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Exponential parameter estimates


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


muhat = expfit(data) estimates the mean of an exponentially distributed sample data. Each entry of muhat corresponds to the data in a column of data.

[muhat,muci] = expfit(data) returns 95% confidence intervals for the mean parameter estimates in matrix muci. The first row of muci contains the lower bounds of the confidence intervals, and the second row contains the upper bounds.

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

[...] = 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.


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 =
muci =

Introduced before R2006a

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