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nlogL = explike(param,data)
[nlogL,avar] = explike(param,data)
[...] = explike(param,data,censoring)
[...] = explike(param,data,censoring,freq)
nlogL = explike(param,data) returns the negative of the log-likelihood for the exponential distribution. param is the mean parameter, mu. nlogL is a scalar.
[nlogL,avar] = explike(param,data) returns the inverse of Fisher's information, avar, a scalar. If the input parameter value in param is the maximum likelihood estimate, avar is its asymptotic variance. avar is based on the observed Fisher's information, not the expected information.
[...] = explike(param,data,censoring) accepts a Boolean vector, censoring, of the same size as data, which is 1 for observations that are right-censored and 0 for observations that are observed exactly.
[...] = explike(param,data,censoring,freq) accepts a frequency vector, freq, of the same size as data. The vector freq typically contains integer frequencies for the corresponding elements in data, but can contain any nonnegative values. Pass in [] for censoring to use its default value.
expcdf | expfit | expinv | exppdf | exprnd | expstat
![]() | expinv | export (dataset) | ![]() |

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