dgsm

data

omega

beta

<p>If <code>TRUE</code>, then log(pdf) is returned.</p>

<p>Computes probability density function (pdf) of the gamma shape mixture (GSM) model. The general form for the pdf of the GSM model is given by
$$f(x,{\Theta}) = \sum_{j=1}^{K}\omega_j \frac{\beta^j}{\Gamma(j)} x^{j-1} \exp\bigl( -\beta x\bigr),$$
where $\Theta=(\omega_1,\dots,\omega_K, \beta)^T$ is the parameter vector and known constant $K$ is the number of components. The vector of mixing parameters is given by $\omega=(\omega_1,\dots,\omega_K)^T$ where $\omega_j$s sum to one, i.e., $\sum_{j=1}^{K}\omega_j=1$. Here $\beta$ is the rate parameter that is equal for all components.</p>

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 four - parameter
Johnson SB distribution, and 9 ) Robust multiple linear regression analysis when error
term follows skewed t distribution.

dgsm: Computing probability density function of the gamma shape mixture model

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