The most widely used measure of segregation is the dissimilarity index, D. It is now well understood that this measure also reflects randomness in the allocation of individuals to units; that is, it measures deviations from evenness not deviations from randomness. This leads to potentially large values of the segregation index when unit sizes and/or minority proportions are small, even if there...

A key problem in structured output prediction is direct optimization of the task reward function that matters for test evaluation. This paper presents a simple and computationally efficient approach to incorporate task reward into a maximum likelihood framework. We establish a connection between the log-likelihood and regularized expected reward objectives, showing that at a zero temperature, t...

1 Summary Conditional likelihood approach is a sensible choice for a hierarchical logistic regression model or other generalized regression models with binary data. However, its heavy computational burden limits its use, especially for the related mixed effects model. In this paper, we use modified profile likelihood as an accurate approximation to conditional likelihood, and then propose the u...

Language models that combine stochastic grammars and N-grams are often used in speech recognition and language understanding systems. One useful aspect of these models is that they can be used to annotate phrases in the text with their constituent grammars; such annotation often plays an important role in subsequent processing of the text. In this paper we present an estimation procedure, under...

Consider a pair of point processes, X and Y , where X is regarded as a `true' point process and Y is an imperfect observation of X. For the transformation from X to Y , we consider a number of disturbance mechanisms covering random thinning, displacement, censoring of the displaced points and super-position of extra points. We present the conditional likelihood of Y given X. When both point pro...

The estimation and inference for conditional estimating equations models with endogenous selection, are considered. approach takes into account possible selection which may lead to a bias. It can be used wide range of statistical not covered by the model-based sampling theory. Endogeneity either part or within covariates. is particularly well suited unknown heteroscedasticity, uncontrolled conf...