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Bootstrapping the estimated latent distribution of the two-parameter latent trait model.
Br J Math Stat Psychol. 2007 May; 60(Pt 1):175-91.BJ

Abstract

This paper focuses on the two-parameter latent trait model for binary data. Although the prior distribution of the latent variable is usually assumed to be a standard normal distribution, that prior distribution can be estimated from the data as a discrete distribution using a combination of EM algorithms and other optimization methods. We assess with what precision we can estimate the prior from the data, using simulations and bootstrapping. A novel calibration method is given to check that near optimality is achieved for the bootstrap estimates. We find that there is sufficient information on the prior distribution to be informative, and that the bootstrap method is reliable. We illustrate the bootstrap method for two sets of real data.

Authors+Show Affiliations

Department of Statistics, London School of Economics and Political Science, UK. M.Knott@lse.ac.ukNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

17535586

Citation

Knott, M, and P Tzamourani. "Bootstrapping the Estimated Latent Distribution of the Two-parameter Latent Trait Model." The British Journal of Mathematical and Statistical Psychology, vol. 60, no. Pt 1, 2007, pp. 175-91.
Knott M, Tzamourani P. Bootstrapping the estimated latent distribution of the two-parameter latent trait model. Br J Math Stat Psychol. 2007;60(Pt 1):175-91.
Knott, M., & Tzamourani, P. (2007). Bootstrapping the estimated latent distribution of the two-parameter latent trait model. The British Journal of Mathematical and Statistical Psychology, 60(Pt 1), 175-91.
Knott M, Tzamourani P. Bootstrapping the Estimated Latent Distribution of the Two-parameter Latent Trait Model. Br J Math Stat Psychol. 2007;60(Pt 1):175-91. PubMed PMID: 17535586.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Bootstrapping the estimated latent distribution of the two-parameter latent trait model. AU - Knott,M, AU - Tzamourani,P, PY - 2007/5/31/pubmed PY - 2007/8/2/medline PY - 2007/5/31/entrez SP - 175 EP - 91 JF - The British journal of mathematical and statistical psychology JO - Br J Math Stat Psychol VL - 60 IS - Pt 1 N2 - This paper focuses on the two-parameter latent trait model for binary data. Although the prior distribution of the latent variable is usually assumed to be a standard normal distribution, that prior distribution can be estimated from the data as a discrete distribution using a combination of EM algorithms and other optimization methods. We assess with what precision we can estimate the prior from the data, using simulations and bootstrapping. A novel calibration method is given to check that near optimality is achieved for the bootstrap estimates. We find that there is sufficient information on the prior distribution to be informative, and that the bootstrap method is reliable. We illustrate the bootstrap method for two sets of real data. SN - 0007-1102 UR - https://www.unboundmedicine.com/medline/citation/17535586/Bootstrapping_the_estimated_latent_distribution_of_the_two_parameter_latent_trait_model_ L2 - https://doi.org/10.1348/000711006X107539 DB - PRIME DP - Unbound Medicine ER -