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Version 2 (modified by Víctor de Buen Remiro, 14 years ago) (diff)

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Package QltvRespModel

Max-likelihood and bayesian estimation of qualitative response models.

Weighted Boolean Regresions

Abstract class @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.

\beta_k \sim N\left(\nu_k, \sigma_k \right)

l_k \le \beta_k \le u_k \wedge l_k < u_k

When \sigma_k is infinite or unknown we will express a uniform prior. When l_k = -\infty or unknown we will express that variable has no lower bound. When 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

A \beta \le a

Weighted Logit Regression

Class @WgtLogit is an specialization of class @WgtBoolReg that handles with weighted logit regressions.

Weighted Probit Regression

Class @WgtProbit is an specialization of class @WgtBoolReg that handles with weighted probit regressions.