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To calculate the semideviation, we identify the returns that fall below the mean return. Then we take the difference between each of these returns and the mean return, square each difference, sum the squared differences, and divide the sum of the squared differences by the total number of returns. This gives the semivariance. Taking the square root of the semivariance converts the statistic into units of return, meaning that the statistic is directly comparable with returns. Exhibit 10.2 illustrates the calculation of semideviation using the same return series that we have been working with from Exhibit 8.1. Fund semideviation is 3.16% and benchmark semideviation is equal to 2.76%. Notice that in the calculation of semideviation the denominator is the total number of observations. We are calculating an average of the below average deviations from the mean, spread over the total number of observations. Here, we divide by thirteen months even though only six returns fell below the fund mean return. Our formula is equivalent to calculating the standard deviation with the replacement of above average deviations with zero. The semideviation measures the volatility implied by returns that fall below the average return. Exhibit 10.3 shows that it is the portion of the return distribution to the left of the mean that counts as risky, using semideviation as a measure. Statistics that measure a portion of the distribution are called partial moments, and downside risk statistics are lower partial moments. Using the lower partial moments addresses the criticism that standard devia-

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The applications of activation analysis are almost innumerable. In the physical sciences, activation analysis is used in trace-element analysis of semiconductor materials, metals, meteorites, lunar samples, and terrestrial rocks. In most cases, the multielemental analysis feature of activation analysis is used to measure the concentrations of several trace-elements simultaneously. From these detailed studies of trace-element abundance patterns, one has been able to deduce information about the thermal and chemical history of the Earth, moon, Mars, and meteorites, as well as the source or age of an object. The use of activation analysis in criminal investigations ( forensic activation analysis) is also well established. The basic idea here is to match the traceelement distributions found in bullets, paint, oil, and so on found at the scene of a crime with the trace-element distributions in objects found with criminal suspects. Such identi cation is rapid and nondestructive (allowing the actual evidence to be presented in court). Moreover, the probability of its correctness can be ascertained quantitatively. Other prominent examples of the use of forensic activation analysis involve con rmation of the notion that Napoleon was poisoned (by nding signi cant amounts of arsenic in hair from his head) and the nding that the activation analysis of the wipe samples taken from a suspect s hand can reveal not only if he or she has red a gun recently but also the type of gun and ammunition used. Applications of activation analysis in the environmental sciences are routine. Determinations of the trace element content of urban atmospheres, lakes, streams,

1 (B = 30) (TWTA, P = 15) 27 (B = 36) (TWTA, P = 60)

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on a given criteria. The current implementation searches the registry by NAICS common classification schemes. There are various other search possibilities, but we will only list one in this case study. The service requestor will use JAXLookupHelper (see Figure 12.8) in the CatalogHelper class to locate the location (URL) of the service endpoint. This search is implemented in a query class called JAXRQueryByNaicsClassification. Other query classes can be plugged into the helper to provide different search criteria such as by WSDL, by service name, and so forth.

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