eigp_sampling

Contains the framework of the estimation, sampling, and hypotheses testing for two special distributions (Exponentiated Exponential-Pareto and Exponentiated Inverse Gamma-Pareto) within the family of Generalized Exponentiated Composite distributions. The detailed explanation and the applications of these two distributions were introduced in Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.1080/03610926.2022.2050399>, Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/math10111895>, and Bowen Liu, Malwane M.A. Ananda (2022) <doi:10.3390/app13010645>.

Bowen Liu

Generalized Exponentiated Composite Distributions

Malwane M.A. Ananda

Dwaipayan Mukhopadhyay

eigp_sampling function

<dl><dt>n</dt>
<dd>Number of observations. (n&gt;=1)</dd>
<dt>theta</dt>
<dd>The location parameter for the parent IGP distribution (eta = 1). The value needs to be positive.</dd>
<dt>eta</dt>
<dd>The exponent parameter. The parameter should be positive.</dd></dl>

Arguments

The Random Number Generation Function for EIGP — eigp_sampling

<dl>

<dt>n</dt>
<dd>Number of observations. (n&gt;=1)</dd>


<dt>theta</dt>
<dd>The location parameter for the parent IGP distribution (eta = 1). The value needs to be positive.</dd>


<dt>eta</dt>
<dd>The exponent parameter. The parameter should be positive.</dd>

</dl>

eigp_sampling: The Random Number Generation Function for EIGP

Description

Usage

Value

Arguments

Details

Examples