Regularized Estimation of the Nominal Response Model
Bibliographic Data
| ID | 19289783 |
|---|---|
| Authors | Michela Battauz (0000-0002-3098-689X, University of Udine, corresponding author) |
| Year | 2020 |
| Volume | 55 |
| Issue | 6 |
| Pages | 811-824 |
| Publication date | 2020-11-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Multivariate Behavioral Research (JOURNAL) |
| Journal identifiers | ISSN: 0027-3171 • E-ISSN: 1532-7906 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/00273171.2019.1681252 |
| PMID | 31682150 |
| OpenAlex | W2989464582 |
| Language | EN |
| Citations received | 2 |
| References cited | 28 |
The nominal response model is an item response theory model that does not require the ordering of the response options. However, while providing a very flexible modeling approach of polytomous responses, it involves the estimation of many parameters at the risk of numerical instability and overfitting. The lasso is a technique widely used to achieve model selection and regularization. In this paper, we propose the use of a fused lasso penalty to group response categories and perform regularization of the unidimensional and multidimensional nominal response models. The good performance of the method is illustrated through real-data applications and simulation studies
Artificial neural network · Item response theory · Lasso (programming language) · Machine learning · Model selection · Overfitting · Polytomous Rasch model · Psychometrics · Regularization (linguistics) · Statistics · Artificial Intelligence · Computer Science · Mathematics · Optimal Experimental Design Methods · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
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Multidimensional Item Response Theory
Model Selection and Estimation in Regression with Grouped Variables
The Adaptive Lasso and Its Oracle Properties
A Generalized Partial Credit Model
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Marginal Maximum Likelihood Estimation of Item Parameters
Mirt
Regression Shrinkage and Selection Via the Lasso
Analysis of Multivariate Social Science Data
Estimating item parameters and latent ability when responses are scored in two or more nominal categories
| Citations per year | 0.5 |
|---|---|
| Citation span | 2022 - 2023 (2) |
| Highly cited | No |