“…Note that the sign of the expected innovation is completely determined by μ j :n . Following Crawford (1995), we use Teichroew's (1956) Table 1 for the standardized normal distribution to obtain the values of μ j :n reported in Table 3. Table 3 also reports the average change in the order statistic over the first seven periods by treatment.…”
“…Note that the sign of the expected innovation is completely determined by μ j :n . Following Crawford (1995), we use Teichroew's (1956) Table 1 for the standardized normal distribution to obtain the values of μ j :n reported in Table 3. Table 3 also reports the average change in the order statistic over the first seven periods by treatment.…”
“…In large samples the trimmed mean and the trimmed standard deviation (the mean and sample standard deviation of "those observed values remaining out of a sample of n when the g highest and g lowest values have been deleted) have been shown to have quite satisfactory properties [5]. This report opens an investigation into the properties of both these and related statistics in small and moderate samples.…”
“…standard normal random variables. The means and variances of Gaussian order statistics have been studied extensively [30]- [32], and the value of for any can be found in [31]. For example, and .…”
Abstract-This paper studies the quantization of prior probabilities, drawn from an ensemble, in distributed detection with data fusion by combination of binary local decisions. Design and performance equivalences between a team of agents and a more powerful single agent are obtained. Effects of identical quantization and diverse quantization on mean Bayes risk are compared. It is shown that when agents using diverse quantizers interact to agree on a perceived common risk, the effective number quantization levels is increased. With this collaboration, optimal diverse regular quantization with cells per quantizer performs as well as optimal identical quantization with cells per quantizer. Similar results are obtained for the maximum Bayes risk error criterion.
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