Consistent Significance Controlled Variable Selection in
Generalized Linear Regression
Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc.

Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.

Prof. BOUKHETALA Kamal <kboukhetala@usthb.dz>, Mr. GUIDOUM
Arsalane <starsalane@gmail.com>
R (>= 2.11.0), tcltk, tcltk2 , stats4 , xlsx
Simulation of diffusion processes and numerical solution
of stochastic differential equations. Analysis of discrete-time
approximations for stochastic differential equations (SDE)
driven by Wiener processes,in financial and actuarial modeling
and other areas of application.

R (>= 2.11.0), tcltk, tcltk2, stats4, rgl ,xlsx
Simulation of diffusion processes and numerical solution
of stochastic differential equations. Analysis of discrete-time
approximations for stochastic differential equations (SDE)
driven by Wiener processes,in financial and actuarial modeling
and other areas of application for example modelling and
simulation of dispersion in shallow water using the attractive
center (K.BOUKHETALA,1996). Simulation and statistical analysis
of the first passage time (FPT) and M-samples of the random
variable X(v) given by a simulated diffusion process.

BOUKHETALA Kamal <kboukhetala@usthb.dz>, GUIDOUM
Arsalane <starsalane@gmail.com>
Simulation of diffusion processes and numerical solution
of stochastic differential equations. Analysis of discrete-time
approximations for stochastic differential equations (SDE)
driven by Wiener processes,in financial and actuarial modeling
and other areas of application for example modelling and
simulation of dispersion in shallow water using the attractive
center (K.BOUKHETALA, 1996). Approximated the evolution of conditional
law a diffusion process with three methods Euler, Kessler and Shoji-Ozaki.
Simulation and statistical analysis of the first passage time (FPT) and M-samples of the random
variable X(v) given by a simulated diffusion process.