5 papers
Why stratification may hurt, & how much
Chris A. J. Klaassen, Andries J. Lenstra
There are circumstances under which stratified sampling is worse than simple random sampling, even if the allocation of the sample sizes is optimal. This phenomenon was discovered…
A Comparison of Information Concerning the Regression Parameter in The Accelerated Failure Time Model under Current Duration and Length Biased Sampling: Does it Pay to be Patient?
Bert van Es, Chris A. J. Klaassen, Philip J. Mokveld
Longitudinal observations are sometimes costly or not available. Cross sectional sampling can be an alternative. Observations are drawn then at a specific point in time from a popu…
Efficient estimation in the accelerated failure time model under cross sectional sampling
Chris A. J. Klaassen, Philip J. Mokveld, Bert van Es
Consider estimation of the regression parameter in the accelerated failure time model, when data are obtained by cross sectional sampling. It is shown that it is possible under reg…
Asymptotically efficient estimation of linear functionals in inverse regression models
Chris A. J. Klaassen, Eun-Joo Lee, Frits H. Ruymgaart
In this paper we will discuss a procedure to improve the usual estimator of a linear functional of the unknown regression function in inverse nonparametric regression models. In Kl…
Discrete Spacings
Chris A. J. Klaassen, J. Theo Runnenburg
Consider a string of positions, i.e. a discrete string of length . Units of length are placed at random on this string in such a way that they do not overlap, and as oft…