It is well known that linkage analysis using simple random sib-pairs has relatively low power for detecting quantitative trait loci with small genetic effects. The power can be substantially increased by using samples selected based on their trait values. Usually, samples that are obtained by truncation selection consist of random samples from a truncated trait distribution. In this article we ...

In this work, we present coding techniques that enable progressive transmission when trellis coded quantization (TCQ) is applied to wavelet coefficients. A method for approximately inverting TCQ in the absence of least significant bits is developed. Results are presented using different rate allocation strategies and different entropy coders. The proposed wavelet-TCQ coder yields excellent codi...

This paper considers the joint problem of dirty-paper and Wyner-Ziv coding with partial host information at the receiver. The rate-distortion and the capacity expressions have been derived for the Gaussian case. It is shown that the availability of the partial host information to the receiver has positive effects to the compression performance and the overall capacity. Moreover, a practical cod...

To justify the use of sampling to solve stochastic programming problems one usually relies on a law of large numbers for random lsc (lower semicontinuous) functions when the samples come from independent, identical experiments. If the samples come from a stationary process, one can appeal to the ergodic theorem proved here. The proof relies on thèscalarization' of random lsc functions.

A composite random sampling design was used to estimate the concentrations of hydrocarbons in sediments from two near-shore areas of Scotland (Firth of Clyde and Firth of Forth). The aim of this work was to estimate a mean value for each parameter in these areas, and to determine whether this can be done with more thorough coverage (better representation), better precision and less variance at ...

This paper is intended to provide the reader with an introduction to ranked set sampling, a statistical technique for data collection that generally leads to more efficient estimators than competitors based on simple random samples. Methods for obtaining ranked set samples are described and the structural differences between ranked set samples and simple random samples are discussed. Properties...

It is previously shown that proper random linear samples of a finite discrete signal (vector) which has a sparse representation in an orthonormal basis make it possible (with probability 1) to recover the original signal. Moreover, the choice of the linear samples does not depend on the sparsity domain. In this paper, we will show that the replacement of random linear samples with deterministic...

Samples can be categorized into two major groups, random and nonrandom. In random samples each person in a population (such as all fifth-graders in the country) has a known (and usually equal) probability of being selected; who gets picked is a matter of blind chance. In nonrandom samples, people are selected on some basis other than chance; who gets chosen in nonrandom studies is usually a mat...