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   Articles with "squares regression" as a keyword



Using Quantile and Asymmetric Least Squares Regression for Optimal Risk Adjustment

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recommendations! Published in 2017 at "Health Economics"

DOI: 10.1002/hec.3352

Abstract: In this paper, we analyze optimal risk adjustment for direct risk selection (DRS). Integrating insurers' activities for risk selection into a discrete choice model of individuals' health insurance choice shows that DRS has the structure… read more here.

Keywords: risk adjustment; optimal risk; least squares; squares regression ... See more keywords

Regularized Negative Label Relaxation Least Squares Regression for Face Recognition

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recommendations! Published in 2020 at "Neural Processing Letters"

DOI: 10.1007/s11063-020-10219-6

Abstract: Least squares regression (LSR) is widely used for pattern classification. Some variants based on it try to enlarge the margin between different classes to achieve better performance. However, the large margin classifier doesn’t work well… read more here.

Keywords: label relaxation; least squares; squares regression; negative label ... See more keywords

A new model selection criterion for partial least squares regression

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recommendations! Published in 2017 at "Chemometrics and Intelligent Laboratory Systems"

DOI: 10.1016/j.chemolab.2017.08.006

Abstract: Abstract Choosing the right number of latent factors to be used in PLS regression (Partial Least Squares Regression) has been a matter of concern among users, academics and researchers. In this paper, we introduce a… read more here.

Keywords: least squares; regression; squares regression; model ... See more keywords

Partial Least-Squares Regression as a Tool to Retrieve Gas Concentrations in Mixtures Detected Using Quartz-Enhanced Photoacoustic Spectroscopy

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recommendations! Published in 2020 at "Analytical Chemistry"

DOI: 10.1021/acs.analchem.0c00075

Abstract: We report on a statistical tool based on partial least-squares regression (PLSR) able to retrieve single-component concentrations in a multiple-gas mixture characterized by spectrally overlapping absorption features. Absorption spectra of mixtures of CO–N2O and mixtures… read more here.

Keywords: least squares; partial least; gas; quartz enhanced ... See more keywords

Gasoline Quality Assessment Using Fast Gas Chromatography and Partial Least-Squares Regression for the Detection of Adulterated Gasoline

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recommendations! Published in 2018 at "Energy & Fuels"

DOI: 10.1021/acs.energyfuels.8b02368

Abstract: To find out if gasoline is adulterated, it is essential to analyze the sample’s information efficiently; however, current official test methods are time consuming and costly. Much research has been conducted to supplement these difficulties… read more here.

Keywords: gas chromatography; least squares; partial least; gasoline ... See more keywords

Compositional Characterization of Glassy Volcanic Material From VSWIR and MIR Spectra Using Partial Least Squares Regression Models

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recommendations! Published in 2024 at "Earth and Space Science"

DOI: 10.1029/2023ea003439

Abstract: The glass phase in volcanic rocks presents a challenge to obtaining compositional data from visible and short‐wave‐infrared (VSWIR) and mid‐infrared (MIR) spectral data of remote surfaces due to its amorphous structure and variable composition. Nonetheless,… read more here.

Keywords: vswir; least squares; squares regression; vswir mir ... See more keywords
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Partial least squares regression with compositional response variables and covariates

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recommendations! Published in 2020 at "Journal of Applied Statistics"

DOI: 10.1080/02664763.2020.1795813

Abstract: The common approach for regression analysis with compositional variables is to express compositions in log-ratio coordinates (coefficients) and then perform standard statistical processing in real ... read more here.

Keywords: compositional response; least squares; response variables; regression compositional ... See more keywords

Marginal Screening for Partial Least Squares Regression

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recommendations! Published in 2017 at "IEEE Access"

DOI: 10.1109/access.2017.2728532

Abstract: Partial least squares (PLS) regression is a versatile modeling approach for high-dimensional data analysis. Recently, PLS-based variable selection has attracted great attention due to high-throughput data reduction and modeling interpretability. In this paper, a class… read more here.

Keywords: marginal screening; least squares; squares regression; screening partial ... See more keywords

Generalized and Robust Least Squares Regression.

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recommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"

DOI: 10.1109/tnnls.2022.3213594

Abstract: As a simple yet effective method, least squares regression (LSR) is extensively applied for data regression and classification. Combined with sparse representation, LSR can be extended to feature selection (FS) as well, in which l1… read more here.

Keywords: loss function; classification; squares regression; generalized robust ... See more keywords

Extrinsic Least Squares Regression with Closed-Form Solution on Product Grassmann Manifold for Video-Based Recognition

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recommendations! Published in 2018 at "Mathematical Problems in Engineering"

DOI: 10.1155/2018/6598025

Abstract: Least squares regression is a fundamental tool in statistical analysis and is more effective than some complicated models with small number of training samples. Representing multidimensional data with product Grassmann manifold has recently led to… read more here.

Keywords: product grassmann; grassmann manifold; least squares; squares regression ... See more keywords

Performance evaluation of variable selection methods coupled with partial least squares regression to determine the target component in solid samples

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recommendations! Published in 2022 at "Journal of Near Infrared Spectroscopy"

DOI: 10.1177/09670335221097236

Abstract: Variable selection can improve the robustness and prediction accuracy of partial least squares (PLS) regression models and decrease the calculation time by selecting the optimal subset of variables in multivariate calibration. In this study, the… read more here.

Keywords: partial least; squares regression; selection methods; selection ... See more keywords