Sequential linear models can be adopted to describe the data where the response variable depends on lagged outcomes and fixed-effects variables. For estimation, variable selection, and high-accuracy response prediction, we propose the penalized metho...
This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sou...
Accurate feature selection is critical for machine learning in medical diagnosis, yet conventional methods often fail to capture complex non-linear relationships in biomedical data. This study introduces an advanced feature selection approach that in...
In this paper, the adaptive lasso method is used to screen variables, and different neural network models of seven countries are established by choosing variables. Gross domestic product (GDP) is a function of land area in the country, cultivated lan...
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...
Determining the predictor variables that have a non-linear effect as well as those that have a linear effect on the response variable is crucial in additive semi-parametric models. This issue has been extensively investigated by many researchers in t...
Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertaint...
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...
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...
In multivariate statistical analysis, accurate modeling of the covariance structure is critical for high-dimensional data analysis, variable selection, and regularization. In high-dimensional settings, strong inter-variable correlation and redundancy...
A significant portion of theoretical and empirical studies in high-dimensional regression have primarily concentrated on clean datasets. However, in numerous practical scenarios, data are often corrupted by missing values and measurement errors, whic...
Variable selection has been a hot topic, with various popular methods including lasso, SCAD, and elastic net. These penalized regression algorithms remain sensitive to noisy data. Furthermore, “concept drift” fundamentally distinguishes s...
This study aims to propose modified semiparametric estimators based on six different penalty and shrinkage strategies for the estimation of a right-censored semiparametric regression model. In this context, the methods used to obtain the estimators a...
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...
Penalized regression methods are widely used for variable selection. Non-negative garrote (NNG) was one of the earliest methods to combine variable selection with shrinkage of regression coefficients, followed by lasso. About a decade after the intro...
We leverage telematics data on driving behavior variables to assess driver risk and predict future insurance claims in a case study utilising a representative telematics sample. In the study, we aim to categorise drivers according to their driving ha...
Variable selection is important for developing accurate and interpretable prediction models. While classical and penalized methods are widely used, few simulation studies provide meaningful comparisons. This study compares their predictive performanc...
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,...
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...
Accurate prediction of aviation safety levels is significant for the efficient early warning and prevention of incidents. However, the causal mechanism and temporal character of aviation accidents are complex and not fully understood, which increases...
High-dimensional datasets, where the number of features far exceeds the number of observations, present significant challenges in feature selection and model performance. This study proposes a novel two-stage feature-selection approach that integrate...
This study includes a comprehensive evaluation of six penalty estimation strategies for partially linear models (PLRMs), focusing on their performance in the presence of multicollinearity and their ability to handle both parametric and nonparametric...
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...
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...
Thermal errors significantly affect the accurate performance of computer numerical control (CNC) machine tools. In this paper, an improved robust thermal error prediction approach is proposed for CNC machine tools based on the adaptive Least Absolute...
The paper develops an innovatively adaptive bi-level variable selection methodology for quantile regression models with a diverging number of covariates. Traditional variable selection techniques in quantile regression, such as the lasso and group la...
The soluble solids content (SSC) of pears is mainly composed of sugars, organic acids, and other soluble substances and is one of the important indices used to measure the sweetness and quality of pear juice. The SSC of pears is mainly composed of su...
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...
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...
Regularization methods such as LASSO, adaptive LASSO, Elastic-Net, and SCAD are widely employed for variable selection in statistical modeling. However, these methods primarily focus on variables with strong effects while often overlooking weaker sig...
Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact o...
Ensuring the accuracy of flow measurement is crucial to promoting high-quality cigarette production. In order to monitor the working status of flowmeters, this paper proposes an anomaly detection method based on the sliding-window recursive Lasso (Le...
Short-wave near-infrared (SW-NIR) spectroscopy provides a rapid and nondestructive sensing route for monitoring bread staling, but formulation-dependent moisture redistribution and starch retrogradation can make pooled spectral regression unstable. T...
Climate change presents significant challenges to agriculture worldwide, leading to food insecurity and impacting rural livelihoods. Maize farming is especially vulnerable to extreme weather, such as heavy rainfall, high temperatures, soil acidity, h...
This paper presents a computationally efficient Multivariate Mixture Model Thresholding (MMMT) technique for sparse signal denoising and recovery, with the goal of improving data quality in modern sensing and biomedical systems. The proposed method e...
In high-dimensional gene expression data analysis, the accuracy and reliability of cancer classification and selection of important genes play a very crucial role. To identify these important genes and predict future outcomes (tumor vs. non-tumor), v...
We introduce Graphical TREX (GTREX), a novel method for graph estimation in high-dimensional Gaussian graphical models. By conducting neighborhood selection with TREX, GTREX avoids tuning parameters and is adaptive to the graph topology. We compared...
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
The use of advanced pre-processing methods, feature selection, hyperparameter optimization, and ensemble learning methods in this study provides a holistic solution for breast cancer diagnosis. The technique employs the synthetic minority over-sampli...
For the autoregressive models, classical estimation methods, including the least squares estimator or the maximum likelihood estimator are not robust to heavy-tailed distributions or outliers in the dataset, and lack sparsity, leading to potentially...
Soil liquefaction is one of the most complicated phenomena to assess in geotechnical earthquake engineering. The conventional procedures developed to determine the liquefaction potential of sandy soil deposits can be categorized into three main group...
With the continuous application of spatial dependent data in various fields, spatial econometric models have attracted more and more attention. In this paper, a robust variable selection method based on exponential squared loss and adaptive lasso is...
Gravity field modeling with spherical harmonics is a fundamental task in physical geodesy. In conventional solutions, all spherical harmonic (SH) components below a prescribed maximum degree and order are typically retained, even though some componen...
Background: Prostate cancer (PCa) remains one of the most prevalent malignancies in men, with diagnostic challenges arising from the limited specificity of current biomarkers, like PSA. Improved stratification tools are essential to reduce overdiagno...
The existing fruit tree yield prediction methods mainly rely on fruit period images or long-term meteorological and soil data, which make it difficult to meet the needs of early yield prediction. In addition, the flowering period images contain compl...
Streaming data sequences arise from various areas in the era of big data, and it is challenging to explore efficient online models that adapt to them. To address the potential heterogeneity, we introduce a new online estimation procedure to analyze t...
The traditional Muskingum model has difficulty representing complex hydraulic behaviors under high-flow conditions because it relies on simplified assumptions and fixed parameters. To address the pronounced nonlinearity and non-stationarity of flood...
Lightweight day-ahead photovoltaic (PV) forecasting models encounter a significant technical challenge: under resource-constrained deployment conditions, it is difficult to simultaneously address weather-regime heterogeneity, maintain model interpret...
To achieve a more accurate assessment of water resource carrying capacity (WRCC), the indicators of water resources, social resources, and ecological environment were selected to construct the WRCC system on the basis of the combinatorial assignment...
With the widespread application of spatial data in fields like econometrics and geographic information science, the methods to enhance the robustness of spatial econometric model estimation and variable selection have become a central focus of resear...
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