests. We investigated the barriers of mathematics success of Turkish students in the TIMSS exam and compared their status with Singaporean students who took part in at top of the ranking in the exam. For this, we employed the adaptive elastic net which is one of the regularized regression methods to dataset and compared their prediction accuracy according to three different alpha levels [0.1; 0.5; 0.9] to determine the model that has high variable selection ability with optimal prediction. The adaptive elastic net with the alpha level [0.9] was selected as superior to others. As the findings, a technology-oriented education system can help to success of the students in Turkey and the countries having similar experiences in international tests.

The Higher-Order of Adaptive Lasso and Elastic Net Methods for Classification on High Dimensional Data

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.