Random Item MIRID Modeling and Its Application

The Model With Internal Restrictions on Item Difficulty (MIRID; Butter, 1994) has been useful for investigating cognitive behavior in terms of the processes that lead to that behavior. The main objective of the MIRID model is to enable one to test how component processes influence the complex cognitive behavior in terms of the item parameters. The original MIRID model is, indeed, a fairly restricted model for a number of reasons. One of these restrictions is that the model treats items as fixed and does not fit measurement contexts where the concept of the random items is needed. In this article, random item approaches to the MIRID model are proposed, and both simulation and empirical studies to test and illustrate the random item MIRID models are conducted. The simulation and empirical studies show that the random item MIRID models provide more accurate estimates when substantial random errors exist, and thus these models may be more beneficial.

Restricted model domain time Radon transforms

The coefficients that synthesize seismic data via the hyperbolic Radon transform (HRT) are estimated by solving a linear-inverse problem. In the classical HRT, the computational cost of the inverse problem is proportional to the size of the data and the number of Radon coefficients. We have developed a strategy that significantly speeds up the implementation of time-domain HRTs. For this purpose, we have defined a restricted model space of coefficients applying hard thresholding to an initial low-resolution Radon gather. Then, an iterative solver that operated on the restricted model space was used to estimate the group of coefficients that synthesized the data. The method is illustrated with synthetic data and tested with a marine data example.