### Function Reference: `explike`

statistics: nlogL = explike (mu, x)
statistics: [nlogL, avar] = explike (mu, x)
statistics: […] = explike (mu, x, censor)
statistics: […] = explike (mu, x, censor, freq)

Negative log-likelihood for the exponential distribution.

`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 `1`s for observations that are right-censored and `0`s 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 λ defined as the mean number of events in an interval as opposed to the parameter μ, which is the mean wait time for an event to occur. λ and μ are reciprocals, i.e. μ = 1 / λ.

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