In computerized adaptive testing (CAT), examinees are presented with various sets of items chosen from a precalibrated item pool. Consequently, the attrition speed of the items is extremely fast, and replenishing the item pool is essential. Therefore, item calibration has become a crucial concern in maintaining item banks. In this study, a two-parameter logistic model is used. We applied optimal designs and adaptive sequential analysis to solve this item calibration problem. The results indicated that the proposed optimal designs are cost effective and time efficient.
Class imbalance problem has attracted many attentions in recent years. When the available training sample size of each class is imbalanced, the directly established classification model will tend to allocate the testing sample into the majority class. A proper resampling method together with a power classifier is generally employed for dealing with this problem. Many multi-classifier ensembles have been shown to outperform single classifier in many experiments. Bagging and boosting are two most popular multi-classifier frameworks and have been applied to deal with the class imbalance problem. By observing that the sample information of the minority class is very limited and the small sample size (SSS) problem might decrease the performance of the classifiers, another powerful multi-classifier method called random subspace method (RSM) is introduced to deal with the class imbalance problem in this study. To evaluate the performance of different classifiers, a well-known resampling method called SMOTE is employed. The experiment results showed RSM has the best performance in most of the considered situations.
The fraction retention non-inferiority hypothesis is often measured for the ratio of the effects of a new treatment to those of the control in medical research. However, the fraction retention non-inferiority test that the new treatment maintains the efficacy of control can be affected by the nuisance parameters. Herein, a heuristic procedure for testing the fraction retention non-inferiority hypothesis is proposed based on the generalized p-value (GPV) under normality assumption and heteroskedasticity. Through the simulation study, it is demonstrated that, the performance of the GPV-based method not only adequately controls the type I error rate at the nominal level but also is uniformly more powerful than the ratio test, Rothmann's and Wang's tests, the comparable extant methods. Finally, we illustrate the proposed method by employing a real example.
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