nlogL = explike (mu, x) returns the negative log likelihood of the data in x corresponding to the exponential distribution with mean parameter mu. x must be a vector of non-negative values, otherwise NaN is returned.

[nlogL, avar] = explike (mu, x) also returns the inverse of Fisher’s information matrix, avar. If the input mean parameter, mu, is the maximum likelihood estimate, avar is its asymptotic variance.

[…] = explike (mu, x, censor) accepts a boolean vector, censor, of the same size as x with 1s for observations that are right-censored and 0s for observations that are observed exactly. By default, or if left empty, censor = zeros (size (x)).

[…] = explike (mu, x, censor, freq) accepts a frequency vector, freq, of the same size as x. freq typically contains integer frequencies for the corresponding elements in x, but it can contain any non-integer non-negative values. By default, or if left empty, freq = ones (size (x)).

A common alternative parameterization of the exponential distribution is to use the parameter $\lambda$ defined as the mean number of events in an interval as opposed to the parameter $\mu$, which is the mean wait time for an event to occur. $\lambda$ and $\mu$ are reciprocals, i.e. $\mu \; =\; 1\; /\; \lambda$.

Further information about the exponential distribution can be found at https://en.wikipedia.org/wiki/Exponential_distribution

See also: expcdf, expinv, exppdf, exprnd, expfit, expstat

Source Code: explike