Point separation in logistic regression on Hilbert space-valued variables

Hilbert Space-valued Mixingales

Efficiencies and Surrogate Variables in Logistic Regression

Raymond J. Carroll Department of Statistics Texas A&M University College Station, TX 77843 We study logistic regression with response y when the true predictor x is measured with· error and the observable data consist of pairs (y,w), where w is correlated with x. Two approaches to estimation are studied. In the first, integrated likelihood estimates are obtained from the conditional distributio...

Dealing with Separation in Logistic Regression Models

When facing small numbers of observations or rare events, political scientists often encounter separation, in which explanatory variables perfectly predict binary events or non-events. In this situation, maximum likelihood provides implausible estimates and the researcher might want incorporate some form of prior information into the model. The most sophisticated research uses Jeffreys’ invaria...

Phylogenetic logistic regression for binary dependent variables.

We develop statistical methods for phylogenetic logistic regression in which the dependent variable is binary (0 or 1) and values are nonindependent among species, with phylogenetically related species tending to have the same value of the dependent variable. The methods are based on an evolutionary model of binary traits in which trait values switch between 0 and 1 as species evolve up a phylo...

Zakai Equations for Hilbert Space Valued Processes*

versity A state process is described by either a discrete time Hilbert space valued process, or a stochastic differential equation in Hilbert space. The state is observed through a finite dimensional process. Using a change of measure and a Fusive theorem the Zakai equation is obtained in discrete or continuous time. A risk sensitive state estimate is also defined.