ICQER¶
- class pystoned.ICQER.ICER(y, x, tau, z=None, cet='addi', fun='prod', rts='vrs')[source]¶
Isotonic convex expectile regression (ICER)
- __init__(y, x, tau, z=None, cet='addi', fun='prod', rts='vrs')[source]¶
ICER model
- Parameters:
y (float) – output variable.
x (float) – input variables.
tau (float) – expectile.
z (float, optional) – Contextual variable(s). Defaults to None.
cet (String, optional) – CET_ADDI (additive composite error term) or CET_MULT (multiplicative composite error term). Defaults to CET_ADDI.
fun (String, optional) – FUN_PROD (production frontier) or FUN_COST (cost frontier). Defaults to FUN_PROD.
rts (String, optional) – RTS_VRS (variable returns to scale) or RTS_CRS (constant returns to scale). Defaults to RTS_VRS.
- display_alpha()¶
Display alpha value
- display_beta()¶
Display beta value
- display_lamda()¶
Display lamda value
- display_negative_residual()¶
Dispaly negative residual value
- display_positive_residual()¶
Dispaly positive residual value
- display_residual()¶
Dispaly residual value
- display_status()¶
Display the status of problem
- get_adjusted_alpha()¶
Return the shifted constatnt(alpha) term by CCNLS
- get_adjusted_residual()¶
Return the shifted residuals(epsilon) tern by CCNLS
- get_alpha()¶
Return alpha value by array
- get_beta()¶
Return beta value by array
- get_frontier()¶
Return estimated frontier value by array
- get_lamda()¶
Return lamda value by array
- get_negative_residual()¶
Return negative residual value by array
- get_positive_residual()¶
Return positive residual value by array
- get_predict(x_test)¶
Return the estimated function in testing sample
- get_residual()¶
Return residual value by array
- get_status()¶
Return status
- 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.ICQER.ICQR(y, x, tau, z=None, cet='addi', fun='prod', rts='vrs')[source]¶
Isotonic convex quantile regression (ICQR)
- __init__(y, x, tau, z=None, cet='addi', fun='prod', rts='vrs')[source]¶
ICQR model
- Parameters:
y (float) – output variable.
x (float) – input variables.
tau (float) – quantile.
z (float, optional) – Contextual variable(s). Defaults to None.
cet (String, optional) – CET_ADDI (additive composite error term) or CET_MULT (multiplicative composite error term). Defaults to CET_ADDI.
fun (String, optional) – FUN_PROD (production frontier) or FUN_COST (cost frontier). Defaults to FUN_PROD.
rts (String, optional) – RTS_VRS (variable returns to scale) or RTS_CRS (constant returns to scale). Defaults to RTS_VRS.
- display_alpha()¶
Display alpha value
- display_beta()¶
Display beta value
- display_lamda()¶
Display lamda value
- display_negative_residual()¶
Dispaly negative residual value
- display_positive_residual()¶
Dispaly positive residual value
- display_residual()¶
Dispaly residual value
- display_status()¶
Display the status of problem
- get_adjusted_alpha()¶
Return the shifted constatnt(alpha) term by CCNLS
- get_adjusted_residual()¶
Return the shifted residuals(epsilon) tern by CCNLS
- get_alpha()¶
Return alpha value by array
- get_beta()¶
Return beta value by array
- get_frontier()¶
Return estimated frontier value by array
- get_lamda()¶
Return lamda value by array
- get_negative_residual()¶
Return negative residual value by array
- get_positive_residual()¶
Return positive residual value by array
- get_predict(x_test)¶
Return the estimated function in testing sample
- get_residual()¶
Return residual value by array
- get_status()¶
Return status
- 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.