fitbayesWeibull

Suppose \(x=(x_1,\dots,x_n)^T\) denotes a vector of \(n\) independent observations coming from a three-parameter Weibull distribution. Using the methodology given in Green et al. (1994), we compute the Bayes' estimators of the shape, scale, and location parameters.

Developed for the following tasks. 1 ) Computing the probability density function,
cumulative distribution function, random generation, and estimating the parameters
of the eleven mixture models. 2 ) Point estimation of the parameters of two -
parameter Weibull distribution using twelve methods and three - parameter Weibull
distribution using nine methods. 3 ) The Bayesian inference for the three -
parameter Weibull distribution. 4 ) Estimating parameters of the three - parameter
Birnbaum - Saunders, generalized exponential, and Weibull distributions fitted to
grouped data using three methods including approximated maximum likelihood,
expectation maximization, and maximum likelihood. 5 ) Estimating the parameters
of the gamma, log-normal, and Weibull mixture models fitted to the grouped data
through the EM algorithm, 6 ) Estimating parameters of the nonlinear height curve
fitted to the height - diameter observation, 7 ) Estimating parameters, computing
probability density function, cumulative distribution function, and generating
realizations from gamma shape mixture model introduced by Venturini et al. (2008)
<doi:10.1214/07-AOAS156> , 8 ) The Bayesian inference, computing probability
density function, cumulative distribution function, and generating realizations
from univariate and bivariate Johnson SB distribution, 9 ) Robust multiple linear
regression analysis when error term follows skewed t distribution, 10 ) Estimating
parameters of a given distribution fitted to grouped data using method of maximum
likelihood, and 11 ) Estimating parameters of the Johnson SB distribution through
the Bayesian, method of moment, conditional maximum likelihood, and two - percentile
method.

Mahdi Teimouri

ForestFit

Statistical Modelling for Plant Size Distributions

fitbayesWeibull function

<dl> <dt>data</dt>
<dd>Vector of observations.</dd> <dt>n.burn</dt>
<dd>Length of the burn-in period, i.e., the point after which Gibbs sampler is supposed to attain convergence. By default <code>n.burn</code> is 8000.</dd> <dt>n.simul</dt>
<dd>Total numbers of Gibbas sampler iterations. By default <code>n.simul</code> is 10,000.</dd></dl>

Arguments

Author

Estimating parameters of the Weibull distribution using the Bayesian approach — fitbayesWeibull

<dl>

 <dt>data</dt>
<dd>Vector of observations.</dd>

 <dt>n.burn</dt>
<dd>Length of the burn-in period, i.e., the point after which Gibbs sampler is supposed to attain convergence. By default <code>n.burn</code> is 8000.</dd>

 <dt>n.simul</dt>
<dd>Total numbers of Gibbas sampler iterations. By default <code>n.simul</code> is 10,000.</dd>

</dl>

fitbayesWeibull: Estimating parameters of the Weibull distribution using the Bayesian approach

Description

Usage

Value

Arguments

Author

Details

References

Examples