In many medical studies, in order to describe the course of illness and treatment effects, longitudinal studies are used. In longitudinal studies, responses are measured frequently over time, but sometimes these responses are discrete and with two-state. Recently Binary quantile REGRESSION methods to analyze this kind of data have been taken into consideration. In this paper, quantile REGRESSION model with LASSO and adaptive LASSO penalty for longitudinal data with dichotomous responses is provided. Since in both methods posteriori distributions of the parameters are not in explicit form, thus the full conditional posteriori distributions of parameters are calculated and the Gibbs sampling algorithm is used to deduction. To compare the performance of the proposed methods with the conventional methods, a simulation study was conducted and at the end, applications to a real data set are illustrated.

Background: Exclusive breastfeeding (EBF) in the first six months of the life can significantly improve maternal and children health, and it is especially important in low-and middle-income countries. We aimed to determine the factors affecting EBF duration in a sample of Iranian infants. Methods: This prospective study was conducted between April 2012 and October 2014 in Fars, Iran. Women (N=2640), who had given birth to healthy term infants were categorized into EBF versus non-EBF groups. Demographic information from mothers and infants, medical and drug history, and pregnancy related factors were compared between the two groups. Multivariable analysis was performed using Adaptive LASSO REGRESSION. P<0. 05 was considered significant. Results: The mean duration of EBF was 4. 63± 1. 99 months. There was an inverse association between the mother’ s educational level and duration of EBF (P<0. 001). Also, we found that mothers who were housewives had a significantly longer duration of EBF (4. 68± 1. 97) compared to mothers with either part-time (4. 21± 2. 01) or full-time jobs (4. 02± 2. 12) (P<0. 001). By eliminating the redundant factors, the proposed multivariable model showed the infant’ s weight gain during EBF, singleton/multiple pregnancies, maternal perception of quantity of breast milk, post-partum infection, use of pacifier, neonate’ s irritability, birth place and mother’ s full-time job as the most important factors affecting the duration of EBF. Twin pregnancies, post-partum infection, cesarean section by maternal request, use of a pacifier and irritability in the neonatal period significantly reduced the duration of EBF. Conclusion: Health policy-makers should promote EBF programs among the educated as well as working mothers in order to positively affect the community’ s health status.

One of the most critical discussions in REGRESSION models is the selection of the optimal model, by identifying critical explanatory variables and negligible variables and more easily express the relationship between the response variable and explanatory variables. Given the limitations of selecting variables in classical methods, such as stepwise selection, it is possible to use penalized REGRESSION methods. One of the penalized REGRESSION models is the LASSO REGRESSION model, in which it is assumed that errors follow a normal distribution. In this paper, we introduce the Bayesian LASSO REGRESSION model with an asymmetric distribution error and the high dimensional setting. Then, using the simulation studies and real data analysis, the performance of the proposed model’, s performance is discussed.

‎The high-dimensional data analysis using classical REGRESSION approaches is not applicable, and the consequences may need to be more accurate. This study tried to analyze such data by introducing new and powerful approaches such as support vector REGRESSION, functional REGRESSION, LASSO and ridge REGRESSION. On this subject, by investigating two high-dimensional data sets (riboflavin and simulated data sets) using the suggested approaches, it is progressed to derive the most efficient model based on three criteria (correlation squared, mean squared error and mean absolute error percentage deviation) according to the type of data.

In this study, for the selection of the characteristics of the company that provides the incremental information to investors and financial analysts, the linear models are adapted by the ordinary LASSO method (Tibshirani, 1996), Adaptive Group LASSO (Zu, 2006) and the least squares method (OLS). The main objective of this research is to determine which method can predict the expected return on stock portfolios in the shortest time and using the least effective features. The research sample is1340observations, including 134companies listed in Tehran Stock Exchange, and the research variables from the financial statements of the companies and the stock market reports between 2008and 2018. The results of this study show that by employing the least squares REGRESSION method, 7 characteristics, the typical 5-characteristics LASSO method and in the Adaptive Group LASSO method, only 4characteristics, contain incremental information to predict the expected returns of stock portfolios. In the second place, by applying the Adaptive Group LASSO REGRESSION method, one can achieve the same results with using the least characteristics.

Variable selection in Poisson REGRESSION with high dimensional data has been widely used in recent years. we proposed in this paper using a penalty function that depends on a function named a penalty. An Atan estimator was compared with  LASSO and adaptive LASSO. A simulation and application show that an Atan estimator has the advantage in the estimation of coefficient and variables selection.

The ability to predict corporate financial distress is important to business individuals as well as to the economy in general. Therefore, the purpose of this article is the detection of potential financial distress and early warnings of impending financial distress among the listed companies on Tehran Stock Exchange (TSE) and Iran Fara Bourse (IFB). To do so, a wide range of features including accrual accounting variables, cash-based accounting variables, marketbased variables, corporate governance mechanisms, and macroeconomic indicators have been identified to prospectively predict the financial distress in the companies. The final sample includes 421 firms leading to 3, 670 firm-year observations. The prepared data, was then split into a train and test data set using a 70/30 ratio. In this research, various data pre-processing machine learning techniques i. e., Zscore standardization, one-hot encoding, stratified K-fold validation combined with feature engineering are applied to improve classifier performance. Stratified K-fold cross validation method, (with k = 5) was used for estimation of model prediction performance during training phase. During the training phase, hyperparameter tuning of a model was carried out using a grid-search. Furthermore, a cost-sensitive meta-learning approach in conjunction with the proposed imbalance-oriented metric i. e., F1 score were used to overcome the extreme class imbalance issue. Based on the experimental results, the tuned LASSO logistic model achieved a f1score, MCC, recall and precision of respectively, 50%, 50%, 73% and 38% on the training set. Finally, the proposed model was tested on the hold-out test set which resulted in a f1-score, MCC, recall and precision of 51%, 51%, 73% and 39%, respectively.