Regression model under skew-normal error with applications in predicting groundwater arsenic level in the Mekong Delta Region

Huynh, Uyen; Pal, Nabendu; Nguyen, Man V.M.

doi:10.1007/s10651-021-00488-2

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Furthermore, the ridge and generalized ridge estimators in the proposed package can deal with asymmetric error distributions. With an abundance of sparse and highdimensional data [36,[40][41][42][43][44] and asymmetrically distributed data [45][46][47][48], the proposed package may provide a reliable statistical tool for statisticians and data scientists.…”

Section: Discussionmentioning

confidence: 99%

g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

Emura,

Matsumoto,

Uozumi

et al. 2024

Symmetry

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Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested that involved generalizing the uniform shrinkage of ridge regression to non-uniform shrinkage; this was shown to perform well in sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package “g.ridge” (first version published on 7 December 2023) that implements both the ridge estimator and generalized ridge estimator. The package is equipped with generalized cross-validation for the automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on the R package “glmnet”. Hence the generalized ridge estimator may be the most recommended estimator for sparse and high-dimensional models. We demonstrate the package using intracerebral hemorrhage data.

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Section: Discussionmentioning

confidence: 99%

g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

Emura,

Matsumoto,

Uozumi

et al. 2024

Symmetry

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“…Since these are highly technical tools, and requires substantial theoretical background, details are going to be omitted here. Any interested reader can see the details provided in two recent papers -Huynh et al (2021a and2021b).…”

Section: Further Improvement Of the Regression Modelmentioning

confidence: 99%

A Statistical Analysis of Vietnamese High School English Test Score Data

Duong,

Huynh,

Pal

2024

VJE

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This statistical study, perhaps the first of its kind in Vietnamese academia, has tried to understand the patterns in high school students’ pre-college national exam test scores which hold tremendous significance in students’ college or university admissions. English belongs to one of the three mandatory subjects (the other two being Mathematics and Literature) where students must be tested as part of their overall evaluation. However, since English is a foreign language for the learners, many high school students, especially those from rural areas, struggle to attain an acceptable level of proficiency. Meanwhile, a good proportion of students, particularly those in urban areas and with their own substantial financial resources, do attend extra tutorial classes to gain an edge in English proficiency. In this study we have analyzed the provincial mean and median test scores over a period of three years (2019, 2020 and 2021). It has been noted that a province’s aggregate performance depends heavily on - (i) the province’s per capita gross domestic product (PCGDP), which measures a province's economic prosperity; and (ii) the average number of students per high school (ANSHS) which indicates the overall competitive environment the students encounter which helps them sharpen their language proficiency.

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“…where b = 2 π . For more on skew normal distribution and its application, interested readers are referred to Azzalini and Valle [43], Uyen et al [44], and Shakil et al [45], among others.…”

Section: Skew Normal Distributionmentioning

confidence: 99%

New Vessel Extraction Method by Using Skew Normal Distribution for MRA Images

Bahrami,

Khamnei,

Lakestani

et al. 2024

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Vascular-related diseases pose significant public health challenges and are a leading cause of mortality and disability. Understanding the complex structure of the vascular system and its processes is crucial for addressing these issues. Recent advancements in medical imaging technology have enabled the generation of high-resolution 3D images of vascular structures, leading to a diverse array of methods for vascular extraction. While previous research has often assumed a normal distribution of image data, this paper introduces a novel vessel extraction method that utilizes the skew normal distribution for more accurate probability distribution modeling. The proposed method begins with a preprocessing step to enhance vessel structures and reduce noise in Magnetic Resonance Angiography (MRA) images. The skew normal distribution, known for its ability to model skewed data, is then employed to characterize the intensity distribution of vessels. By estimating the parameters of the skew normal distribution using the Expectation-Maximization (EM) algorithm, the method effectively separates vessel pixels from the background and non-vessel regions. To extract vessels, a thresholding technique is applied based on the estimated skew normal distribution parameters. This segmentation process enables accurate vessel extraction, particularly in detecting thin vessels and enhancing the delineation of vascular edges with low contrast. Experimental evaluations on a diverse set of MRA images demonstrate the superior performance of the proposed method compared to previous approaches in terms of accuracy and computational efficiency. The presented vessel extraction method holds promise for improving the diagnosis and treatment of vascular-related diseases. By leveraging the skew normal distribution, it provides accurate and efficient vessel segmentation, contributing to the advancement of vascular imaging in the field of medical image analysis.

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Regression model under skew-normal error with applications in predicting groundwater arsenic level in the Mekong Delta Region

Cited by 7 publications

References 15 publications

g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

g.ridge: An R Package for Generalized Ridge Regression for Sparse and High-Dimensional Linear Models

A Statistical Analysis of Vietnamese High School English Test Score Data

New Vessel Extraction Method by Using Skew Normal Distribution for MRA Images

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