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4.2.1.3 Microarray Data Transformation After the numerical readings are obtained from the image, the objective of microarray data transformation is to identify outliers in the data and to adjust the data to meet the distribution assumptions implied by statistical analysis methods. A simple logarithmic transformation illustrated in Figure 4.6 is commonly used. It reshapes the data distribution into a bell shape that resembles normal distribution. This transformation is especially bene cial for data from dual-channel arrays, since data from these arrays are often expressed as ratios of signal intensities of pairs of samples. Alternative transformations used in practice include arcsinh function, linlog transformation, curve- tting transformations, and shift transformation [9]; among them, the linlog transformation was demonstrated to be the most bene cial. 4.2.2 Handling Missing Values

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On referring to the gure of the complete Grid (Figure 48) the following features will be noticed:

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Graphically, the Sharpe Ratio is the slope of the line from the risk-free rate to the portfolio in question. Exhibit 8.11 shows two portfolios. I know that the Sharpe Ratio for Portfolio 2 is higher than that for Portfolio 1, because the slope of the line to Portfolio 2 is higher than the slope of the line to Portfolio 1. Finally, let s look at the relation between the Sharpe Ratio and the ef cient frontier. In Exhibit 8.12, Portfolios 1 and 2 have the same level of risk. Note that the Sharpe Ratio is higher for Portfolio 2 than for Portfolio 1. Also note that there is no feasible portfolio with this risk level that has a higher expected return. What this means is that points on the ef cient frontier represent the highest Sharpe Ratio for that level of risk. Put another way, every point on an ef cient frontier is a maximum Sharpe Ratio the maximum Sharpe Ratio for that level of risk. So it follows that if you have the maximum Sharpe Ratio, you will be on the ef cient frontier.

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EXAMPLE: Sam Seller sells his house to Betty Buyer for $90,000. Sam has an existing mortgage with a balance of $60,000 with interest at 8 percent. Betty puts $5,000 down and gives Sam a mortgage for the remaining $85,000 at an interest rate of, say, 11 percent. Sam receives monthly payments from Betty and then uses a portion of the money to make the monthly payments on his underlying $60,000 mortgage. In addition to making 11 percent interest on the $25,000 Sam is effectively lending to Betty to buy his house, Sam is also making the interest spread (11 percent to 8 percent) on the underlying mortgage of $60,000. The new $85,000 mortgage to Betty is said to wrap around the old mortgage hence the name wraparound mortgage. This interest spread is the reason most residential mortgages can t be assumed the banks and savings and loans want to earn the additional interest by requiring a new loan.

The normal equations can be solved using LU decomposition and backsubstitution but the solution is susceptible to roundoff error. It is common practice to use singular value decomposition to solve this problem, and this is the route we have taken. The implementation of the generalised linear least squares algorithm can be found in the ppf.math.generalised least squares module and the details of the implementation are shown below.

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