On Using Multivariate Nonparametric Regression Methods

Multivariate nonparametric regression using lifting

For regularly spaced one-dimensional data wavelet shrinkage has proven to be a compelling method for nonparametric function estimation. We argue that this is not the case for irregularly spaced data in two or higher dimensions. This article develops three methods for the multiscale analysis of irregularly spaced data based on the recently developed lifting paradigm by “lifting one coefficient a...

Multivariate Nonparametric Regression

As in many areas of biostatistics, oncological problems often have multivariate predictors. While assuming a linear additive model is convenient and straightforward, it is often not satisfactory when the relation between the outcome measure and the predictors is either nonlinear or nonadditive. In addition, when the number of predictors becomes (much) larger than the number of independent obser...

Estimating Rate Equations Using Nonparametric Regression Methods

Nonparametric Regression Using Kernel and Spline Methods

When applying nonparametric regression methods, the researcher is interested in estimating the relationship between one dependent variable, Y , and one or several covariates, X1, . . . , Xq. We discuss here the situation with one covariate, X (the case with multiple covariates is addressed in the references provided below). The relationship between X and Y can be expressed as the conditional ex...

Nonparametric Bayesian Regression Methods

A common problem in statistics, and other disciplines , is to approximate adequately a function of several variables. In this paper we review some possible nonparametric Bayesian models with which we can perform this multiple regression problem. We shall also demonstrate how these basic models can be extended to allow the analysis of time series , both conventional and nancial, survival analysi...