Page
%P
14 Result(s)
-
Article
Quantifying functionals of age distributions in the wild by solving an operator equation
Residual demography is a recent concept that has proved to be a useful tool to gain insights about the age distributions of wild populations, especially insects. We develop an operator equation that permits th...
-
Book
-
Chapter
Introduction
If we analyse longitudinal data, we are usually interested in the estimation of the underlying curve which produces the observed measurements. This curve describes the time course of some measured quantity lik...
-
Chapter
Further Applications
The remarks made here concern typical problems in the medical field which can as well be encountered in other fields of application. Longitudinal medical data are not only collected with the aim of description...
-
Chapter
Nonparametric Regression Methods
Besides kernel estimators, commonly used nonparametric regression estimators are local least squares estimators and smoothing splines. Besides these estimators, we also discuss orthogonal series estimators whi...
-
Chapter
Fortran Routines for Kernel Smoothing and Differentiation
The programs listed below are suited for kernel estimation and differentiation (υ=0–3) with estimators (4.4); various kernels of different orders can be chosen and there are two options for bandwidth choices: ...
-
Chapter
Nonparametric Estimation of the Human Height Growth Curve
As an example of an application of some of the methods discussed before, the analysis of the human height growth curve by nonparametric regression methods is considered. The data that are analysed were obtaine...
-
Chapter
Longitudinal Data and Regression Models
There exist several kinds of longitudinal data, i.e., measurements (observations) of the same quantity (occurrence) on the same subject at different time points, each of which requires different methods for an...
-
Chapter
Consistency Properties of Moving Weighted Averages
We consider here the usual fixed design regression model $$ Y_{i,n} {\text{ = g(t}}_{i,n} ) + \varepsilon _{i,n} $$ with tri...
-
Chapter
Kernel and Local Weighted Least Squares Methods
It is assumed from now on that in the model (2.1) $$ {\text{Y}}_i {\text{,n-g(t}}_{i,n} ) + \varepsilon _{i,n} {\text{ ,i = 1,}}...{\text{,n}}...
-
Chapter
Multivariate Kernel Estimators
The kernel estimate (4.4) can be generalized to the case of a multivariate regression function g: A → ℝ where A ⊂ ℝm, m ≥ 1. The proofs usually can be generalized from the univariate case without difficulty. Ther...
-
Chapter
Longitudinal Parameters
In biomedical settings, a common problem is the comparison and description of samples of curves. Assuming there are N subjects and nj measurements are made for the j-th subject, we might describe the situation by...
-
Chapter
Optimization of Kernel and Weighted Local Regression Methods
Optimization here means minimization of the asymptotically leading term of the IMSE. Since the asymptotic expression for the IMSE is the same for both kernel and weighted local least squares methods, optimizat...
-
Chapter
Choice of Global and Local Bandwidths
For practice applications of curve smoothing methods, the choice of a good smoothing parameter is a very important issue. For kernel and weighted local least squares estimators this is the choice of the bandwi...
