sCQER¶
- class pystoned.sCQER.sCER(y, x, tau, C)[source]¶
Simultaneous estimation of CER
- __init__(y, x, tau, C)[source]¶
sCER model
- Parameters:
y (float) – output variable.
x (float) – input variables.
tau – vector of expectile.
C – interval (small positive value)
- get_alpha()¶
Return alpha value by array
- get_beta()¶
Return beta value by array
- get_frontier()¶
Return estimated frontier value by array
- get_negative_residual()¶
Return negative residual value by array
- get_positive_residual()¶
Return positive residual value by array
- optimize(email='local', solver=None)¶
Optimize the function by requested method
- Parameters:
email (string) – The email address for remote optimization. It will optimize locally if OPT_LOCAL is given.
solver (string) – The solver chosen for optimization. It will optimize with default solver if OPT_DEFAULT is given.
- class pystoned.sCQER.sCQR(y, x, tau, C)[source]¶
Simultaneous estimation of CQR
- __init__(y, x, tau, C)[source]¶
sCQR model
- Parameters:
y (float) – output variable.
x (float) – input variables.
tau – vector of quantile.
C – interval (small positive value)
- optimize(email='local', solver=None)[source]¶
Optimize the function by requested method
- Parameters:
email (string) – The email address for remote optimization. It will optimize locally if OPT_LOCAL is given.
solver (string) – The solver chosen for optimization. It will optimize with default solver if OPT_DEFAULT is given.