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Kue-Hong Chen, Jeng-Hong Kao and Yi-Hui Hsu
In this manuscript, we will apply the regularized meshless method, coupled with an error estimation technique, to tackle the challenge of modeling oblique incident waves interacting with multiple cylinders. Given the impracticality of obtaining an exact ...
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Alexander Robitzsch
Item response theory (IRT) models are frequently used to analyze multivariate categorical data from questionnaires or cognitive test data. In order to reduce the model complexity in item response models, regularized estimation is now widely applied, addi...
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Alexander Robitzsch
This article reviews several implementation aspects in estimating regularized single-group and multiple-group structural equation models (SEM). It is demonstrated that approximate estimation approaches that rely on a differentiable approximation of non-d...
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Alexander Robitzsch
Missing item responses are frequently found in educational large-scale assessment studies. In this article, the Mislevy-Wu item response model is applied for handling nonignorable missing item responses. This model allows that the missingness of an item ...
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Alexander Robitzsch
Structural equation models (SEM) are widely used in the social sciences. They model the relationships between latent variables in structural models, while defining the latent variables by observed variables in measurement models. Frequently, it is of int...
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Egor I. Chetkin, Sergei L. Shishkin and Bogdan L. Kozyrskiy
Bayesian neural networks (BNNs) are effective tools for a variety of tasks that allow for the estimation of the uncertainty of the model. As BNNs use prior constraints on parameters, they are better regularized and less prone to overfitting, which is a s...
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Allimuthu Elangovan, Nguyen Trung Duc, Dhandapani Raju, Sudhir Kumar, Biswabiplab Singh, Chandrapal Vishwakarma, Subbaiyan Gopala Krishnan, Ranjith Kumar Ellur, Monika Dalal, Padmini Swain, Sushanta Kumar Dash, Madan Pal Singh, Rabi Narayan Sahoo, Govindaraj Kamalam Dinesh, Poonam Gupta and Viswanathan Chinnusamy
Phenomics technologies have advanced rapidly in the recent past for precision phenotyping of diverse crop plants. High-throughput phenotyping using imaging sensors has been proven to fetch more informative data from a large population of genotypes than t...
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Hamidreza Marateb, Mina Norouzirad, Kouhyar Tavakolian, Faezeh Aminorroaya, Mohammadreza Mohebbian, Miguel Ángel Mañanas, Sergio Romero Lafuente, Ramin Sami and Marjan Mansourian
Optimal allocation of ward beds is crucial given the respiratory nature of COVID-19, which necessitates urgent hospitalization for certain patients. Several governments have leveraged technology to mitigate the pandemic?s adverse impacts. Based on clinic...
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Alexander Robitzsch
The mixture Rasch model is a popular mixture model for analyzing multivariate binary data. The drawback of this model is that the number of estimated parameters substantially increases with an increasing number of latent classes, which, in turn, hinders ...
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A.V. Vorobyev
Pág. 29 - 34
The limited availability of information collection is a factor hindering the application of high-performance machine learning algorithms. The development of methods to improve the accuracy of models while reducing the observation periods, can be an effec...
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