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Small area estimation using multiple imputation in three-parameter logistic models

Cristian F. Téllez-Piñerez et al.

Chilean Journal of Statistics2024https://doi.org/10.32372/chjs.15-01-01article
ABDC C
Weight
0.30

What the paper says

This article presents a new method that combines item response theory techniques with small-area estimation approaches to handle missing data.We propose an unbiased estimator for the average skill parameter of three-parameter logistic models using plausible values as the imputation method for missing data.We conduct a thorough simulation study to compare our estimator with the Horvitz-Thompson estimator in complex sampling.Synthetic data experiments demonstrate that our proposal has lower standard errors than its competitor.Additionally, we apply our method to the results in mathematics of the 2015 Program for International Student Assessment and compare our findings with previous studies.These findings indicate that our method is a competitive alternative for generating accurate official statistics.

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https://doi.org/https://doi.org/10.32372/chjs.15-01-01

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@article{cristian2024,
  title        = {{Small area estimation using multiple imputation in three-parameter logistic models}},
  author       = {Cristian F. Téllez-Piñerez et al.},
  journal      = {Chilean Journal of Statistics},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.32372/chjs.15-01-01},
}

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Small area estimation using multiple imputation in three-parameter logistic models

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Evidence weight

0.30

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.