政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/142013

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政大典藏 > College of Commerce > Department of Statistics > Periodical Articles > Item 140.119/142013

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142013

Title:	Bayesian estimation for an item response tree model for nonresponse modeling
Authors:	張育瑋 Chang, Yu-Wei Tu, Jyun-Ye
Contributors:	統計系
Keywords:	Bayesian estimation;Item response data;Markov chain Monte Carlo;Nonresponse data;Tree model
Date:	2022-11
Issue Date:	2022-09-21 11:44:52 (UTC+8)
Abstract:	Nonresponse data are common in achievement tests or questionnaires. Chang et al. (Br J Math Stat Psychol 74:487–512, 2021) proposed an Item Response tree model, namely TR4, for modeling some potential mechanisms underlying nonresponses so that the estimates of parameters of interest would not be biased due to missing not at random (Rubin in Biometrika 63:581–592, 1976). TR4 has two notable degenerate cases, both with insightful practical meanings. When TR4 is fitted to data originated from some degenerate cases, there exist model identifiability issues so that the existing frequentist inference for the TR4 model is not suitable. In the current study, we propose a Bayesian estimation procedure that incorporates the Markov chain Monte Carlo technique for estimating the TR4 model. We conducted simulation studies to demonstrate the effectiveness of the Bayesian estimation procedure in solving the model unidentifiability issue. In addition, the TR4 model is further extended in the present study to effectively accommodate the complexity underlying some real data. The advantage of the extended models over TR4 is demonstrated in the real data analysis where we apply our method to the data of a geography test for college admission in Taiwan.
Relation:	Metrika, Vol.85, pp.1023-1047
Data Type:	article
DOI link:	https://doi.org/10.1007/s00184-022-00858-1
DOI:	10.1007/s00184-022-00858-1
Appears in Collections:	[Department of Statistics] Periodical Articles

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