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One of the most common censoring methods is the progressive type-II censoring. In this method of censoring, a total of $n$ units are placed on the test, and at the time of failure of each unit, some of the remaining units are randomly removed. This will continue to record $m$ failure times, where $m$ is a pre-determined value, and then the experiment ends. The problem of determining the optimal censoring scheme in the progressive type-II censoring has been studied so far by considering different criteria. Another issue in the progressive type-II censoring is choosing the sample size at the start of the experiment, namely $n$. In this paper, assuming the Pareto distribution for the data, we will determine the optimal sample size, $n_ {opt}$, as well as the optimal censoring scheme by means of the Fisher Information. Finally, to evaluate the results, numerical calculations have been presented by using $R$ software.

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Today, applying statistics in other sciences, including medical sciences, has become very common. Researchers consider optimal design as a tool to increase the efficiency of experiments.
Pharmacokinetics is particularly important in the medical sciences as a branch of pharmacology that studies the performance of drugs in living organisms.
This study aims to introduce optimal designs for models in pharmacokinetic studies. The models used in this paper are known as nonlinear models in the statistical literature. These models depend on specific parameters based on pharmacological factors and time as predictor variables.
Optimal designs are obtained based on functions of the Fisher information matrix. These functions are known as optimal criteria. In this paper, we consider two criteria, A- and E-optimality. Based on these two criteria, locally optimal designs are obtained for the considered models.