Applying the Minimax Principle to Sequential Mastery Testing

نویسندگان

  • Hans J. Vos
  • J. Nelissen
چکیده

The purpose of this paper is to derive optimal rules for sequential mastery tests. In a sequential mastery test, the decision is to classify a subject as a master, a nonmaster, or to continue sampling and administering another random item. The framework of minimax sequential decision theory (minimum information approach) is used; that is, optimal rules are obtained by minimizing the maximum expected losses associated with all possible decision rules at each stage of sampling. The main advantage of this approach is that costs of sampling can be explicitly taken into account. The binomial model is assumed for the probability of a correct response given the true level of functioning, whereas threshold loss is adopted for the loss function involved. Monotonicity conditions are derived, that is, conditions sufficient for optimal rules to be in the form of sequential cutting scores. The paper concludes with a simulation study, in which the minimax sequential strategy is compared with other procedures that exist for similar classification decision problems in the literature.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Adaptive Mastery Testing Using the Rasch Model and Bayesian Sequential Decision Theory

A version of sequential mastery testing is studied in which response behavior is modeled by an item response theory (IRT) model. First, a general theoretical framework is sketched that is based on a combination of Bayesian sequential decision theory and item response theory. A discussion follows on how IRT based sequential mastery testing can be generalized to adaptive item and testlet selectio...

متن کامل

Asymptotically Minimax Robust Hypothesis Testing

The design of asymptotically minimax robust hypothesis testing is formalized for the Bayesian and Neyman-Pearson tests of Type I and II. The uncertainty classes based on the KL-divergence, αdivergence, symmetrized α-divergence, total variation distance, as well as the band model, moment classes and p-point classes are considered. It is shown with a counterexample that minimax robust tests do no...

متن کامل

A Rejection Principle for Sequential Tests of Multiple Hypotheses Controlling Familywise Error Rates.

We present a unifying approach to multiple testing procedures for sequential (or streaming) data by giving sufficient conditions for a sequential multiple testing procedure to control the familywise error rate (FWER). Together we call these conditions a "rejection principle for sequential tests," which we then apply to some existing sequential multiple testing procedures to give simplified unde...

متن کامل

Applications of Bayesian Decision Theory to Sequential Mastery Testing

The purpose of this paper is to formulate optimal sequential rules for mastery tests. The framework for this approach is derived from empirical Bayesian decision theory. Both a threshold and linear loss structure are considered. The binomial probability distribution is adopted as the psychometric model involved. Conditions sufficient for sequentially setting optimal cutting scores are presented...

متن کامل

Robustness in portfolio optimization based on minimax regret approach

Portfolio optimization is one of the most important issues for effective and economic investment. There is plenty of research in the literature addressing this issue. Most of these pieces of research attempt to make the Markowitz’s primary portfolio selection model more realistic or seek to solve the model for obtaining fairly optimum portfolios. An efficient frontier in the ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2012