### 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 2: Returns to Education, Spline Specification

"... In PAGE 15: ...reported in the upper panel of the table, while results without these dummies are reported in the lower panel. A number of things are worth noting about the results in Table2 . First, a joint F- test shows that the more flexible spline function is a significant improvement in fit over the prototypical Mincerian equation in all of the specifications.... In PAGE 15: ...ear of primary school (.062) and a year of university (.171), and greatly reduces the sheepskin effects at the primary and secondary levels (but not university). The results based on the semi-parametric regressions in equation (2), presented in Table 3 and Figure 4, are quite consistent with those reported in Table2... In PAGE 32: ...57*** 11.35*** Number of observations 2,355 2,355 2,851 2,851 Note: See note at foot of Table2 for explanation of coefficients, standard errors, test statistics, and levels of significance. Specifications 1 and 2 are limited to men whose relationship to the household head is son in households in which both the household head and his spouse are members and report their education levels.... ..."

### Table 2 Correction accuracies of the rule in (2.13) and the LD method

1983

### Table 3: Returns to Education, Semi-Parametric Specification

"... In PAGE 15: ...ear of primary school (.062) and a year of university (.171), and greatly reduces the sheepskin effects at the primary and secondary levels (but not university). The results based on the semi-parametric regressions in equation (2), presented in Table3... In PAGE 33: ...266 .269 Number of observations 2,355 2,355 2,851 2,851 Note: See note at foot of Table3 for explanation of coefficients, standard errors, test statistics, and levels of significance. Specifications 1 and 2 are limited to men whose relationship to the household head is son in households in which both the household head and his spouse are members and report their education levels.... ..."

### Table 1. Translation Error

"... In PAGE 6: ... For each iteration, the translation needed to achieve the correct pre-grasp pose and the error in the place- ment of the robot arm was measured. Example values are shown in Table1 . The components of the trans- lation vector with respect to the robot apos;s coordinate system are given by x, y, and z.... In PAGE 6: ... The absolute error is the translation distance required to put the robot hand into a pre-grasp position after the poses were computed and the robot arm moved. Table1 shows that as the size of the absolute trans- lation required to achieve the pre-grasp position gets smaller, the absolute error also gets smaller. Table 1.... ..."

### Table 2. Preventable Problem Types Reported by Participants

"... In PAGE 3: ... The participants were equally divided between African American and white. Types of Preventable Problems Experienced The 24 participants described experiencing 63 different preventable problems and errors, ranging from misdi- agnosis to the inability to get a timely appointment ( Table2 ). Offi ce administration and communication problems were most frequently described.... ..."

### Table 2 Estimation Results

1998

"... In PAGE 8: ... In Appendix 2 we show how this model can be written in state space form and estimated using the Kalman filter and maximum likelihood methods. Table2 reports the 9 See Smets (1998). This estimation methodology extends the work by Kuttner (1994) and Gerlach... In PAGE 17: ... First, following the literature discussed above we analyse the optimal Taylor rule if we take into account the estimated variance-covariance matrix of the parameter estimates as a measure of model uncertainty. The lower panel of Table2 presents the results.23 We basically confirm the results of Estrella and Mishkin (1998) and Rudebusch (1998) who show that parameter uncertainty only marginally reduces the efficient feedback parameters in the instrument rules.... ..."

### Table 2. NLSY Earnings Regressions (dependent variable: log weekly wage)

1998

"... In PAGE 22: ... They found that AFQT scores are a significant determinant of wages and explain 63% of the black-white wage differential after controlling for education.19 Table2 presents analogous results for our sample. In column 1 of table 2 we report the overall black-white wage differential to be explained using log wages.... ..."

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### Table 1: Simulated ISAR(1) model: ordinary least squares estimates Constant Exponential Reciprocal

1995

"... In PAGE 14: ... 4i apos;s are sampled from a Gamma distribution with parameter = 2 and = 0:5 (giving the mean ticking frequency of 1) and i apos;s are sampled from a Normal distribution with mean 0 and variance 1. The ordinary least squares estimates together with the AIC values from three di erent functions are shown in Table1 . Our approach is successful in two aspects: First, the OLS estimates are all very close to the values we sampled from ( = 0:3): (i) constant function (^ = 0:28), (ii) exponential function ( ^ a = 0:31; ^ b = 0:23), (iii) reciprocal function ( ^ a = 0:29; ^ b = 0:30), Second, we are able to select the correct model based on AIC.... In PAGE 14: ... Our approach is successful in two aspects: First, the OLS estimates are all very close to the values we sampled from ( = 0:3): (i) constant function (^ = 0:28), (ii) exponential function ( ^ a = 0:31; ^ b = 0:23), (iii) reciprocal function ( ^ a = 0:29; ^ b = 0:30), Second, we are able to select the correct model based on AIC. More interesting results can be seen from Table1 . The estimated parameter (^ = 0:41) modeled by the constant function with data sampled from the exponential function is very closed to 0:42 which is value in (5) with = 2 and = 0:5.... ..."

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