Two algorithms for fitting constrained marginal models

Two algorithms for fitting constrained marginal models

The two main algorithms that have been considered for fitting constrained marginal models to discrete data, one based on Lagrange multipliers and the other on a regression model, are studied in detail. It is shown that the updates produced by the two methods are identical, but that the Lagrangian method is more efficient in the case of identically distributed observations. A generalization is g...

Algorithms for Fitting the Constrained Lasso

We compare alternative computing strategies for solving the constrained lasso problem. As its name suggests, the constrained lasso extends the widely-used lasso to handle linear constraints, which allow the user to incorporate prior information into the model. In addition to quadratic programming, we employ the alternating direction method of multipliers (ADMM) and also derive an efficient solu...

An application of model-fitting procedures for marginal structural models.

Marginal structural models (MSMs) are being used more frequently to obtain causal effect estimates in observational studies. Although the principal estimator of MSM coefficients has been the inverse probability of treatment weight (IPTW) estimator, there are few published examples that illustrate how to apply IPTW or discuss the impact of model selection on effect estimates. The authors applied...

Fitting Algorithms for MMPP ATM Traffic Models

In this paper, we propose and study fitting algorithms for MMPP(2) and CMPP ATM traffic models, which are special cases of Markov Modulated Poisson Processes (MMPP). Two fitting algorithms, both based on the cell interarrival times, are considered for the MMPP(2) model: one fits the cumulative distribution and auto-covariance functions and other fits the first three moments and auto-covariance ...

MCMC algorithms for fitting Bayesian models