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Thus, the zeroth-order Renyi entropy gives the logarithm of the measure of the support set of the density f , and the Shannon entropy h1 gives the logarithm of the size of the effective support set (Theorem 8.2.2). We now de ne the equivalent of the entropy power for Renyi entropies. De nition The Renyi entropy power Vr (X) of order r is de ned as 2r 0 < r , r = 1 , 1 + r1 = 1 f r (x) dx n r , r 2 Vr (X) = exp [ n h(X)], r=1 2 ({x : f (x) > 0}) n , r = 0 (17.99) Theorem 17.8.3 For two independent random variables X and Y and any 0 r < and any 0 1, we have log Vr (X + Y ) log Vp (X) + (1 ) log Vq (Y ) + H ( ) + 1+r H 1 r

We present an illustration of this type of analysis later in this chapter In a later section ( A Few Loose Ends ), we also discuss brie y the use of the EM covariance matrix for taking a quick and dirty look at one s data, even when hypothesis testing is required Use of the EM matrix is also good for establishing goodness of t in SEM when data are missing (Graham & Hofer, 2000) EM Algorithm Programs There are many programs available for the EM algorithm for covariance matrices Perhaps the best option is Schafer s (1997) NORM program The program is designed to perform multiple imputation, but one of the intermediate steps is to calculate the EM covariance matrix Utility programs (eg.

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, ALPHNORM) are easily written that allow the use of NORM s EM covariance matrix for performing analysis with SAS (and other programs) and for doing coef cient alpha analysis Other programs for performing EM include EMCOV (Graham et al, 1994), SPSS, and SAS EMCOV is a DOSbased program that was developed in the early 1990s Nearly all of its functions are better handled by Schafer s (1997) NORM program, except that, as a stand-alone program, EMCOV is sometimes easier to use with simulation studies The current implementation of the EM algorithm within SPSS (version 10) is disappointing First, the program is painfully slow, and it often crashes with problems of any size Second, the EM routine is not integrated into the other SPSS procedures.

An excellent, but nonexistent option, for example, would be to use the EM covariance matrix (automatically) as input into the factor analysis and reliability procedures In fact, this should be the default for handling.

with fat, the ripening process that produces aroma proceeds with very slow drying. The geometric surface of chopped products decreases during drying because the diameter of the rods decreases. The permeable part of the surface decreases to a higher degree than the geometrical surface because the impermeable fat particles atten out, occupying a greater part of the surface. The water vapor leaving the surface increases the moisture content of the ambient air. The relative vapor content will be higher because the evaporation cools down the surface and the environment. The air surrounding the product has to become drier and warmer to maintain the driving force. The high rate of air circulation decreases the air-side drying resistance too. The product-sided resistance depends on the porosity of the casing, the mold, and the fat lm on the surface. This resistance steadily increases during the process, lowering the drying rate. The salt content of the surface layer grows with the water loss and decreases with the diffusion of salt into the inner layer. The driving force of diffusion is the difference in the salt content compared with the water content. The diffusion of salt seems to be quicker because the salt content along the

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