Completeness comparisons among sequences of samples II. censoring from above or below, and general censoring

Completeness Comparisons a ~ Dng Sequences of Samples

Adaptive Progressive Type-II Censoring

Extending the model of progressive Type-II censoring, an adaption process is introduced. It allows to choose the next censoring number taking into account both the previous censoring numbers and the previous failure times. After deriving some distributional results, it is shown that maximum likelihood estimators coincide with those in deterministic progressive Type-II censoring. Finally, infere...

exponentiated-Weibull distribution under type II censoring

This paper considers the three-parameter exponentiated Weibull family under type II censoring. It first graphically illustrates the shape property of the hazard function. Then, it proposes a simple algorithm for computing the maximum likelihood estimator and derives the Fisher information matrix. The latter one is represented through a single integral in terms of hazard function, hence it solve...

Optimal sample size and censoring scheme in progressively type II censoring based on Fisher information for the Pareto distribution

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...

Discrete Choice and Censoring

Multinomial Choice: Data consists of consumer choices of various goods, along char­ acteristics. Let there be J choices and y = (y1, . . . , yJ ) where yj = 1 if good j is chosen and yj = 0 otherwise. Let x be observed characteristics of the goods and choices. Here a conditional density for y corresponds to conditional choice probabilities P (j|x, β), one for each j, with �J j=1 P (j|x, β) = 1 ...