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The method relies on the calculation of a discriminant function for each group. There will be k functions generated based on the number of unique categories the response variable can take. If the response variable is color with possible values red, green, and blue, then k will be 3. Predictions are made by calculating a score using each group s discriminant function. An observation is predicted to be a member of the group with the highest discriminant function score. Similar to multiple linear regression, linear discriminant analysis attempts to identify a solution that minimizes the overall error. By making the assumptions described earlier, that is, a normal distribution, with similar group covariance matrices, the following formula can be used to estimate a linear discriminant function: 1 T S ^ ^ S ^ p fk xT ^ 1 mk mk ^ 1 mk log ( ^k ) 2 ^ In this formula, x is a vector of values for a single observation, S is an estimate of the covariance matrix, mk is a vector of mean values for the variables correspond^ ing to group k, and ^k is an estimate of the prior probability. The superscript 1 p represents an inverse matrix and the superscript T represents a transposed matrix. More information on how this formula was derived can be found in Hastie (2003) and Rencher (2002). One approach to calculating ^ the estimate of the covariance, is to calculate S, the covariance matrix (described in Section 3.3.5) for each of the groups and then combine the individual matrices into a single pooled covariance matrix. ^k represents the prior probability and can be estimated using Nk , which is the p number of observations in category k: ^k p Nk N



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n this chapter, we examine diffusion processes that are approximated by threestate lattices known as trinomial trees. In section 4.1, we discuss the general movement of assets in a Cox-Ross-Rubinstein (CRR) framework, as well as in a Jarrow-Rudd (JR) framework. In section 4.2, we examine the JR trinomial tree in more detail. In section 4.3, we examine the CRR trinomial tree in more detail. In section 4.4, we discuss the optimal parameter for changing branching probabilities. In section 4.5, we provide trinomial tree implementations for the CRR and JR trees. In section 4.6, we give an implementation for building generic trinomial trees that approximate various diffusion processes. In section 4.7, we discuss implied trees that are based on constructing trinomial trees based on Arrow-Debreu state prices.





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4.2 Digital modulation Around the modem itself, a few blocks of Figure 4.2-14 require a short explanation:

Central is the payment itself, which is associated with the debtor agent and creditor agent, which are both financial institutions. The payment is also associated with a debtor and creditor, which are both parties (i.e. persons or organisations, financial or other), which in turn have elements such as a name and address. Additionally, these parties may be owners of an account. Behind these elements lie further details. A payment, for example, contains elements such as currency and amount, a requested execution date and settlement date, and remittance information.

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Asset Profit # # Max Trades Days Draw MDD MCL P:MD P:L Ratio %W Time %

DBCP provides the actual connection-pooling library that we will use but it is dependent on the other two .jar files. Download the binary distributions for each of these projects and decompress the files into a working directory. (It s a good idea to use a common jakarta directory for all Jakarta projects that you download.) After the files have been unpacked, we need to install the .jar files for Tomcat to use. The best way to do this is to copy the files into the CATALINA_HOME/common/lib subdirectory. On Linux, you need to remember to set the ownership and permissions to match those of the rest of the Tomcat installation. Following is an example of doing this on our system:

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