One of the main practical problems of nonparametric regression estimation is the curse of dimensionality. The curse of dimensionality arises because nonparametric regression estimates are dependent variable averages local to the point at which the regression function is to be estimated. The number of observations ‘local’ to the point of estimation decreases exponentially with the number of dime...

Title of dissertation: ASYMPTOTIC NORMALITY IN GENERALIZED LINEAR MIXED MODELS Min Min Doctor of Philosophy, 2007 Dissertation directed by: Professor Paul J. Smith Statistics Program Department of Mathematics Generalized Linear Mixed Models (GLMMs) extend the framework of Generalized Linear Models (GLMs) by including random effects into the linear predictor. This will achieve two main goals of ...

We address the problem of estimating generalized linear models when some covariate values aremissing but imputations are available to fill-in the missing values. This situation generates a bias-precision tradeoff in the estimation of the model parameters. Extending the generalized missing-indicator method proposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a p...

We consider generalized partially linear measurement error models when the linear covariate is measured with additive errors. Estimators of parameter and nonparametric function obtained by local liner regression with kernel weight, the SIMEX technique, and generalized estimating equation are proposed. The asymptotic normality of the estimate of the parameter and the asymptotics of the estimate ...

‎In this article‎, ‎we develop two nonparametric smoothing estimators for parameter of a time-variant parametric model‎. ‎This parameter can be from any parametric family or from any parametric or semi-parametric regression model‎. ‎Estimation is based on a two-step procedure‎, ‎in which we first get the raw estimate of the parameter at a set of disjoint time...