We propose a semiparametric two-phase regression estimator with a semiparametric generalized propensity score estimator for estimating average treatment effects in the presence of informative first-phase sampling. The proposed estimator can be easily extended to any number of treatments and does not rely on a prespecified form of the response or outcome functions. The proposed estimator is show...

This article describes the asymptotic properties of local polynomial regression estimators for univariate and additive models when observation weights are included. Such weighted additive models are a crucial component of local scoring, the widely used estimation algorithm for generalized additive models described in Hastie and Tibshirani (1990). The statistical properties of the univariate loc...

Abstract: Poisson regression models for count variables have been utilized in many applications. However, in many problems overdispersion and zero-inflation occur. In this paper we study regression models associated with the generalized Poisson distribution (Consul (1989)). These regression models which have been used for about 15 years do not belong to the class of generalized linear models co...

Recently Fan and colleagues have proposed two measures of the strength of dependency between failure time variates over a finite region of the sample space; namely, an average relative risk measure and a finite region version of Kendall’s tau. Here, these dependency measures are generalized to accommodate regression effects on marginal hazard functions. Specifically, the dependency measures pre...

We use the model-calibrated pseudo empirical likelihood method to construct estimators for the finite population distribution function. Under an assumed superpopulation working model, the proposed estimators have minimum model expectation of asymptotic design-based variance among a class of estimators and therefore are optimal in that class. The estimators are asymptotically designunbiased irre...

This paper analyses the properties of standard estimators, tests, and con…dence sets (CS’s) in a class of models in which the parameters are unidenti…ed or weakly identi…ed in some parts of the parameter space. The paper also introduces a method to make the tests and CS’s robust to such identi…cation problems. The results apply to a class of extremum estimators and corresponding tests and CS’s,...