Improved estimation in cumulative link models

Improved estimation in cumulative link models

Bayesian Model Choice in Cumulative Link Ordinal Regression Models

The use of the proportional odds (PO) model for ordinal regression is ubiquitous in the literature. If the assumption of parallel lines does not hold for the data, then an alternative is to specify a non-proportional odds (NPO) model, where the regression parameters are allowed to vary depending on the level of the response. However, it is often difficult to fit these models, and challenges reg...

Cumulative Risk Estimation for Chemical Mixtures

In reality, humans are always exposed to a combination of toxic substances and seldom to a single agent. Simultaneous exposure to a multitude of chemicals could result in unexpected consequences. The combined risk may lead to greater or less than a simple summation of the effects induced by chemicals given individually. Here, a method is proposed for estimating the cumulative risk which is the ...

Improved Estimation in Time Varying Models

Locally adapted parameterizations of a model (such as locally weighted regression) are expressive but often suffer from high variance. We describe an approach for reducing this variance, based on the idea of estimating simultaneously a transformed space for the model and locally adapted parameterizations expressed in the new space. We present a new problem formulation that captures this idea an...

Cumulative distribution networks: Inference, estimation and applications of graphical models for cumulative distribution functions

Cumulative distribution networks: Inference, estimation and applications of graphical models for cumulative distribution functions Jim C. Huang Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2009 This thesis presents a class of graphical models for directly representing the joint cumulative distribution function (CDF) of many random variabl...