### Table 5.6 lists the functions related to one-sided communication that have been implemented.

2004

### Table XXIX. Distribution of collaboration patterns across the PDA and PC communicator platform pairs. Description One-Sided Takeover

### Table 3. Accuracies achieved with C4.5 and 1-NN after one-sided se- lection

1998

"... In PAGE 15: ... One-sided se- lection removes from the training set redundant and noisy negative examples. The results achieved using one-sided selection are summarized in Table3 . These results were obtained using 5 random runs of 10-fold cross-validation starting with all the examples; in each run, the training set was reduced using the one-sided selection before the C4.... ..."

Cited by 59

### Table 3. Accuracies achieved with C4.5 and 1-NN after one-sided sampling

1998

"... In PAGE 14: ...4 the training set preferentially those negative examples that are redundant or noisy. The results achieved using this technique are summarized in Table3 . These results were obtained using 5 random runs of 10-fold crossvalidation using all examples; in each run, the training set was reduced using the one-sided selection before the C4.... ..."

Cited by 59

### Table 2: Comparing the three transmission channels bank lending channel {5.3

1996

"... In PAGE 29: ...5. Results for the tests of H1, H2,andH3are shown in Table2 . Since the null hypotheses of no e ect have one-sided alternatives, critical values for a one-tailed test will be used to evaluate the three hypotheses.... In PAGE 29: ... Again the test of the bank lending channel has a non-standard distribution because the parameter u is unidenti ed under H1. According to the evidence in Table2 , the hypothesis of no bank lending channel, H1,canbe rejected. The hypothesis of no broad credit channel, H2, and the hypothesis of no asymmetric channels, H3, cannot be rejected.... ..."

Cited by 2

### Table 4. The Power of the One-Sided Range Test, k = 5: 4 6 9 14

1999

"... In PAGE 9: ...Tables 2 ? 9 for k = 3(1)6(2)10; 15; 20; = 4; 6; 9; 14; 19; 29; 59; 1; and = :10; :05 and :01; where the values of c are the critical values c ;k; for various combinations of k; and : An example of how to use these Tables is illustrated as follows: If one has k = 5 treatments in the experiment, and the initial sample available is n0 = 15 observations (df = 14); at the price of = 10% risk; he/she would like to detect a di erence of at least = 3:0 with a required power of :950: From Table4 , the ratio =pz = 5:6 can be found corresponding to the required power :950: Then, the design constant is found to be z = ( =5:6)2 or z = :287 which will be employed in (2) to determine the total sample size Ni in the experiment. Simulation study shows that linear interpolation in =pz would give satisfactory results for values of power being not tabulated.... In PAGE 15: ...Table4 . The Power of the One-Sided Range Test, k = 5: 19 29 59 1 10% 5% 1% 10% 5% 1% 10% 5% 1% 10% 5% 1% c 3.... ..."

Cited by 1

### Table 4. The Power of the One-Sided Range Test, k = 5: 19 29 59 1

1999

"... In PAGE 9: ...Tables 2 ? 9 for k = 3(1)6(2)10; 15; 20; = 4; 6; 9; 14; 19; 29; 59; 1; and = :10; :05 and :01; where the values of c are the critical values c ;k; for various combinations of k; and : An example of how to use these Tables is illustrated as follows: If one has k = 5 treatments in the experiment, and the initial sample available is n0 = 15 observations (df = 14); at the price of = 10% risk; he/she would like to detect a di erence of at least = 3:0 with a required power of :950: From Table4 , the ratio =pz = 5:6 can be found corresponding to the required power :950: Then, the design constant is found to be z = ( =5:6)2 or z = :287 which will be employed in (2) to determine the total sample size Ni in the experiment. Simulation study shows that linear interpolation in =pz would give satisfactory results for values of power being not tabulated.... In PAGE 14: ...Table4 . The Power of the One-Sided Range Test, k = 5: 4 6 9 14 10% 5% 1% 10% 5% 1% 10% 5% 1% 10% 5% 1% c 4.... ..."

Cited by 1

### Table 3: Computation of one-sided bounds. Measure Expectation Expectation Lower bound

"... In PAGE 9: ... 4, and the second with 2. This leads to table 3 (note that the precision is not the same for the different sets of the second column): Table3 provides a lower bound, whereas table 2 provides a precision. It is based upon two different bounds and therefore is more tedious, but it allows better precision, especially by the use of optimized bounds such as eq.... ..."

### Table 1: Average input powers (one-sided, in dBm) for CSA loop 6 using optimal transmit spectra.

"... In PAGE 5: ... The input power was determined for a xed uncoded performance margin of 1 dB. Input Powers: Table1 lists the average input powers required to achieve the uncoded performance margin of 1 dB. The required input powers are signi cantly lower than the upper limit of 16:78 dBm suggested by OPTIS [4].... ..."

### Table 1: Average input powers #28one-sided, in dBm#29 for CSA loop 6 using optimal transmit spectra.

"... In PAGE 5: ... The input power was determined for a #0Cxed uncoded performance margin of 1 dB. Input Powers: Table1 lists the average input powers required to achieve the uncoded performance margin of 1 dB. The required input powers are signi#0Ccantly lower than the upper limit of 16:78 dBm suggested by OPTIS #5B4#5D.... ..."