Smoothly Clipped Absolute Deviation Research Articles

Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets.

Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clustering method that shows promise in detecting heterogeneous relationships between molecular markers and a continuous phenotype of interest. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings. We calculated the true positive rate (TPR), true negative rate (TNR), IC and clustering accuracy of our proposed modifications, benchmarked against the existing CSMR algorithm, using an extensive set of simulation studies and real biological datasets. Our results demonstrated that substituting Adaptive-Lasso within the existing feature selection method used in CSMR led to significantly improved IC and clustering accuracy, with strong performance even in high-dimensional scenarios. In conclusion, our modifications of the CSMR method resulted in improved clustering performance and may thus serve as viable alternatives for the regression-based clustering of high-dimensional datasets.

Variational mode decomposition for surface and intramuscular EMG signal denoising

Electromyographic signals contaminated with noise during the acquisition process affect the results of follow-up applications such as disease diagnosis, motion recognition, gesture recognition, and human–computer interaction. This paper proposes a denoising technique based on the variational mode decomposition (VMD) for both surface electromyography signals (sEMG) and intramuscular electromyography signals (iEMG). sEMG and iEMG obtained from 5 healthy subjects were first decomposed using VMD into respective variational mode functions (VMFs), then thresholds were set to remove the noise, and finally, the denoised signal was reconstructed. The denoising efficacy of interval thresholding (IT) and iterative interval thresholding (IIT) techniques in combination with SOFT, HARD, and smoothly clipped absolute deviation (SCAD) thresholding operators was quantitatively evaluated by using Signal to Noise Ratio (SNR) and further statistically validated by Friedman test. The results demonstrated that IIT provides better SNR values than IT at all noise levels (P-value < 0.05) for sEMG signals. For iEMG, IIT outperformed IT at 0db and 5db noise levels, but at a noise level of 10db and 15db, IT outperformed IIT. However, the results for the 10db noise level were statistically insignificant. The SOFT thresholding operator outperforms HARD and SCAD at all noise levels for sEMG, as well as iEMG (P-value < 0.05). The study demonstrates that the combination of the IIT thresholding technique with the VMD-based SOFT thresholding operator yields the best denoising results while retaining the original signal characteristics. The proposed method can be used in the fields of disease diagnosis, pattern recognition, and movement classification.

Sparse Estimation in Finite Mixture of Accelerated Failure Time and Mixture of Regression Models with R Package fmrs

Variable selection in large-dimensional data has been extensively studied in different settings over the past decades. In a recent article, Shokoohi et. al. [29, DOI:10.1214/18-AOAS1198] proposed a method for variable selection in finite mixture of accelerated failure time regression models for studies on time-to-event data to capture heterogeneity within the population and account for censoring. In this paper, we introduce the fmrs package, which implements the variable selection methodology for such models. Furthermore, as a byproduct, the fmrs package facilitates variable selection in finite mixture regression models. The package also incorporates a tuning parameter selection mechanism based on component-wise bic. Commonly used penalties, such as Least Absolute Shrinkage and Selection Operator, and Smoothly Clipped Absolute Deviation, are integrated into fmrs. Additionally, the package offers an option for non-mixture regression models. The C language is chosen to boost the optimization speed. We provide an overview of the fmrs principles and the strategies employed for optimization. Hands-on illustrations are presented to help users get acquainted with fmrs. Finally, we apply fmrs to a lung cancer dataset and observe that a two-component mixture model reveals a subgroup with a more aggressive form of the disease, displaying a lower survival time.

The impact of temporal lobe epilepsy surgery on picture naming and its relationship to network metric change

BackgroundAnterior temporal lobe resection (ATLR) is a successful treatment for medically-refractory temporal lobe epilepsy (TLE). In the language-dominant hemisphere, 30%- 50% of individuals experience a naming decline which can impact upon daily life. Measures of structural networks are associated with language performance pre-operatively. It is unclear if analysis of network measures may predict post-operative decline. MethodsWhite matter fibre tractography was performed on preoperative diffusion MRI of 44 left lateralised and left resection individuals with TLE to reconstruct the preoperative structural network. Resection masks, drawn on co-registered pre- and post-operative T1-weighted MRI scans, were used as exclusion regions on pre-operative tractography to estimate the post-operative network. Changes in graph theory metrics, cortical strength, betweenness centrality, and clustering coefficient were generated by comparing the estimated pre- and post-operative networks. These were thresholded based on the presence of the connection in each patient, ranging from 75% to 100% in steps of 5%. The average graph theory metric across thresholds was taken.We incorporated leave-one-out cross-validation with smoothly clipped absolute deviation (SCAD) least absolute shrinkage and selection operator (LASSO) feature selection and a support vector classifier to assess graph theory metrics on picture naming decline. Picture naming was assessed via the Graded Naming Test preoperatively and at 3 and 12 months post-operatively and the outcome was classified using the reliable change index (RCI) to identify clinically significant decline. The best feature combination and model was selected using the area under the curve (AUC). The sensitivity, specificity and F1-score were also reported. Permutation testing was performed to assess the machine learning model and selected regions difference significance. ResultsA combination of clinical and graph theory metrics were able to classify outcome of picture naming at 3 months with an AUC of 0.84. At 12 months, change in strength to cortical regions was best able to correctly classify outcome with an AUC of 0.86. Longitudinal analysis revealed that betweenness centrality was the best metric to identify patients who declined at 3 months, who will then continue to experience decline from 3 to 12 months. Both models were significantly higher AUC values than a random classifier. ConclusionOur results suggest that inferred changes of network integrity were able to correctly classify picture naming decline after ATLR. These measures may be used to prospectively to identify patients who are at risk of picture naming decline after surgery and could potentially be utilised to assist tailoring the resection in order to prevent this decline.

Ridge regression coupled with a new uninformative variable elimination algorithm as a new descriptor screening method: Application of data reduction in QSAR study of some sulfonated derivatives as c-Met inhibitors

An uninformative variable elimination algorithm was combined with the Ridge regression method. This combination makes the penalized Ridge method, which is essentially incapable of deleting or selecting a variable, an efficient descriptor screening method for screening of descriptors in QSAR studies. The importance of descriptors was used to identify uninformative and redundant descriptors. The descriptor importance was defined as the ratio of the mean coefficients to the standard deviation of the coefficients derived from the leave-one-out (LOO) Ridge models. Then, with an iterative algorithm, among 392 molecular descriptors obtained after initial preprocessing, 358 uninformative descriptors were identified and removed from the data set. The remaining 34 descriptors were used for further variable selection and modeling through the smoothly clipped absolute deviation (SCAD) method. The proposed screening method was used to screen and reduce the size of QSAR data containing 63 compounds as c-Met inhibitors with anti-cancer activity. For comparison, the SCAD models were constructed using original high-dimensional data (SCAD), and the reduced-size data via the UVE-Ridge screening method (UVE-Ridge-SCAD). Both models were used to predict biological activities (pIC50) for compounds in the external test set. The coefficients of determination (R2) and the FIT parameter for the test data predicted using the SCAD model (with 12 selected descriptors) were equal to 0.8042 and 3.78, respectively. The UVE-Ridge-SCAD model (with 9 descriptors) showed R2 and FIT values of 0.9025 and 4.38, respectively. Higher R2 and FIT values indicate the excellent predictive power of the UVE-Ridge-SCAD model. Finally, the UVE-Ridge-SCAD model was used to suggest new high-potency compounds. The accuracy of the proposed compounds was investigated using ligand-receptor interactions.

UAMPnet: Unrolled approximate message passing network for nonconvex regularization

Deep neural networks and model-based methods are both popular for their wide and great success in many inference problems. In this paper, resorting to deep learning, we study the efficient algorithms for two popular nonconvex regularization methods, smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). Approximate message passing (AMP) algorithm can be effective to optimize nonconvex regularization models. First, we unroll the AMP-based algorithm as the feed-forward neural network through leveraging the novel activation functions of neurons, dubbed as Unrolled-AMP. And then, for the case where the measurement matrix deviates from the i.i.d. Gaussian distribution, we propose two improved iterative algorithms based on the “Vector AMP (VAMP)” algorithm to solve the nonconvex regularization methods. Further, we unroll them as the feed-forward neural network, dubbed as Unrolled-VAMP. These two novel neural network architectures use a back-propagation algorithm to learn all their parameters from the training data. Finally, the convergence of Unrolled-AMP algorithm is analyzed, and the efficiency of the proposed networks is demonstrated through experiments on sparse signal reconstruction and 5G wireless communication.

ABPO-TVSCAD: alternating Bregman proximity operators approach based on TVSCAD regularization for bioluminescence tomography

Objective. Bioluminescence tomography (BLT) is a promising non-invasive optical medical imaging technique, which can visualize and quantitatively analyze the distribution of tumor cells in living tissues. However, due to the influence of photon scattering effect and ill-conditioned inverse problem, the reconstruction result is unsatisfactory. The purpose of this study is to improve the reconstruction performance of BLT. Approach. An alternating Bregman proximity operators (ABPO) method based on TVSCAD regularization is proposed for BLT reconstruction. TVSCAD combines the anisotropic total variation (TV) regularization constraints and the non-convex smoothly clipped absolute deviation (SCAD) penalty constraints, to make a trade-off between the sparsity and edge preservation of the source. ABPO approach is used to solve the TVSCAD model (ABPO-TVSCAD for short). In addition, to accelerate the convergence speed of the ABPO, we adapt the strategy of shrinking the permission source region, which further improves the performance of ABPO-TVSCAD. Main results. The results of numerical simulations and in vivo xenograft mouse experiment show that our proposed method achieved superior accuracy in spatial localization and morphological reconstruction of bioluminescent source. Significance. ABPO-TVSCAD is an effective and robust reconstruction method for BLT, and we hope that this method can promote the development of optical molecular tomography.

Development and validation of an extended Cox prognostic model for patients with ER/PR+ and HER2− breast cancer: a retrospective cohort study

BackgroundThe purpose of this study was to explore a new estrogen receptor (ER) and/or progesterone receptor (PR)+ and human epidermal growth factor receptor 2 (HER2)− breast cancer prognostic model, called the extended Cox prognostic model, for determining the cutoff values for multiple continuous prognostic factors and their interaction via the new model concept and variable selection method.MethodsA total of 335 patients with ER/PR+ and HER2− breast cancer were enrolled for the final analysis. The primary endpoint was breast cancer-specific mortality (BCSM). Prognostic factors (histological grade, histological type, stage, T, N, lymphovascular invasion (LVI), P53, Ki67, ER, PR, and age) were included in this study. The four continuous variables (Ki67, ER, PR, and age) were partitioned into a series of binary variables that were fitted in the multivariate Cox analysis. A smoothly clipped absolute deviation (SCAD) variable selection method was used. Model performance was expressed in discrimination and calibration.ResultsWe developed an extended Cox model with a time threshold of 164-week (more than 3 years) postoperation and developed a user-friendly nomogram based on our extended Cox model to facilitate clinical application. We found that the cutoff values for PR, Ki67, and age were 20%, 60%, and 41–55 years, respectively. There was an interaction between age and PR for patients aged ≥ 41 years and PR ≥ 20% at 164-week postoperation: the older the patients with ER/PR+, HER2−, and PR ≥ 20% were, the lower the survival and more likely to recur and metastasize exceeding 164 weeks (more than 3 years) after surgery.ConclusionsOur study offers guidance on the prognosis of patients with ER/PR+ and HER2− breast cancer in China. The new concept can inform modeling and the determination of cutoff values of prognostic factors in the future.

An inertial proximal partially symmetric ADMM-based algorithm for linearly constrained multi-block nonconvex optimization problems with applications

The alternating direction method of multipliers (ADMM) is an efficient splitting method for solving separable optimization with linear constraints. In this paper, an inertial proximal partially symmetric ADMM is proposed for solving linearly constrained multi-block nonconvex separable optimization, which can improve the computational efficiency by considering the ideas of inertial proximal point and linearization technique. Meanwhile, the proposed method updates the Lagrange multiplier twice and considers different relaxation factors in each iteration. Under the assumption that the generated sequence is bounded and the auxiliary function satisfies the Kurdyka-Łojasiewicz property, the global convergence of the proposed method with a more relaxed parameter range is analyzed. Moreover, some numerical results on SCAD (for smoothly clipped absolute deviation), image processing and robust PCA nonconvex problems are reported to demonstrate the efficiency and superiority of the proposed method.

Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms

The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.5-T MRI scanner. All images were segmented by two expert radiologists (in consensus). Prior to extraction of radiomics features, all MR images were resampled to an isotropic voxel size of 1.8 × 1.8 × 1.8 mm3. Subsequently, intensities were quantized to 64 discretized gray levels and a total of 93 features were extracted. The applied algorithms included a smoothly clipped absolute deviation (SCAD)–penalized support vector machine (SVM) and the recursive partitioning (RP) algorithm as a robust classifier for binary classification in this high-dimensional and non-sparse data. All models were validated with repeated fivefold cross-validation and 10,000 bootstrapping resamples. Ten and seven features were selected with SCAD-penalized SVM and RP algorithm, respectively, for CABG responder/non-responder classification. Considering univariate analysis, the GLSZM gray-level non-uniformity-normalized feature achieved the best performance (AUC: 0.62, 95% CI: 0.53–0.76) with SCAD-penalized SVM. Regarding multivariable modeling, SCAD-penalized SVM obtained an AUC of 0.784 (95% CI: 0.64–0.92), whereas the RP algorithm achieved an AUC of 0.654 (95% CI: 0.50–0.82). In conclusion, different radiomics texture features alone or combined in multivariate analysis using machine learning algorithms provide prognostic information regarding myocardial function in patients after CABG.

A global two-stage algorithm for non-convex penalized high-dimensional linear regression problems

By the asymptotic oracle property, non-convex penalties represented by minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) have attracted much attentions in high-dimensional data analysis, and have been widely used in signal processing, image restoration, matrix estimation, etc. However, in view of their non-convex and non-smooth characteristics, they are computationally challenging. Almost all existing algorithms converge locally, and the proper selection of initial values is crucial. Therefore, in actual operation, they often combine a warm-starting technique to meet the rigid requirement that the initial value must be sufficiently close to the optimal solution of the corresponding problem. In this paper, based on the DC (difference of convex functions) property of MCP and SCAD penalties, we aim to design a global two-stage algorithm for the high-dimensional least squares linear regression problems. A key idea for making the proposed algorithm to be efficient is to use the primal dual active set with continuation (PDASC) method to solve the corresponding sub-problems. Theoretically, we not only prove the global convergence of the proposed algorithm, but also verify that the generated iterative sequence converges to a d-stationary point. In terms of computational performance, the abundant research of simulation and real data show that the algorithm in this paper is superior to the latest SSN method and the classic coordinate descent (CD) algorithm for solving non-convex penalized high-dimensional linear regression problems.

Model selection for functional linear regression with hierarchical structure

Scalar-on-function regression allows for a scalar response to be dependent on functional predictors; however, not much work has been done when interaction effects between the functional predictors are included. In this paper, we introduce a multiple functional linear regression model with interaction terms. Meanwhile, we enforce the hierarchical structure constraint on the model, that is, interaction terms can be selected into the model only if the associated main effects are in the model. Based on the functional principal component analysis and group smoothly clipped absolute deviation (SCAD) penalty, we propose a new penalized estimation procedure to select the important functional predictors and interactions while automatically obeying the hierarchical structure. With appropriate selection of the tuning parameters, the rates of convergence of the proposed estimators and the consistency of the model selection procedure are established under some regularity conditions. At last, we illustrate the finite sample performance of our proposed methods with some simulation studies and a real data application.

Potential of Inflammatory Protein Signatures for Enhanced Selection of People for Lung Cancer Screening.

Simple SummarySelection of appropriate high-risk smokers is one of the major challenges for implementing low-dose computed tomography screening for lung cancer. Many lung cancer risk prediction models have been proposed to supplement lung cancer screening. This study evaluated the potential of inflammatory protein markers to enhance lung cancer risk stratification beyond lung cancer risk models. The addition of inflammatory protein markers to existing lung cancer risk models improved risk prediction. The inflammatory protein markers may enhance current risk stratification and may be useful to identify high-risk populations for lung cancer screening.Randomized trials have demonstrated a substantial reduction in lung cancer (LC) mortality by screening heavy smokers with low-dose computed tomography (LDCT). The aim of this study was to assess if and to what extent blood-based inflammatory protein biomarkers might enhance selection of those at highest risk for LC screening. Ever smoking participants were chosen from 9940 participants, aged 50–75 years, who were followed up with respect to LC incidence for 17 years in a prospective population-based cohort study conducted in Saarland, Germany. Using proximity extension assay, 92 inflammation protein biomarkers were measured in baseline plasma samples of ever smoking participants, including 172 incident LC cases and 285 randomly selected participants free of LC. Smoothly clipped absolute deviation (SCAD) penalized regression with 0.632+ bootstrap for correction of overoptimism was applied to derive an inflammation protein biomarker score (INS) and a combined INS-pack-years score in a training set, and algorithms were further evaluated in an independent validation set. Furthermore, the performances of nine LC risk prediction models individually and in combination with inflammatory plasma protein biomarkers for predicting LC incidence were comparatively evaluated. The combined INS-pack-years score predicted LC incidence with area under the curves (AUCs) of 0.811 and 0.782 in the training and the validation sets, respectively. The addition of inflammatory plasma protein biomarkers to established nine LC risk models increased the AUCs up to 0.121 and 0.070 among ever smoking participants from training and validation sets, respectively. Our results suggest that inflammatory protein biomarkers may have potential to improve the selection of people for LC screening and thereby enhance screening efficiency.

Patch-based weighted SCAD prior for compressive sensing

The nuclear norm-based convex surrogate of the rank function has been widely used in compressive sensing (CS) to exploit the sparsity of nonlocal similar patches in an image. However, this method treats different singular values equally and thus may produce a result far from the optimum one. In order to alleviate the limitations of the nuclear norm, different singular values should be treated differently. The reason is that large singular values can be used to retrieve substantial contents of an image, while small ones may contain noisy information. In this paper, we propose a model via non-convex weighted Smoothly Clipped Absolute Deviation (SCAD) prior. Our motivation is that SCAD shrinkage behaves like a soft shrinkage operator for small enough inputs, whereas for large enough ones, it leaves the input intact and behaves like hard shrinkage. For moderate input values, SCAD makes a good balance between soft shrinkage and hard shrinkage. Numerically, the alternating direction method of multiplier (ADMM) is adopted to split the original problem into several sub-problems with closed-form solutions. We further analyze the convergence of the proposed method under mild conditions. Various experimental results demonstrate that the proposed model outperforms many existing state-of-the-art CS methods.

Evaluating the Performance of Feature Selection Methods Using Huge Big Data: A Monte Carlo Simulation Approach

In this article, we compare autometrics and machine learning techniques including Minimax Concave Penalty (MCP), Elastic Smoothly Clipped Absolute Deviation (E-SCAD), and Adaptive Elastic Net (AEnet). For simulation experiments, three kinds of scenarios are considered by allowing the multicollinearity, heteroscedasticity, and autocorrelation conditions with varying sample sizes and the varied number of covariates. We found that all methods show improved their performance for a large sample size. In the presence of low and moderate multicollinearity and low and moderate autocorrelation, the considered methods retain all relevant variables. However, for low and moderate multicollinearity, excluding AEnet, all methods keep many irrelevant predictors as well. In contrast, under low and moderate autocorrelation, along with AEnet, the Autometrics retain less irrelevant predictors. Considering the case of extreme multicollinearity, AEnet retains more than 93 percent correct variables with an outstanding gauge (zero percent). However, the potency of remaining techniques, specifically MCP and E-SCAD, tends towards unity with augmenting sample size but capturing massive irrelevant predictors. Similarly, in case of high autocorrelation, E-SCAD has shown good performance in the selection of relevant variables for a small sample, while in gauge, Autometrics and AEnet are performed better and often retained less than 5 percent irrelevant variables. In the presence of heteroscedasticity, all techniques often hold all relevant variables but also suffer from overspecification problems except AEnet and Autometrics which circumvent the irrelevant predictors and establish the true model precisely. For an empirical application, we take into account the workers’ remittance data for Pakistan along its twenty-seven determinants spanning from 1972 to 2020 for Pakistan. The AEnet selected thirteen relevant covariates of workers’ remittance while E-SCAD and MCP suffered from an overspecification problem. Hence, the policymakers and practitioners should focus on the relevant variables selected by AEnet to improve workers' remittance in the case of Pakistan. In this regard, the Pakistan government has devised policies that make it easy to transfer remittances legally and mitigate the cost of transferring remittances from abroad. The AEnet approach can help policymakers arrive at relevant variables in the presence of a huge set of covariates, which in turn produce accurate predictions.

A Comparison of Autometrics and Penalization Techniques under Various Error Distributions: Evidence from Monte Carlo Simulation

This work compares Autometrics with dual penalization techniques such as minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) under asymmetric error distributions such as exponential, gamma, and Frechet with varying sample sizes as well as predictors. Comprehensive simulations, based on a wide variety of scenarios, reveal that the methods considered show improved performance for increased sample size. In the case of low multicollinearity, these methods show good performance in terms of potency, but in gauge, shrinkage methods collapse, and higher gauge leads to overspecification of the models. High levels of multicollinearity adversely affect the performance of Autometrics. In contrast, shrinkage methods are robust in presence of high multicollinearity in terms of potency, but they tend to select a massive set of irrelevant variables. Moreover, we find that expanding the data mitigates the adverse impact of high multicollinearity on Autometrics rapidly and gradually corrects the gauge of shrinkage methods. For empirical application, we take the gold prices data spanning from 1981 to 2020. While comparing the forecasting performance of all selected methods, we divide the data into two parts: data over 1981–2010 are taken as training data, and those over 2011–2020 are used as testing data. All methods are trained for the training data and then are assessed for performance through the testing data. Based on a root-mean-square error and mean absolute error, Autometrics remain the best in capturing the gold prices trend and producing better forecasts than MCP and SCAD.

Jackknife model averaging for high-dimensional quantile regression.

In this paper, we propose a frequentist model averaging method for quantile regression with high-dimensional covariates. Although research on these subjects has proliferated as separate approaches, no study has considered them in conjunction. Our method entails reducing the covariate dimensions through ranking the covariates based on marginal quantile utilities. The second step of our method implements model averaging on the models containing the covariates that survive the screening of the first step. We use a delete-one cross-validation method to select the model weights, and prove that the resultant estimator possesses an optimal asymptotic property uniformly over any compact (0,1) subset of the quantile indices. Our proof, which relies on empirical process theory, is arguably more challenging than proofs of similar results in other contexts owing to the high-dimensional nature of the problem and our relaxation of the conventional assumption of the weights summing to one. Our investigation of finite-sample performance demonstrates that the proposed method exhibits very favorable properties compared to the least absolute shrinkage and selection operator (LASSO) and smoothly clipped absolute deviation (SCAD) penalized regression methods. The method is applied to a microarray gene expression dataset.

A LogTVSCAD Nonconvex Regularization Model for Image Deblurring in the Presence of Impulse Noise

This paper proposes a nonconvex model (called LogTVSCAD) for deblurring images with impulsive noises, using the log-function penalty as the regularizer and adopting the smoothly clipped absolute deviation (SCAD) function as the data-fitting term. The proposed nonconvex model can effectively overcome the poor performance of the classical TVL1 model for high-level impulsive noise. A difference of convex functions algorithm (DCA) is proposed to solve the nonconvex model. For the model subproblem, we consider the alternating direction method of multipliers (ADMM) algorithm to solve it. The global convergence is discussed based on Kurdyka–Lojasiewicz. Experimental results show the advantages of the proposed nonconvex model over existing models.

Penalized joint generalized estimating equations for longitudinal binary data.

In statistical research, variable selection and feature extraction are a typical issue. Variable selection in linear models has been fully developed, while it has received relatively little attention for longitudinal data. Since a longitudinal study involves within-subject correlations, the likelihood function of discrete longitudinal responses generally cannot be expressed in analytically closed form, and standard variable selection methods cannot be directly applied. As an alternative, the penalized generalized estimating equation (PGEE) is helpful but very likely results in incorrect variable selection if the working correlation matrix is misspecified. In many circumstances, the within-subject correlations are of interest and need to be modeled together with the mean. For longitudinal binary data, it becomes more challenging because the within-subject correlation coefficients have the so-called Fréchet-Hoeffding upper bound. In this paper, we proposed smoothly clipped absolute deviation (SCAD)-based and least absolute shrinkage and selection operator (LASSO)-based penalized joint generalized estimating equation (PJGEE) methods to simultaneously model the mean and correlations for longitudinal binary data, together with variable selection in the mean model. The estimated correlation coefficients satisfy the upper bound constraints. Simulation studies under different scenarios are made to assess the performance of the proposed method. Compared to existing PGEE methods that specify a working correlation matrix for longitudinal binary data, the proposed PJGEE method works much better in terms of variable selection consistency and parameter estimation accuracy. A real data set on Clinical Global Impression is analyzed forillustration.

Regularized Weighted Nonparametric Likelihood Approach for High-Dimension Sparse Subdistribution Hazards Model for Competing Risk Data.

Variable selection and penalized regression models in high-dimension settings have become an increasingly important topic in many disciplines. For instance, omics data are generated in biomedical researches that may be associated with survival of patients and suggest insights into disease dynamics to identify patients with worse prognosis and to improve the therapy. Analysis of high-dimensional time-to-event data in the presence of competing risks requires special modeling techniques. So far, some attempts have been made to variable selection in low- and high-dimension competing risk setting using partial likelihood-based procedures. In this paper, a weighted likelihood-based penalized approach is extended for direct variable selection under the subdistribution hazards model for high-dimensional competing risk data. The proposed method which considers a larger class of semiparametric regression models for the subdistribution allows for taking into account time-varying effects and is of particular importance, because the proportional hazards assumption may not be valid in general, especially in the high-dimension setting. Also, this model relaxes from the constraint of the ability to simultaneously model multiple cumulative incidence functions using the Fine and Gray approach. The performance/effectiveness of several penalties including minimax concave penalty (MCP); adaptive LASSO and smoothly clipped absolute deviation (SCAD) as well as their L2 counterparts were investigated through simulation studies in terms of sensitivity/specificity. The results revealed that sensitivity of all penalties were comparable, but the MCP and MCP-L2 penalties outperformed the other methods in term of selecting less noninformative variables. The practical use of the model was investigated through the analysis of genomic competing risk data obtained from patients with bladder cancer and six genes of CDC20, NCF2, SMARCAD1, RTN4, ETFDH, and SON were identified using all the methods and were significantly correlated with the subdistribution.