Maximum Likelihood Estimation of TPXG Regression Coefficients {TPXG} | R Documentation |
Estimation of log-link TPXG regression coefficients.
Description
This function estimates the Two Parameter Xgamma regression coefficients as well as the
\alpha
parameter of the Two Parameter Xgamma distribution using the maximum likelihood method.
Usage
tpxg.reg(y,x)
Arguments
y |
A numeric vector containg strictly positive values. |
x |
A matrix or a data.frame with the predictor variables. |
Details
This implementation employs a logarithmic link function to relate the \theta
parameter of the
Two-Parameter Xgamma distribution to the predictor variables.
Specifically, the relationship is defined as:
\theta=e^{X\beta}
where X is a matrix whose columns represent the predictor variables, and
\beta
is a column vector of corresponding regression coefficients.
Value
A named list containing \alpha
parameter, a vector containing the \beta
coefficients and the maximum likelihood value.
Author(s)
Nikolaos Kontemeniotis.
R implementation and documentation: Nikolaos Kontemeniotis kontemeniotisn@gmail.com and Michail Tsagris mtsagris@uoc.gr.
References
"Sen, S., Chandra, N. and Maiti, S. S. (2018). On properties and applications of a two-parameter XGamma distribution. Journal of Statistical Theory and Applications, 17(4): 674–685."
See Also
Examples
x <- matrix( rnorm(100 * 2), ncol = 2 )
y <- rtpxg(100)
tpxg.reg(y, x)