Changes between Initial Version and Version 1 of OfficialTolArchiveNetworkQltvRespModel

Timestamp:

Dec 20, 2010, 7:20:27 PM (15 years ago)

Author:

Víctor de Buen Remiro

Comment:

--

OfficialTolArchiveNetworkQltvRespModel

@@ +1 @@
+= Package QltvRespModel =
+
+Max-likelihood and bayesian estimation of qualitative response models.
+
+== Weighted Boolean Regresions ==
+
+Abstract class 
+[source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtBoolReg.tol @WgtBoolReg] 
+is the base to inherit weighted boolean regressions as logit or probit or any other 
+given justthe scalar distribution function.
+
+This class implements max-likelihood by means of package 
+[wiki/OfficialTolArchiveNetworkNonLinGloOpt NonLinGloOpt] and bayesian estimation 
+using [wiki/OfficialTolArchiveNetworkBysSampler BysSampler].
+
+User can and should define scalar truncated normal or uniform prior information and 
+bounds for all variables for which he/she has robust knowledge.[[BR]] [[BR]]
+[[LatexEquation( \beta_k \sim N\left(\nu_k, \sigma_k \right) )]]
+[[LatexEquation( l_k \le \beta_k \le u_k \wedge l_k < u_k)]]
+When [[LatexEquation( \sigma_k )]] is infinite or unknown we will express a uniform
+prior.
+When [[LatexEquation( l_k = -\infty)]] or unknown we will express that variable 
+has no lower bound.
+When [[LatexEquation( u_k = +\infty)]] or unknown we will express that variable
+has no upper bound.
+
+It's also allowed to give any set of constraining linear inequations [[BR]] [[BR]]
+[[LatexEquation( A \beta \le a )]]
+
+=== Weighted Logit Regression ===
+Class [source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtLogit.tol @WgtLogit] 
+is an specialization of class 
+[source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtBoolReg.tol @WgtBoolReg]
+that handles with weighted logit regressions.
+
+
+=== Weighted Probit Regression ===
+Class [source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtProbit.tol @WgtProbit] 
+is an specialization of class 
+[source:/tolp/OfficialTolArchiveNetwork/QltvRespModel/WgtBoolReg.tol @WgtBoolReg]
+that handles with weighted probit regressions.