Results 21 to 25 of about 1,003,663 (25)

Bias-Reduction in Variational Regularization [PDF]

arXiv, 2016
The aim of this paper is to introduce and study a two-step debiasing method for variational regularization. After solving the standard variational problem, the key idea is to add a consecutive debiasing step minimizing the data fidelity on an appropriate set, the so-called model manifold.
arxiv  

Maximum likelihood estimation in the sparse Rasch model [PDF]

arXiv
The Rasch model has been widely used to analyse item response data in psychometrics and educational assessments. When the number of individuals and items are large, it may be impractical to provide all possible responses. It is desirable to study sparse item response experiments.
arxiv  

Random pairing MLE for estimation of item parameters in Rasch model [PDF]

arXiv
The Rasch model, a classical model in the item response theory, is widely used in psychometrics to model the relationship between individuals' latent traits and their binary responses on assessments or questionnaires. In this paper, we introduce a new likelihood-based estimator -- random pairing maximum likelihood estimator ($\mathsf{RP\text{-}MLE ...
arxiv  

Leveraging Natural Language and Item Response Theory Models for ESG Scoring [PDF]

arXiv
This paper explores an innovative approach to Environmental, Social, and Governance (ESG) scoring by integrating Natural Language Processing (NLP) techniques with Item Response Theory (IRT), specifically the Rasch model. The study utilizes a comprehensive dataset of news articles in Portuguese related to Petrobras, a major oil company in Brazil ...
arxiv  

On the Consistency of Bayesian Adaptive Testing under the Rasch Model [PDF]

arXiv
This study establishes the consistency of Bayesian adaptive testing methods under the Rasch model, addressing a gap in the literature on their large-sample guarantees. Although Bayesian approaches are recognized for their finite-sample performance and capability to circumvent issues such as the cold-start problem; however, rigorous proofs of their ...
arxiv