Regression analysis of correlated ordinal data using orthogonalized residuals

ORTH: R and SAS software for regression models of correlated binary data based on orthogonalized residuals and alternating logistic regressions

This article describes a new software for modeling correlated binary data based on orthogonalized residuals, a recently developed estimating equations approach that includes, as a special case, alternating logistic regressions. The software is flexible with respect to fitting in that the user can choose estimating equations for association models based on alternating logistic regressions or ort...

On Gamma Regression Residuals

In this paper, ‎we propose new residuals for gamma regression models, ‎assuming that both mean and shape parameters follow regression structures. The  models are summarized and fitted by applying both classic and Bayesian methods as proposed by Cepeda-Cuervo (2001). The residuals are proposed from properties of the biparametric exponential family of distributions. ‎Simulated an...

Generating correlated discrete ordinal data using R and SAS IML

Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the perfo...

Complexity-Regularized Regression for Serially-Correlated Residuals with Applications to Stock Market Data

Abstract: A popular approach in the investigation of the short-term behavior of a non-stationary time series is to assume that the time series decomposes additively into a long-term trend and short-term fluctuations. A first step towards investigating the short-term behavior requires estimation of the trend, typically via smoothing in the time domain. We propose a method for time-domain smoothi...

Marginal Regression Models with VaryingCoeÆ ients for Correlated Ordinal