### TABLE IV NOTATIONS FOR ADAPTIVE DATA COLLECTION AND MODEL-BASED PREDICTION PARAMETERS

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### Table 4 ARMD trial

2007

"... In PAGE 17: ...ared. For the observed, partially incomplete data, GEE is supplemented with WGEE. Further, a random-intercepts GLMM is considered, based on numerical integration. The GEE analyses are reported in Table4 and the random-effects models in Table 5. For GEE, a working exchangeable correlation matrix is considered.... In PAGE 19: ... The advantage of having separate treatment effects at each time is that particular attention can be given at the treatment effect assessment at the last planned measurement occasion, that is, after one year. From Table4 it is clear that the model-based and empirically corrected standard errors agree extremely well. This is due to the unstructured nature of the full time by treatment mean structure.... In PAGE 20: ... The results for the random-effects models are given in Table 5. We observe the usual relationship between the marginal parameters of Table4 and their random-effects counterparts. Note also that the random-intercepts variance is largest under LOCF, underscoring again that this method artificially increases the association between mea- surements on the same subject.... ..."

### Table 4. Model-based reductions of the complete pathway (model statistics)

"... In PAGE 25: ... Similarly, the removal of the de- complexation of FGFR:FRS2 would only be noticeable over a very small time scale: as the rate of FRS2 and FGFR complexation is extremely fast, following the decomplexation of FRS2 and FGFR one would see the (re)complexation of FGFR and FRS2 almost immediately. Table4 gives the model statistics both for the complete model and the model obtained after applying the reductions (1), (2) and (3) both in isolation and collectively. The results show that reduction (1) - removal of Sos - yields the greatest decrease in state space of the three.... ..."

### Table 3 summarizes the results obtained for several Model-based and Memory-

2001

"... In PAGE 6: ... Except for the twoBayesian methods, all the other methods are memory-based methods. The results in Table3 are for the best variation of each method. Clearly the... In PAGE 7: ...Table3 . Summary of Results on MSWEB data Collaborative Filtering Method R-metric Static Ranked List 49.... ..."

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### Table 8: Classiflcation table for the model-based clustering with variable selection method.

"... In PAGE 14: ...Raftery and Dean (2004) propose a version of model-based clustering with variable selection. This method was applied to the wine data and the results of this analysis are given in Table8 . Interestingly, only four of the variables were selected: Malic Acid, Proline, Flavanoids and Color Intensity.... ..."

### Table 3. Comparative performance with earlier results on a model-based dataset

"... In PAGE 5: ....2.2 Model-based benchmark We also performed a compara- tive study of alignment quality using the average log LGscore based on the model-based benchmark. Our results in Table3 reiterate the closeness in performance of the incremental window-based alignment method to the highly optimized SW-PSSM alignment algorithm for the family-, superfamily- and fold-level subsets. For this dataset too, we performed a thorough parameter study by varying wmer lengths for our alignment schemes.... In PAGE 5: ... (Owing to space constraints we do not show the detailed results on the model-based dataset here.) Table3 also shows results for the optimized local (local sequence alignment using a global scoring matrix), global (global sequence alignment using a global scoring matrix), PSI [3D-PSSM (Kelley et al., 2000) based global sequence alignment against a profile (Gribskov et al.... In PAGE 5: ... Using sequence alignment tech- niques we would like to achieve these high levels of accuracy. The results shown in Table3 for the various previously published schemes, as well as for our methods, are the best achieved after optimization of the various parameters. We further analyze the data by annotating a model as being correct based on the LGscore value.... ..."

### Table 4. Model-based reductions of the complete pathway (model statistics) states transitions construction model checking

"... In PAGE 25: ... Similarly, the removal of the de- complexation of FGFR:FRS2 would only be noticeable over a very small time scale: as the rate of FRS2 and FGFR complexation is extremely fast, following the decomplexation of FRS2 and FGFR one would see the (re)complexation of FGFR and FRS2 almost immediately. Table4 gives the model statistics both for the complete model and the model obtained after applying the reductions (1), (2) and (3), both in isolation and collectively. The results show that reduction (1) - removal of Sos - yields the greatest decrease in state space of the three.... In PAGE 25: ... In general, it is therefore advantageous to look into a number of different reduction approaches, although, as already stated, this does re- quire some understanding of the model under study. Table4 also presents the times required for model construction and model checking of a single property (property H of Section 6) using each of the different combinations of model reductions. It can be seen that the decreases in model size are also reflected in these timings.... ..."

### Table 3: Ranking distibution of the label obtained with the model-based approach on the validation dataset

2004

"... In PAGE 10: ... 4.3 Two-stage classification system As we can see on Table3 , after the first stage of classification the label of the data is not always in the first two classes, which justifies the choice of a dynamic number of classes in conflict. ... ..."

### Table 4: Standard errors (two times) for model-based seasonally adjusted gures

"... In PAGE 18: ... In our model the seasonally adjusted data is the sum of the estimated trend and irregular. The di erences in width of the con dence intervals are indicated bytwice the standard errors and they are reported in Table4 for models (a), (c) and (e). We present the results for the typical years of 1980 (recession) and 1996 (no recession).... ..."

### Table 4: Standard errors (two times) for model-based seasonally adjusted gures

"... In PAGE 18: ... In our model the seasonally adjusted data is the sum of the estimated trend and irregular. The di erences in width of the con dence intervals are indicated by twice the standard errors and they are reported in Table4 for models (a), (c) and (e). We present the results for the typical years of 1980 (recession) and 1996 (no recession).... ..."