Computational Precision of the Power Function for Conditional Tests of Assumptions of the Rasch Model  

作　　者：Clemens Draxler Jan Philipp Nolte 

机构地区：[1]UMIT—The Health and Life Sciences University, Hall in Tirol, Austria

出　　处：《Open Journal of Statistics》2018年第6期873-884,共12页统计学期刊（英文）

摘　　要：Draxler and Zessin [1] derived the power function for a class of conditional tests of assumptions of a psychometric model known as the Rasch model and suggested an MCMC approach developed by Verhelst [2] for the numerical approximation of the power of the tests. In this contribution, the precision of the Verhelst approach is investigated and compared with an exact sampling procedure proposed by Miller and Harrison [3] for which the discrete probability distribution to be sampled from is exactly known. Results show no substantial differences between the two numerical procedures and quite accurate power computations. Regarding the question of computing time the Verhelst approach will have to be considered much more efficient.Draxler and Zessin [1] derived the power function for a class of conditional tests of assumptions of a psychometric model known as the Rasch model and suggested an MCMC approach developed by Verhelst [2] for the numerical approximation of the power of the tests. In this contribution, the precision of the Verhelst approach is investigated and compared with an exact sampling procedure proposed by Miller and Harrison [3] for which the discrete probability distribution to be sampled from is exactly known. Results show no substantial differences between the two numerical procedures and quite accurate power computations. Regarding the question of computing time the Verhelst approach will have to be considered much more efficient.

关 键 词：CONDITIONAL Tests CONDITIONAL PROBABILITY DISTRIBUTION HYPERGEOMETRIC DISTRIBUTION Power Function Random Sampling RASCH Model 

分 类 号：O1[理学—数学]