Lesson 7: Using the bootstap to estimate variability in thresholds. Now that we've introduced the bootstrapping procedure for a general function, we're ready to apply it to estimating the variability in the threshold parameter from the best-fitting Weibull function. This MATLAB function returns the maximum likelihood estimates, parmhat, of the parameters of the Weibull distribution given the values in the vector data, which must be positive.

This MATLAB function returns the maximum likelihood estimates, parmhat, of the parameters of the Weibull distribution given the values in the vector data, which must be positive. ... The first row contains the lower bounds of the confidence intervals for the parameters, and the second row contains the upper bounds of the confidence intervals ...

Oct 20, 2014 · However the confidence interval on the mean is an estimate of the dispersion of the true population mean, and since you are usually comparing means of two or more populations to see if they are different, or to see if the mean of one population is different from zero (or some other constant), that is appropriate.

Sep 28, 2011 · ci = paramci(pd); % This function calculates the values of the parameters based on a certain confidence interval. Here the by default the confidence interval is 95 percent Here the by default the confidence interval is 95 percent Weibull Regression for a Right-Censored Endpoint with Interval-Censored Covariate. The main function of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates.