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Proof: We will not prove this lemma, but instead, outline the proof in Problem 10.16 at the end of the chapter. In essence, the proof involves nding a lower bound on the size of the conditionally typical set. We will proceed directly to the achievability of the rate distortion function. We will only give an outline to illustrate the main ideas. The construction of the codebook and the encoding and decoding are similar to the proof in Section 10.5. Proof: Fix p(x|x). Calculate p(x) = x p(x)p(x|x). Fix > 0. Later we will choose appropriately to achieve an expected distortion less than D + . Generation of codebook: Generate a rate distortion codebook C consist ing of 2nR sequences X n drawn i.i.d. i p(xi ). Denote the sequences n (1), . . . , X n (2nR ). X Encoding: Given a sequence X n , index it by w if there exists a w such that (X n , X n (w)) A (n) , the strongly jointly typical set. If there is more than one such w, send the rst in lexicographic order. If there is no such w, let w = 1. Decoding: Let the reproduced sequence be X n (w). Calculation of distortion: As in the case of the proof in Section 10.5, we calculate the expected distortion over the random choice of codebook as D = EXn ,C d(X n , X n ) = EC

Next, let s establish a baseline and classify the records using naive Bayes with 10-fold cross validation, where all nine attributes are input to the classi er. 1. Select the Classify Tab. 2. Under Classi er, press the Choose button. 3. Select Classi ers Bayes Naive Bayes from the navigation hierarchy. 4. By default, under Test options, notice that WEKA speci es Cross-validation. We ll use this option for our experiment because we have a single data le. 5. Click Start.

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If you create custom worksheet functions using VBA, you may want to associate a Help file and context ID with each function. Once these items are assigned to a function, the Help topic can be displayed from the Insert Function dialog box by pressing F1. To specify a context ID for a custom worksheet function, follow these steps: 1. Create the function as usual. 2. Make sure that your project has an associated Help file (refer to the preceding section). 3. In the VBE, press F2 to activate the Object Browser. 4. Select your project from the Project/Library drop-down list. 5. In the Classes window, select the module that contains your function. 6. In the Members of window, select the function. 7. Right-click the function, and select Properties from the shortcut menu. This displays the Member Options dialog box, as shown in Figure 24-9.

event is to collect data more frequently when events are most likely to occur Information on the anticipated shape of the hazard function provides helpful information for selecting times for data collection The idea is to collect data more frequently when hazard is high and less frequently when hazard is low This allocation strategy was used effectively, for example, by Hall, Rugg, Tunstall, and Jones (1984), who in their 1-year prospective study of smoking abstinence following behavioral skills training placed their four data-collection periods at 3, 6, 26, and 52 weeks after treatment If they had spaced data-collection episodes equally, waiting until week 13 to collect the rst follow-up data, they would have been unable to determine that the risk of relapse was highest in the few weeks immediately following cessation.

(15.203)

Can You Reconstruct Event Histories From Retrospective Data Collection In 1837 William Farr wrote, Is your study to be retrospective or prospective If the former, the replies will be general, vague, and I fear of little value (cited in Lilienfeld & Lilienfeld, 1980) His words remain true today Whenever possible, researchers should collect data prospectively But in the study of infrequent events depression onset, initiation into opiate drug use, child maltreatment prospective data collection may be infeasible Many researchers therefore opt for a different approach: Interview people and ask, Has the event ever occurred and if so, When did it rst occur Retrospective data collection has been used successfully by researchers studying the age at rst use of many different addictive substances and remains a fruitful strategy for research (see, eg, Adler & Kandel, 1983).

These values can be used to evaluate the backward conditional probabilities: P(X = 0/Y = 0) = P(X = 0/Y = 1) = P(X = 1/Y = 1) = P(X = 1/Y = 0) = P(Y = 0/ X = 0)P(X = 0) (3/4)(4/5) 24 = = P(Y = 0) (25/40) 25 P(Y = 1/ X = 0)P(X = 0) (1/4)(4/5) 8 = = P(Y = 1) (15/40) 15 P(Y = 1/ X = 1)P(X = 1) (7/8)(1/5) 7 = = P(Y = 1) (15/40) 15 P(Y = 0/ X = 1)P(X = 1) (1/8)(1/5) 1 = = P(Y = 0) (25/40) 25

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