# expfit

Exponential parameter estimates

## Syntax

```muhat = expfit(data) [muhat,muci] = expfit(data) [muhat,muci] = expfit(data,alpha) [...] = expfit(data,alpha,censoring) [...] = expfit(data,alpha,censoring,freq) ```

## Description

`muhat = expfit(data)` estimates the mean of exponentially distributed sample data in the vector `data`.

`[muhat,muci] = expfit(data)` also returns the 95% confidence interval for the mean parameter estimates in `muci`. The first row of `muci` is the lower bound of the confidence interval, and the second row is the upper bound.

`[muhat,muci] = expfit(data,alpha)` returns the 100(1–`alpha`)% confidence interval for the parameter estimate `muhat`, where `alpha` is a value in the range `[0 1]` specifying the width of the confidence interval. By default, `alpha` is `0.05`, which corresponds to the 95% confidence interval.

`[...] = expfit(data,alpha,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. `data` must be a vector in order to pass in the argument `censoring`.

`[...] = expfit(data,alpha,censoring,freq)` accepts a frequency vector, `freq` of the same size as `data`. Typically, `freq` contains integer frequencies for the corresponding elements in `data`, but can contain any nonnegative values. Pass in `[]` for `alpha`, `censoring`, or `freq` to use their default values.

## Examples

The following estimates the mean `mu` of exponentially distributed data, and returns a 95% confidence interval for the estimate:

```mu = 3; data = exprnd(mu,100,1); % Simulated data [muhat,muci] = expfit(data) muhat = 2.7511 muci = 2.2826 3.3813```