CAJ | 학술논문

针对马尔科夫链蒙特卡洛(MCMC)做参数估计因初值不好而链长太长且耗时太久的缺陷,利用 Monte Carlo 模拟进行研究,结果发现:使用 MCMC 进行估计项目反应理论中2PLM 的未知能力参数和项目参数,当样本量或项目量较大,且链长较短时,初值作用明显,比较准确的初值估计精度更高.这个发现可以在一定条件下,弥补MCMC耗时太久的缺陷.
침대마이과부련몽특잡락(MCMC)주삼수고계인초치불호이련장태장차모시태구적결함,이용 Monte Carlo 모의진행연구,결과발현:사용 MCMC 진행고계항목반응이론중2PLM 적미지능력삼수화항목삼수,당양본량혹항목량교대,차련장교단시,초치작용명현,비교준학적초치고계정도경고.저개발현가이재일정조건하,미보MCMC모시태구적결함.
To implement a Markov Chain Monte Carlo (MCMC) method, one of important issues is the choice of starting values. If MCMC sampler started at poor initial values, the chain is taking a long time to move around the parameter space, therefore it will take longer to converge to the stationary distribution. To remedy the defect of MCMC, the special initial values of the unknown parameters in the 2-parameter Logistic model are employed to be the initial values of the MCMC.The results of the Monte Carlo simulation show that the scheme could greatly speed up MCMC convergence.