Nowadays, the use of various types of censoring plan in studies of lifetime engineering systems and industrial experiment are worthwhile. In this paper, by using the idea in Cramer and Iliopoulos (2010), an adaptive progressive Type-I censoring is introduced. It is assumed that the next censoring number is random variable and depends on the previous censoring numbers, previous failure times and censoring times. General distributional results are obtained in explicit analytic forms. It is shown that maximum likelihood estimators coincide with those in deterministic progressive Type-I censoring. Finally, in order to illustrate and make a comparison, simulation study is done for one-parameter exponential distribution.

Nowadays, the use of various censorship methods has become widespread in industrial and clinical tests. Type I and Type II progressive censoring are two types of these censors. The use of these censors also has some disadvantages. This article tries to reduce the defects of the type I progressive censoring by making some change to progressive censorship. Considering the number and the time of the withdrawals as a random variable, this is done. First, Type I, Type II progressive censoring and two of their generalizations are introduced. Then, we introduce the new censoring based on the Type I progressive censoring and its probability density function. Also, some of its special cases will be explained and a few related theorems are brought. Finally, the simulation algorithm is brought and for comparison of introduced censorship against the traditional censorships a simulation study was done.

‎This paper presents a nonparametric multi-class depth-based classification approach for multivariate data. This approach is easy to implement rather than most existing nonparametric methods that have computational complexity. If the assumption of the elliptical symmetry holds, this method is equivalent to the Bayes optimal rule. Some simulated data sets as well as real example have been used to evaluate the performance of these depth-based classifiers.