We assume ε(t) | {X(s), y(s− 1), s ≤ t} ∼ N(0,Σ). This is the assumption that X(t) is predetermined in this system. In this notation, each equation (column of the system) has the same X(t) variable on the right-hand side and a distinct coefficient vector (column of B). However, we can consider versions of the system with 0 constraints on elements of B, which create different lists of variables ...

In this tutorial we show how complete hierarchical multinomial marginal (HMM) models for categorical variables can be defined, estimated and tested using the hmmm package.

This paper extends the Bayes marginal model plot (BMMP) model assessment technique from a traditional logistic regression setting to a multilevel application in the area of criminal justice. Convicted felons in the United States receive either a prison sentence or a less severe jail or non-custodial sentence. Researchers have identified many determinants of sentencing variation across the count...

We examine bias corrections which have been proposed for the Fixed Effects Panel Probit model with exogenous regressors, using several different data generating processes to evaluate the performance of the estimators in different situations. We find a best estimator across all cases for coefficient estimates, but when the marginal effects are the quantity of interest no analytical correction is...

This paper is concerned with combined inference for point processes on the real line observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point processes. For a range of models, the marginal and conditional distributions are derived. We discuss likelihood based inference as well as parameter estimat...

Zeger and Liang (1992) allege that regression parameter estimates from transition models tend to be smaller in absolute value than regression parameter estimates from marginal models. This statement is true in typical longitudinal data situations for the relation between the true parameters of the marginal and transition model. However, under the assumption of a transition model the Generalized...

To explore human deviations from Bayes’ rule in numerically explicit problems, prior and likelihood probabilities or frequencies are manipulated and their effects on posterior probabilities or surprisals are measured. Results show that people use both priors and likelihoods in Bayesian directions, but the effect of likelihood information is stronger than that of prior information. Use of freque...

Methods for group comparisons using predicted probabilities and marginal effects on probabilities are developed for regression models for binary outcomes. Unlike approaches based on the comparison of regression coefficients across groups, the methods we propose are unaffected by the identification of the coefficients and are expressed in the natural metric of the outcome probability. While we d...