Variable Selection Criteria Research Articles

License to drive vehicles: Legal aspects and clinical variables in Brazilian patients with epilepsy.

To evaluate the license to drive a vehicle in patients with epilepsy according to Brazilian legislation and to relate it to demographic and clinical variables. The license-to-drive vehicle rate was related to the demographic and clinical data of 305 patients with epilepsy. The mean age was 49.2years, with 176 females, the mean education was six years, and structural epilepsy occurred in 203 patients. The license to drive vehicles occurred in 35 (11.5%) patients. In the multiple logistic regression analysis, with stepwise variable selection criteria, it was observed that the variables that together best predict the possession of a driving license are education (p-value=0.012; OR = 1.15 (1.03, 1.29)), sex (p-value=0.001; OR = 4.05 (1.80, 9.68)), marital status (p-value=0.022; OR = 2.67 (1.18, 6.41)), neurological examination (p-value=0.029; OR = 3.62 (1.25, 13.22)) and number of antiseizure medication (ASM) in use (p-value=0.019; OR = 3.04 (1.26, 8.28)). The license did not comply with Brazilian legislation in 21 (60%) patients. In nine patients, the practice violated the law due to the type of vehicle, and in 12 patients, due to the maintenance of seizures in the last year. Patients with non-legal aspects of the license were older, had less education, were male, and had a spouse. In patients with epilepsy, the rate of license to drive vehicles was low, and it was associated with higher schooling, males, having a spouse, having no alterations in the neurological examination, and the use of a single ASM. The non-compliance with current legislation was high, suggesting a lack of information or denial of the disease.

Analysis of Fertility Determinants and Regional Disparities in Nigeria using Geo-Additive Regression

This study applied geo-additive regression modelling on high-dimensional data with metrical and categorical predictors, using data from the latest Nigeria Demographic and Health Survey (NDHS-6). The sampled data comprises ninety-three predictors of total children ever born by eighty women of age group between fifteen and forty-nine years. Three penalty-based variable selection criteria—Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP) were employed to reduce the dimensionality of the data. Geo-additive regression model were thereafter applied on the selected predictors to determine the impact of metrical, categorical and spatial predictors on the response variable. Findings revealed that predictors such as contraceptive use, age at first birth, and marital status are major determinants of total children ever born by a woman in Nigeria. Furthermore, spatial analysis revealed regional disparities in fertility rates within Nigeria, with notably higher fertility rate in northeastern states. This study’s findings have broad applications across disciplines. By providing robust methodologies for handling complex datasets, this research supports evidence-based decision-making in public health, agriculture, and environmental policy. Ultimately, these findings contribute to efforts aimed at promoting sustainable development and enhancing maternal and child health outcomes in Nigeria and similar contexts globally.

Causality of immune cells and endometriosis: a bidirectional mendelian randomization study

BackgroundEndometriosis, a prevalent chronic condition, afflicts approximately 10% of women in their reproductive years. Emerging evidence implicates immune cells in the pathogenesis of endometriosis, particularly in angiogenesis, tissue proliferation, and lesion invasion. This investigation employs two-sample Mendelian Randomization (MR) to dissect the bidirectional causal relationships between immune cell profiles and endometriosis.MethodsWe leveraged publicly available genome-wide association study (GWAS) data to elucidate the causal interplay between immune cell traits and endometriosis. Utilizing GWAS summary statistics ranging from accession numbers GCST90001391 to GCST90002121 and endometriosis data from the FinnGen study GWAS (8,288 endometriosis cases and 68,969 controls), we adopted stringent criteria for instrumental variable selection. We applied MR-Egger, weighted median, inverse variance weighted (IVW), and weighted mode methods to derive causal estimates. To address potential heterogeneity and pleiotropy, Cochran’s Q test, MR-Egger intercept, and leave-one-out analyses were executed. Reverse-direction MR and bidirectional MR analyses evaluated potential reciprocal causation and the influence of endometriosis on immune cell composition.ResultsOur analysis identified five immune phenotypes inversely associated with endometriosis risk. These phenotypes comprise: a percentage of CD11c + HLA-DR + + monocytes, CD25 expression on CD39 + CD4 + T cells, elevated CD25 on CD45RA + CD4 + non-regulatory T cells, HLA-DR intensity on HLA-DR + CD8 bright (CD8br) T cells, and the proportion of naïve double-negative (CD4 − CD8− %DN) T cells. In contrast, eleven phenotypes were positively correlated with endometriosis risk, including: CD127 expression on T cells, the proportion of CD24 + CD27 + B cells within lymphocytes, CD25 expression on CD28 + CD4 + T cells, CD28 expression on CD39 + activated regulatory T cells (activated Tregs), the frequency of bright CD33 HLA-DR + CD14 − cells within the CD33br HLA-DR + compartment, CD45 expression on lymphocytes and natural killer (NK) cells, activation status of central memory CD8 bright (CM CD8br) T cells, CX3CR1 expression on monocytes, and the percentage of HLA-DR + NK cells within the NK cell subset. Sensitivity assessments that excluded significant heterogeneity and pleiotropy confirmed the stability of these associations, thereby reinforcing the validity of our findings.ConclusionThis study provides novel evidence of the potential causal impact of specific immune cells on the risk of developing endometriosis. These findings enhance our understanding of endometriosis pathophysiology and may inform innovative approaches for its diagnosis and management. While our findings provide novel insights, limitations such as potential horizontal pleiotropy and reliance on European ancestry data should be considered. Future research should expand to diverse populations and incorporate individual-level data to refine these findings.

Agricultural Economic Water Productivity Differences across Counties in the Colorado River Basin

This study estimates the relative contribution of different factors to the wide variation in agricultural economic water productivity (EWP) across Colorado River Basin counties. It updates EWP measures for Basin counties using more detailed, localized data for the Colorado River mainstem. Using the Schwarz Bayesian Information Criterion for variable selection, regression analysis and productivity accounting methods identified factors contributing to EWP differences. The EWP was USD 1033 (USD 2023)/acre foot (af) for Lower Basin Counties on the U.S.–Mexico Border, USD 729 (USD 2023)/af for other Lower Basin Counties, and USD 168 (USD 2023)/af for Upper Basin Counties. Adoption rates for improved irrigation technologies showed little inter-county variation and so did not have a statistically significant impact on EWP. Counties with the lowest EWP consumed 25% of the Basin’s agricultural water (>2.3 million af) to generate 3% of the Basin’s crop revenue. Low populations/remoteness and more irrigated acreage per farm were negatively associated with EWP. Warmer winter temperatures and greater July humidity were positively associated with EWP. When controlling for other factors, being on the Border increased a county’s EWP by USD 570 (2023 USD)/af. Border Counties have greater access to labor from Mexico, enabling greater production of high-value, labor-intensive specialty crops.

Causal role of immune cells in uveitis: a Mendelian randomization study.

Uveitis refers to a group inflammation affecting the uvea, retina, retinal blood vessels as well as vitreous body, which is one of the common causes of blindness. There is growing evidence linking different types of immune cells to uveitis, although it remains uncertain if these associations imply causal relationships. Recent advancements in high-density genetic markers like SNPs or CNVs for genotyping, along with the progress in genome-wide association studies (GWAS) technologies, have improved our understanding of the immunological mechanisms involved in ocular diseases. Therefore, our objective was to investigate the potential causal link between immune cells and uveitis using a Mendelian randomization study. The exposure and outcome GWAS data for this study were sourced from an open-access database (https://gwas.mrcieu.ac.uk/). Two-sample MR analysis was utilized to evaluate the causal relationship between 731 immune cell features and uveitis. Various MR methods were employed to reduce bias and obtain dependable estimates of the causal link between the immune cell variables and the outcomes. Instrumental variable selection criteria were carefully chosen to enhance the accuracy and efficacy of the causal relationship between different immune cell types and the risk of uveitis. Using two-sample MR, IVW modeling showed that GAD had significant effect on immunophenotypes. CD3 levels on CD45RA- CD4+ T cells (OR = 1.087, 95%CI = 1.029 ~ 1.147, p = 0.003) and CD3 levels on CM CD4+ T cells (OR = 1.086, 95%CI = 1.033 ~ 1.141, p = 0.001) were found to be elevated in cases of uveitis. HLA DR levels in CD14- CD16+ monocyte cells (OR = 0.735, 95% CI = 0.635 ~ 0.850, p < 0.001) and HLA DR levels in NK cells (OR = 0.910, 95% CI = 0.851 ~ 0.972, p = 0.005) were observed to be reduced in individuals with uveitis. Furthermore, Two cells were identified to be significantly associated with uveitis risk: HLA DR on in NK cells (OR = 0.938, 95%CI = 0.899 ~ 0.979, p = 0.003), HLA DR on CD14- CD16+ monocytes (OR = 0.924, 95%CI = 0.878 ~ 0.972, p = 0.002). This study highlights the intricate relationship between immune cells and generalized anxiety disorder using genetic methods, offering valuable insights for future clinical investigations.

How to develop, validate, and update clinical prediction models using multinomial logistic regression

ObjectivesMulticategory prediction models (MPMs) can be used in health care when the primary outcome of interest has more than two categories. The application of MPMs is scarce, possibly due to added methodological complexities compared to binary outcome models. We provide a guide of how to develop, validate, and update clinical prediction models based on multinomial logistic regression. Study Design and SettingWe present guidance and recommendations based on recent methodological literature, illustrated by a previously developed and validated MPM for treatment outcomes in rheumatoid arthritis. Prediction models using multinomial logistic regression can be developed for nominal outcomes, but also for ordinal outcomes. This article is intended to supplement existing general guidance on prediction model research. ResultsThis guide is split into three parts: 1) outcome definition and variable selection, 2) model development, and 3) model evaluation (including performance assessment, internal and external validation, and model recalibration). We outline how to evaluate and interpret the predictive performance of MPMs. R code is provided. ConclusionWe recommend the application of MPMs in clinical settings where the prediction of a multicategory outcome is of interest. Future methodological research could focus on MPM-specific considerations for variable selection and sample size criteria for external validation.

Comparison of Non-Convex Variable Selection Criteria in High-dimensional Data with Count Response

Comparison of Non-Convex Variable Selection Criteria in High-dimensional Data with Count Response

Metabolomic insights into pulmonary fibrosis: a mendelian randomization study

BackgroundThis study leverages a two-sample Mendelian Randomization (MR) approach to explore the causal relationships between 1,400 metabolites and pulmonary fibrosis, using genetic variation as instrumental variables. By adhering to stringent criteria for instrumental variable selection, the research aims to uncover metabolic pathways that may influence the risk and progression of pulmonary fibrosis, providing insights into potential therapeutic targets.MethodsUtilizing data from the OpenGWAS project, which includes a significant European cohort, and metabolite GWAS data from the Canadian Longitudinal Aging Study (CLSA), the study employs advanced statistical methods. These include inverse variance weighting (IVW), weighted median estimations, and comprehensive sensitivity analyses conducted using the R software environment to ensure the robustness of the causal inferences.ResultsThe study identified 62 metabolites with significant causal relationships with pulmonary fibrosis, highlighting both risk-enhancing and protective metabolic factors. This extensive list of metabolites presents a broad spectrum of potential therapeutic targets and biomarkers for early detection, underscoring the metabolic complexity underlying pulmonary fibrosis.ConclusionsThe findings from this MR study significantly advance our understanding of the metabolic underpinnings of pulmonary fibrosis, suggesting that alterations in specific metabolites could influence the risk and progression of the disease. These insights pave the way for the development of novel diagnostic and therapeutic strategies, emphasizing the potential of metabolic modulation in managing pulmonary fibrosis.

Application of nonparametric quantifiers for online handwritten signature verification: A statistical learning approach

AbstractThis work explores the use of nonparametric quantifiers in the signature verification problem of handwritten signatures. We used the MCYT‐100 (MCYT Fingerprint subcorpus) database, widely used in signature verification problems. The discrete‐time sequence positions in the x ‐axis and y‐axis provided in the database are preprocessed, and time causal information based on nonparametric quantifiers such as entropy, complexity, Fisher information, and trend are employed. The study also proposes to evaluate these quantifiers with the time series obtained, applying the first and second derivatives of each sequence position to evaluate the dynamic behavior by looking at their velocity and acceleration regimes, respectively. The signatures in the MCYT‐100 database are classified via Logistic Regression, Support Vector Machines (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). The quantifiers were used as input features to train the classifiers. To assess the ability and impact of nonparametric quantifiers to distinguish forgery and genuine signatures, we used variable selection criteria, such as: information gain, analysis of variance, and variance inflation factor. The performance of classifiers was evaluated by measures of classification error such as specificity and area under the curve. The results show that the SVM and XGBoost classifiers present the best performance.

Variable selection in Poisson regression model based on chaotic meta-heuristic search algorithm

By determining the most significant variables that are connected to the response variable, Increasing prediction accuracy and processing speed can be achieved through the process of variable selection. Regression modeling has drawn a lot of interest from several scientific domains. One of the most effective nature-inspired algorithms that has been suggested recently and can be used effectively for variable selection is the Firefly algorithm. The chaotic firefly algorithm is presented in this work to carry out the Poisson regression model's variable selection. A simulation study is carried out to assess how well the suggested strategy performs in terms of variable selection criteria and prediction accuracy. Its effectiveness is also contrasted with alternative approaches. The outcomes demonstrated the effectiveness of our suggested strategies, which beat other widely used approaches.

Central Compartment Neck Dissection in Laryngeal and Hypopharyngeal Squamous Cell Carcinoma: Clinical Considerations.

Metastatic lymph node involvement represents the most relevant prognostic factor in head and neck squamous cell carcinomas (HNSCCs), invariably affecting overall survival, disease-specific survival, and relapse-free survival. Among HNSCCs, laryngeal and hypopharyngeal cancers are known to be at highest risk to metastasize to the central neck compartment (CNC). However, prevalence and prognostic implications related to the CNC involvement are not well defined yet, and controversies still exist regarding the occult metastasis rate. Guidelines for the management of CNC in laryngeal and hypopharyngeal cancers are vague, resulting in highly variable selection criteria for the central neck dissection among different surgeons and institutions. With this review, the authors intend to reappraise the existing data related to the involvement of CNC in laryngeal and hypopharyngeal malignancies, in the attempt to define the principles of management while highlighting the debated aspects that are lacking in evidence and consensus. Furthermore, as definition and boundaries of the CNC have changed over the years, an up-to-date anatomical-surgical description of the CNC is provided.

Information-Theoretic Principal Variable Selection Criterion and Its Properties

Information-Theoretic Principal Variable Selection Criterion and Its Properties

Parameters influencing pedestrian injury and severity – A systematic review and meta-analysis

Pedestrians account for 26% of all traffic fatalities worldwide. According to in-depth collision databases, around 3500 variables can affect the outcome of a collision, making it crucial to establish the relationship between each variable and the outcome. To date, there is no method defined to assess these variables' relevance other than a statistical correlation, which can sometimes lead to reasonable conclusions, but only under specific circumstances. This article addresses this issue by first conducting a literature review to determine all relevant variables, followed by developing a variable selection criterion to select crucial variables, and then conducting a meta-analysis to quantify these relationships. Epidemiological studies published between 1990 and 2022 were examined, including 93 papers from 19 different nations that considered 904,655 pedestrian collisions. Of the 204 variables that were extracted from these studies, 152 were examined using the variable selection criterion, and 68 were found to be significant. Of these, 20 were included in the meta-analysis, which combined odds ratios to aggregate the effect of a variable across various studies, thus removing study-specific conclusions. This study makes a compelling argument that using statistical correlation by itself is insufficient to determine a variable's significance. The proposed method is an objective way to distinguish the variables for stakeholders and identify their relevance. This study offers a definitive list of the 15 characteristics that must be present in any pedestrian-to-vehicle collision databases, as well as a list of 53 variables that require additional investigation, allowing for appropriate actions for safer roads.

Development and validation of a prediction model for early mortality after transcatheter aortic valve implantation (TAVI) based on the Netherlands Heart Registration (NHR): The TAVI-NHR risk model.

The currently available mortality prediction models (MPM) have suboptimal performance when predicting early mortality (30-days) following transcatheter aortic valve implantation (TAVI) on various external populations. We developed and validated a new TAVI-MPM based on a large number of predictors with recent data from a national heart registry. We included all TAVI-patients treated in the Netherlands between 2013 and 2018, from the Netherlands Heart Registration. We used logistic-regression analysis based on the Akaike Information Criterion for variable selection. We multiply imputed missing values, but excluded variables with &gt;30% missing values. For internal validation, we used ten-fold cross-validation. For temporal (prospective) validation, we used the 2018-data set for testing. We assessed discrimination by the c-statistic, predicted probability accuracy by the Brier score, and calibration by calibration graphs, and calibration-intercept and calibration slope. We compared our new model to the updated ACC-TAVI and IRRMA MPMs on our population. We included 9144 TAVI-patients. The observed early mortality was 4.0%. The final MPM had 10 variables, including: critical-preoperative state, procedure-acuteness, body surface area, serum creatinine, and diabetes-mellitus status. The median c-statistic was 0.69 (interquartile range [IQR] 0.646-0.75). The median Brier score was 0.038 (IQR 0.038-0.040). No signs of miscalibration were observed. The c-statistic's temporal-validation was 0.71 (95% confidence intervals 0.64-0.78). Our model outperformed the updated currently available MPMs ACC-TAVI and IRRMA (p value &lt; 0.05). The new TAVI-model used additional variables and showed fair discrimination and good calibration. It outperformed the updated currently available TAVI-models on our population. The model's good calibration benefits preprocedural risk-assessment and patient counseling.

Estimation of mask effectiveness perception for small domains using multiple data sources

Abstract Understanding the impacts of pandemics on public health and related societal issues at granular levels is of great interest. COVID-19 is affecting everyone in the globe and mask wearing is one of the few precautions against it. To quantify people’s perception of mask effectiveness and to prevent the spread of COVID-19 for small areas, we use Understanding America Study’s (UAS) survey data on COVID-19 as our primary data source. Our data analysis shows that direct survey-weighted estimates for small areas could be highly unreliable. In this paper, we develop a synthetic estimation method to estimate proportions of perceived mask effectiveness for small areas using a logistic model that combines information from multiple data sources. We select our working model using an extensive data analysis facilitated by a new variable selection criterion for survey data and benchmarking ratios. We suggest a jackknife method to estimate the variance of our estimator. From our data analysis, it is evident that our proposed synthetic method outperforms the direct survey-weighted estimator with respect to commonly used evaluation measures.

Selection of Auxiliary Variables for Three-Fold Linking Models in Small Area Estimation: A Simple and Effective Method

Model-based estimation of small area means can lead to reliable estimates when the area sample sizes are small. This is accomplished by borrowing strength across related areas using models linking area means to related covariates and random area effects. The effective selection of variables to be included in the linking model is important in small area estimation. The main purpose of this paper is to extend the earlier work on variable selection for area level and two-fold subarea level models to three-fold sub-subarea models linking sub-subarea means to related covariates and random effects at the area, sub-area, and sub-subarea levels. The proposed variable selection method transforms the sub-subarea means to reduce the linking model to a standard regression model and applies commonly used criteria for variable selection, such as AIC and BIC, to the reduced model. The resulting criteria depend on the unknown sub-subarea means, which are then estimated using the sample sub-subarea means. Then, the estimated selection criteria are used for variable selection. Simulation results on the performance of the proposed variable selection method relative to methods based on area level and two-fold subarea level models are also presented.

Latent variable sdelection in multidimensional item response theory models using the expectation model selection algorithm.

The aim of latent variable selection in multidimensional item response theory (MIRT) models is to identify latent traits probed by test items of a multidimensional test. In this paper the expectation model selection (EMS) algorithm proposed by Jiang etal. (2015) is applied to minimize the Bayesian information criterion (BIC) for latent variable selection in MIRT models with a known number of latent traits. Under mild assumptions, we prove the numerical convergence of the EMS algorithm for model selection by minimizing the BIC of observed data in the presence of missing data. For the identification of MIRT models, we assume that the variances of all latent traits are unity and each latent trait has an item that is only related to it. Under this identifiability assumption, the convergence of the EMS algorithm for latent variable selection in the multidimensional two-parameter logistic (M2PL) models can be verified. We give an efficient implementation of the EMS for the M2PL models. Simulation studies show that the EMS outperforms the EM-based L1 regularization in terms of correctly selected latent variables and computation time. The EMS algorithm is applied to a real data set related to the Eysenck Personality Questionnaire.

Finding the Signal in the Noise-Interday Reliability and Seasonal Sensitivity of 84 Countermovement Jump Variables in Professional Basketball Players.

Mercer, RAJ, Russell, JL, McGuigan, LC, Coutts, AJ, Strack, DS, and McLean, BD. Finding the signal in the noise-interday reliability and seasonal sensitivity of 84 countermovement jump variables in professional basketball players. J Strength Cond Res 37(2): 394-402, 2023-This study examined the measurement characteristics of countermovement jump (CMJ) variables in basketball athletes using different variable selection criteria. Test-retest reliability (noise) and seasonal variability (signal) CMJ data were collected from 13 professional basketball athletes playing for the same club throughout 1 competitive season. Interday reliability (coefficient of variation [CV] and intraclass correlation coefficients) were calculated over 3 preseason tests conducted on 3 consecutive days. To evaluate sensitivity, signal-to-noise ratio (SNR) was calculated by dividing seasonal variability (CV) from 8 in-season CMJ tests (collected from November to February) by preseason reliability (CV). Players performed 3 CMJs each testing day, and 3 data analysis techniques were applied: a single variable from the trial with either the best jump height (BestJH; calculated by flight time) or the best flight time to contraction time (BestFT:CT) and mean output across 3 jumps (Mean3). Mean3 was the most reliable data analysis technique, with 79 and 82 of 84 variables displaying lower interday CVs compared with BestJH and BestFT:CT, respectively. Overall, many CMJ measures display seasonal changes that are greater than the inherent noise, with 77 variables producing SNR of >1.00 for Mean3 compared with 65 and 58 variables for BestJH and BestFT:CT, respectively. To improve reliability and sensitivity, it is recommended that practitioners use the average of multiple CMJ trials and regularly reassess measurement characteristics specific to their cohort and environment.

A Combined Strategy of Improved Variable Selection and Ensemble Algorithm to Map the Growing Stem Volume of Planted Coniferous Forest

Remote sensing technology is becoming mainstream for mapping the growing stem volume (GSV) and overcoming the shortage of traditional labor-consumed approaches. Naturally, the GSV estimation accuracy utilizing remote sensing imagery is highly related to the variable selection methods and algorithms. Thus, to reduce the uncertainty caused by variables and models, this paper proposes a combined strategy involving improved variable selection with the collinearity test and the secondary ensemble algorithm to obtain the optimally combined variables and extract a reliable GSV from several base models. Our study extracted four types of alternative variables from the Sentinel-1A and Sentinel-2A image datasets, including vegetation indices, spectral reflectance variables, backscattering coefficients, and texture features. Then, an improved variable selection criterion with the collinearity test was developed and evaluated based on machine learning algorithms (classification and regression trees (CART), k-nearest neighbors (KNN), support vector regression (SVR), and artificial neural network (ANN)) considering the correlation between variables and GSV (with random forest (RF), distance correlation coefficient (DC), maximal information coefficient (MIC), and Pearson correlation coefficient (PCC) as evaluation metrics), and the collinearity among the variables. Additionally, we proposed a secondary ensemble with an improved weighted average approach (IWA) to estimate the reliable forest GSV using the first ensemble models constructed by Bagging and AdaBoost. The experimental results demonstrated that the proposed variable selection criterion efficiently obtained the optimal combined variable set without affecting the forest GSV mapping accuracy. Specifically, considering the first ensemble, the relative root mean square error (rRMSE) values ranged from 21.91% to 30.28% for Bagging and 23.33% to 31.49% for AdaBoost, respectively. After the secondary ensemble involving the IWA, the rRMSE values ranged from 18.89% to 21.34%. Furthermore, the variance of the GSV mapped by the secondary ensemble with various ranking methods was significantly reduced. The results prove that the proposed combined strategy has great potential to reduce the GSV mapping uncertainty imposed by current variable selection approaches and algorithms.

Regularization and variable selection in Heckman selection model

Sample selection arises when the outcome of interest is partially observed in a study. A common challenge is the requirement for exclusion restrictions. That is, some of the covariates affecting missingness mechanism do not affect the outcome. The drive to establish this requirement often leads to the inclusion of irrelevant variables in the model. A suboptimal solution is the use of classical variable selection criteria such as AIC and BIC, and traditional variable selection procedures such as stepwise selection. These methods are unstable when there is limited expert knowledge about the variables to include in the model. To address this, we propose the use of adaptive Lasso for variable selection and parameter estimation in both the selection and outcome submodels simultaneously in the absence of exclusion restrictions. By using the maximum likelihood estimator of the sample selection model, we constructed a loss function similar to the least squares regression problem up to a constant, and minimized its penalized version using an efficient algorithm. We show that the estimator, with proper choice of regularization parameter, is consistent and possesses the oracle properties. The method is compared to Lasso and adaptively weighted L_{1} penalized Two-step method. We applied the methods to the well-known Ambulatory Expenditure Data.