Statistical Modelling for Plant Size Distributions
Description
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)
, 8 ) The Bayesian inference, computing probability
density function, cumulative distribution function, and generating realizations
from four-parameter 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.