This article introduces a novel two-stage variable selection method to solve the common asymmetry problem between the response variable and its influencing factors. In practical applications, we cannot correctly extract important factors from a large...
Sparsity-based methods have recently come to the foreground of damage detection applications posing a robust and efficient alternative for traditional approaches. At the same time, low-frequency inspection is known to enable global monitoring with wa...
Clustering is a powerful and efficient technique for pattern recognition which improves classification accuracy. In machine learning, it is a useful unsupervised learning approach due to its simplicity and efficiency for clustering applications. The...
The shape and properties of closed loops depend on various topological factors. One of them is loop-threading, which is present in complex lasso proteins. In this work, we analyze the probability of loop-threading by the tail and its influence on the...
Complex lasso proteins are a recently identified class of biological compounds that are present in considerable fraction of proteins with disulfide bridges. In this work, we look at complex lasso proteins as a generalization of well-known cysteine kn...
Clustering is a technique of grouping data into a homogeneous structure according to the similarity or dissimilarity measures between objects. In clustering, the fuzzy c-means (FCM) algorithm is the best-known and most commonly used method and is a f...
The emergence of multi-drug-resistant bacteria (known as superbugs) represents one of the greatest challenges for human health and modern medicine. Due to their remarkable ability to rapidly develop resistance to currently used antibiotics, new molec...
This study developed a novel acoustic emission (AE) tomography algorithm for non-destructive testing (NDT) based on Lasso regression (LASSO). The conventional AE tomography method takes considerable measurement data to obtain the elastic velocity dis...
Regularization regression techniques are widely used to overcome a model’s parameter estimation problem in the presence of multicollinearity. Several biased techniques are available in the literature, including ridge, Least Angle Shrinkage Sele...
Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides (RiPPs) and feature the threaded, lariat knot-like topology. The basic post-translational modifications (PTMs) of lasso peptide contain two steps, inc...
Least absolute deviation is proposed as a robust estimator to solve the problem when the error has an asymmetric heavy-tailed distribution or outliers. In order to be insensitive to the above situation and select the truly important variables from a...
In contemporary statistical methods, robust regression shrinkage and variable selection have gained paramount significance due to the prevalence of datasets characterized by contamination and an abundance of variables, often categorized as ‘hig...
Lasso transmission is a method for realizing long-distance flexible transmission and lightweight robots. However, there are transmission characteristic losses of velocity, force, and displacement during the motion of lasso transmission. Therefore, th...
The Clp protease system fulfills a plethora of important functions in bacteria. It consists of a tetradecameric ClpP barrel holding the proteolytic centers and two hexameric Clp-ATPase rings, which recognize, unfold, and then feed substrate proteins...
Estimation of error variance in a regression model is a fundamental problem in statistical modeling and inference. In high-dimensional linear models, variance estimation is a difficult problem, due to the issue of model selection. In this paper, we p...
The direction of arrival (DOA) estimation problem is formulated in a compressive sensing (CS) framework, and an extended array aperture is presented to increase the number of degrees of freedom of the array. The ordinary least square adaptable least...
Recent electricity price forecasting (EPF) studies suggest that the least absolute shrinkage and selection operator (LASSO) leads to well performing models that are generally better than those obtained from other variable selection schemes. By conduc...
We consider learning as an undirected graphical model from sparse data. While several efficient algorithms have been proposed for graphical lasso (GL), the alternating direction method of multipliers (ADMM) is the main approach taken concerning joint...
In order to improve the prediction accuracy of gas emission in the mining face, a method combining least absolute value convergence and selection operator (LASSO), whale optimization algorithm (WOA), and extreme gradient boosting (XGBoost) was propos...
Multiplexed cloning of long DNA sequences is a valuable technique in many biotechnology applications, such as long-read genome sequencing and the creation of open reading frame (ORF) libraries. Long-adapter single-stranded oligonucleotide (LASSO) pro...
As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is propose...
The synthesis of a peptide-containing lasso molecular switch by a self-entanglement strategy is described. The interlocked [1] rotaxane molecular machine consists of a benzometaphenylene[25]crown-8 (BMP25C8) macrocycle surrounding a molecular axle. T...
A multitude of dietary factors from dietary fat to macro and micronutrients intakes have been associated with breast cancer, yet data are still equivocal. Therefore, utilizing data from the large, multi-year, cross-sectional National Health and Nutri...
There is a growing interest in applying statistical machine learning methods, such as LASSO regression and its extensions, to analyze healthcare datasets. The existing study has examined LASSO and group LASSO regression with categorical predictors th...
The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n-1) interaction effects in a spatial autoregres...
This paper explores the leadership lessons embedded within the television series Ted Lasso, using the show as a case study to illustrate the intersection of authentic and servant leadership theories. While leadership research has often debated the di...
We investigate whether Lasso-type linear methods are able to improve the predictive accuracy of OLS in selecting relevant firm characteristics for forecasting the future cross-section of stock returns. Through extensive Monte Carlo simulations, we sh...
Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time,...
In this paper, a partially linear model based on the fused lasso method is proposed to solve the problem of high correlation between adjacent variables, and then the idea of the two-stage estimation method is used to study the solution of this model....
The use of Fibre Reinforced Cementitious Matrix (FRCM) for structural retrofitting requires prior assessment of the composite’s mechanical properties, particularly its tensile stress–strain response. This paper presents a LASSO regression...
In high-dimensional data, the performances of various classifiers are largely dependent on the selection of important features. Most of the individual classifiers with the existing feature selection (FS) methods do not perform well for highly correla...
Regional green innovation efficiency is affected by multiple factors. Based on the undesirable output of 11 provinces in the Yangtze River Economic Belt from 2012–2021, this paper uses the Super-SBM model containing undesirable outputs to measu...
In genomic studies, univariate analysis is commonly used to discover susceptible variants. It applies univariate regression for each variant and tests the significance of the regression coefficient or slope parameter. This strategy, however, may miss...
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 esti...
Traditional risk models often fail to capture extreme losses in interconnected global stock markets. This study introduces a novel approach, Expectile Regression Neural Network with Modified LASSO regularization (ERNN-mLASSO), to model nonlinear syst...
In the last three decades the vast majority of electricity price forecasting (EPF) research has concerned day-ahead markets. However, the rapid expansion of renewable generation—mostly wind and solar—have shifted the focus to intraday mar...
Spatial cluster detection is one of the focus areas of spatial analysis, whose objective is the identification of clusters from spatial distributions of point events aggregated in districts with small areas. Choi et al. (2018) formulated cluster dete...
The real-time operational safety of in-service bridges has received wide attention in recent years. By fully utilizing the health monitoring data of bridges, a structural abnormal pattern detection method based on data mining can be established to ef...
With the Lutou Forest Farm as the research area, the Lasso algorithm was used for characteristic selection, and the optimal combination of variables was input into the support vector regression (SVR) model. The most suitable SVR model was selected to...
The correlation-based network is a powerful tool to reveal the influential mechanisms and relations in stock markets. However, current methods for developing network models are dominantly based on the pairwise relationship of positive correlations. T...
COVID-19 has caused an economic crisis in the business world, leaving limitations in the continuity of the payment chain, with companies resorting to credit access. This study aimed to determine the optimal machine learning predictive model for the c...
Accurate carbon price index prediction can delve deeply into the internal law of carbon price changes, provide helpful information to managers and decision makers, as well as improve the carbon market system. Nevertheless, existing methods for combin...
The least absolute shrinkage and selection operator (LASSO) algorithm is a promising method for sparse source location in time–division multiplexing (TDM) multiple–input, multiple–output (MIMO) radar systems, with notable performanc...
Logistic regression is often used to solve classification problems. This article combines the advantages of Bayesian methods and spike-and-slab Lasso to select variables in high-dimensional logistic regression. The method of introducing a new hidden...
Amyotrophic lateral sclerosis (ALS) is a multi-system neurodegenerative disease that affects both upper and lower motor neurons, resulting from a combination of genetic, environmental, and lifestyle factors. Usually, the association between single-nu...
Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed...
Conventional breeding approaches that focus on yield under highly favorable nutrient conditions have resulted in reduced genetic and trait diversity in crops. Under the growing threat from climate change, the mining of novel genes in more resilient v...
An information matrix of a parametric model being singular at a certain true value of a parameter vector is irregular. The maximum likelihood estimator in the irregular case usually has a rate of convergence slower than the n -rate in a regula...
The incorporation of ℓ1 regularization in Lasso regression plays a crucial role by inducing convexity to the objective function, thereby facilitating its minimization; when compared to non-convex regularization, the utilization of ℓ1 regu...
In the early stages of residential project investment, accurately estimating the engineering costs of residential projects is crucial for cost control and management of the project. However, the current cost estimation of residential engineering in C...
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