The gamma generalized linear model, log transformation, and the robust Yuen-Welch test for analyzing group means with skewed and heteroscedastic data

Ng, Victoria Ka Yin; Cribbie, Robert A.

doi:10.1080/03610918.2018.1440301

Cited by 14 publications

(4 citation statements)

References 32 publications

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“…To examine the association between employment and outcomes of depression and life satisfaction, we applied different types of regression models. For depression, we used the log-Gamma GLM [ 31 ]. For life satisfaction, we used the heterogeneous choice model [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Visual impairment and employment in Norway

Brunes

Heir

2022

BMC Public Health

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Background Past studies have suggested lower employment of people with visual impairment. Reasons for this are less known. This study aimed to examine the employment rates among people with visual impairment, and its association with sociodemographic characteristics, vision-related factors, depression, and life satisfaction. Methods This cross-sectional study included a stratified random sample of 574 working-age adults (18–67 years) who were members of the Norwegian Association of the Blind and Partially Sighted. Data were collected by telephone interviews between January and May 2017, and included information on work status, sociodemographic factors, vision-related characteristics, depression (Patient Health Questionnaire-9), and life satisfaction (Cantril’s Ladder of Life Satisfaction). Associations with employment (full-time, part-time, or self-employment) were examined using regression analyses. Results Of the participants, 51.2% of males and 38.1% of females reported to be employed. Employment was associated with being of middle age, male gender, higher education, residing in high-income municipalities, having a moderate degree of vision loss, lower onset-age of vision loss, and having no additional impairments. Employed participants had lower levels of depression compared to others (adjusted exponentiated beta: 0.80, 95% confidence interval (CI): 0.67, 0.96). They also had a higher odds of scoring higher on life satisfaction (adjusted exponentiated beta (odds ratio): 1.85, 95% CI: 1.32, 2.59). Conclusions Employment was lower in people with visual impairment than in the general population. Inclusion of the blind and partially sighted into the workforce could promote health and thus have socio-economic benefits.

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Section: Methodsmentioning

confidence: 99%

Visual impairment and employment in Norway

Brunes

Heir

2022

BMC Public Health

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“…Generalized Linear Model (GLM) is eventually the enhancement of old-style linear models, and the series of instructions inside these models turn to data by using the MLE technique. These models give tremendous, high-speed, and parallel computation with a small number of explanatory variables with non-zero constants [35,36].…”

Section: Testing and Evaluation Of The Modelmentioning

confidence: 99%

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Abidi

Hussain

et al. 2018

Sustainability

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Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.

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“…It is eventually assessed by the famous statistical principle of maximum likelihood estimation (MLE) and it helps to minimize the supposition that difference between observed and the predicted value of response variable which is called residual and is Gaussian distributed [35].…”

Section:  Generalized Linear Model (Glm)mentioning

confidence: 99%

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Abidi¹,

Hussain²,

Xu³

et al. 2018

Preprint

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Incorporating substantial sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study is to identify the confused students who have failed to master the skill(s) given by the tutors as a homework using Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models that include: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated and tested learning algorithms, performed stratified cross-validation and measured the performance of the models through various performance metrics i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity & Specificity. We found GLM, DT & RF are high accuracies achieving classifiers. However, other perceptions such as detection of unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students which were confused attempting the homework exercise and can help students foster their knowledge, and talent to play a vital role in environmental development.

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The gamma generalized linear model, log transformation, and the robust Yuen-Welch test for analyzing group means with skewed and heteroscedastic data

Cited by 14 publications

References 32 publications

Visual impairment and employment in Norway

Visual impairment and employment in Norway

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

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