We study a semiparametric generalized additive coefficient model, in which linear predictors in the conventional generalized linear models is generalized to unknown functions depending on certain covariates, and approximate the nonparametric functions by using polynomial spline. The asymptotic expansion with optimal rates of convergence for the estimators of the nonparametric part is establishe...

Wet/dry spell characteristics of daily precipitation are of interest for a number of hydrologic applications (e.g., flood forecasting or assessment of erosion potential). Here, we examine issues related to designing an appropriate nonparametric scheme that focuses on spell characteristics for resampling historical daily precipitation data. A subset of the nonparametric wet/dry spell model prese...

We employ a nonlinear, nonparametric method to model the stochastic behavior of changes in several short and long term U.S. interest rates. We apply a nonlinear autoregression to the series using the locally weighted regression (LWR) estimation method, a nearest-neighbor method, and evaluate the forecasting performance with a measure of root mean square error (RMSE). We compare the forecasting ...

This paper is concerned with a semiparametric partially linear regression model with unknown regression coefficients, an unknown nonparametric function for the non-linear component, and unobservable Gaussian distributed random errors. We present a wavelet thresholding based estimation procedure to estimate the components of the partial linear model by establishing a connection between an l1-pen...

The partly linear additive Cox model is an extention of the (linear) Cox model and allows flexible modeling of covariate effects semiparametrically. We study asymptotic properties of the maximum partial likelihood estimator of this model with right-censored data using polynomial splines. We show that, with a range of choices of the smoothing parameter (the number of spline basis functions) requ...

We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMMs are a class of models that use a nonparametric function to model a time effect, a parametric function to model other covariate effects, and parametric or nonparametric random effects to account for the within-subject correlation. We model the nonparametric function using a Bayesian formulation of ...

where (εi)i=1,...,n are i.i.d. random variables. The unknown autoregression function f is then the target of statistical inference and the development of efficient estimators is a natural task for theoretically oriented statisticians. On the one hand, it has been recognized for a long time that commonly used estimators in model (1) have the same asymptotic behavior as corresponding estimators i...

This study describes a new graphical method for assessing and characterizing e$ect modi%cation by a matching covariate in matched case–control studies. This method to understand e$ect modi%cation is based on a semiparametric model using a varying coe2cient model. The method allows for nonparametric relationships between e$ect modi%cation and other covariates, or can be useful in suggesting para...

This paper considers the nonparametric and semiparametric methods for estimating regression models with continuous endogenous regressors. We list a number of different generalizations of the linear structural equation model, and discuss how three common estimation approaches for linear equations — the “instrumental variables,” “fitted value,” and “control function” approaches — may or may not b...

This paper introduces a new speci…cation for the heterogeneous autoregressive (HAR) model for the realized volatility of S&P500 index returns. In this new model, the coe¢ cients of the HAR are allowed to be time-varying with unknown functional forms. We propose a local linear method for estimating this TVC-HAR model as well as a bootstrap method for constructing con…dence intervals for the time...