This paper explores innovations to parameter estimation in generalized linear and nonlinear models, which may be used in item response modeling to account for guessing/pretending or slipping/dissimulation and for the effect of covariates. We introduce a new implementation of the EM algorithm and propose a new algorithm based on the parametrized link function. The two novel iterative algorithms are compared to existing methods in a simulation study. Additionally, the study examines software implementation, including the specification of initial values for numerical algorithms and asymptotic properties with an estimation of standard errors. Overall, the newly proposed algorithm based on the parametrized link function outperforms other procedures, especially for small sample sizes. Moreover, the newly implemented EM algorithm provides additional information regarding respondents' inclination to guess or pretend and slip or dissimulate when answering the item. The study also discusses applications of the methods in the context of the detection of differential item functioning and addresses the measurement error. Methods are offered in the difNLR package and in the interactive application of the ShinyItemAnalysis package; demonstration is provided using real data from psychological and educational assessments.

翻译：本文探讨了广义线性和非线性模型中参数估计的创新方法，这些方法可用于项目反应建模，以考虑猜测/伪装或失误/掩饰效应以及协变量的影响。我们引入了EM算法的新实现，并提出了一种基于参数化连接函数的新算法。通过模拟研究，将这两种新颖的迭代算法与现有方法进行了比较。此外，本研究还探讨了软件实现问题，包括数值算法初始值的设定、渐近性质以及标准误的估计。总体而言，新提出的基于参数化连接函数的算法优于其他方法，尤其在小样本情况下表现更佳。同时，新实现的EM算法能够提供关于受试者在作答时猜测或伪装、失误或掩饰倾向的额外信息。研究还讨论了这些方法在检测差异项目功能背景下的应用，并探讨了测量误差问题。相关方法已在difNLR软件包及ShinyItemAnalysis软件包的交互式应用中实现，并利用心理与教育评估领域的真实数据进行了演示。