Inference for a Multivariate Exponential Distribution with a Censored Sample

Maximum likelihood estimators for the parameters of a multivariate exponential Cdf are easily obtained from partial information about a random sample, censored or not. The partial information consists of the minimum from each multivariate observation and the counts of how often each r.v. was equal to the minimum in an observation. The censoring might cause only the smallest r out of n minima to be observed along with the counts. The estimators depend on the total time-on-test statistic familiar in univariate exponential life testing. A likelihood ratio test for s-independence is derived which has s-significance α = 0 and easily calculated power function.