### Table 4: Experiments for non-Gaussian variations and nonlinear delay.

2007

"... In PAGE 5: ... We compare the solution quality of n2SSTA with the golden Monte Carlo simulation of 100,000 runs. Similar to the experiment setting in [12], Table4 com- pares n2SSTA and Monte Carlo simulation in terms of the ratio between sigma and mean, the 95% yield timing, and runtime in second. In the rst (or second) set of experi- ments, all variation sources follow a uniform (or a tri-angle) distribution.... In PAGE 6: ... This clearly shows that n2SSTA is not only more general, but also more accurate than linSSTA. Note that n2SSTA has a larger error for Gaussian variation sources in Table 5 than for uniform or triangle variation sources in Table4 , and this is because n2SSTA needs bigger bounds (10) for Gaussian variations than for uniform or triangle variations. Interestingly, we nd that both approaches pre- dict the 95% yield point well.... ..."

Cited by 1

### Table 7 The non-linearity

"... In PAGE 15: ...ig. 20. Mean pulse height/energy vs. energy for pions. example, in the case of 8 : 2 con quot;guration of T411 beam test, the average density of the calorimeter module was 6.69 g/cm3 ( Table7 ). SPACAL group obtained lateral size parameter j1 quot;17.... ..."

### Table 4: Average number of Newton iterations for solving the linearized model in non-linear Fair-estimation.

in Solution of Linear Programming and Non-Linear Regression Problems Using Linear M-Estimation Methods

1999

"... In PAGE 109: ...Table4 : Results for the updating routine of the software package when used as a tool for nding L from scratch. Times are given in seconds.... ..."

### Table 1. The Calculated Results for Analyzed Data-Set

2000

"... In PAGE 9: ... In order to have easy interpretable models, we have fixed the maximal number of terms in the equation to be equal to 8 and the maximum degree of polynoms to be equal to 3. The calculations performed using the select params option of the ANALYSIS are summarized in Table1 . The number of stored models was 3.... In PAGE 9: ... It was shown that the use of significant variables, as detected by MUSEUM, = improved PLS results (compare data in column 7 vs. column 6 in Table1 ). The similar tendency was also observed if only variables found to be relevant by the PNN algorithm were used in the cross-validation calculations (compare the last and 7 columns of Table 1).... In PAGE 12: ... b Number of significant PLS components. c The cross-validated q2 calculated using input variables optimized by MUSEUM approach (unless not stated otherwise the PLS results are from Table1 and 15 of (2)). d Number of input variables selected by PNN.... ..."

Cited by 2

### Table 1 : The results of the non-linearity test

1995

"... In PAGE 3: ... 3. Results About 90 % of all the signals were found stationary during both positions ( Table1 ). At least 50 % of all the stationary recordings were found to contain significant non-linearities (p lt;0.... ..."

Cited by 1

### Table 1. Comparison of the amplitude of the non-linear

"... In PAGE 4: ... Also, equa- tion (17) simpli es to, jmw(a; r) = A Njmw(n) a 6 (5+n) r? : (20) So, the task of comparing the amplitude of to the N- body calibrated formula simpli es to a comparison of the normalization constants Nsc(n) and Njmw(n). The results for the cases n = 0; ?1; and ?2 are shown in Table1 . Note that, for the n = ?2 case, the agreement between our prediction and the N-body data is better than indicated in the table because, as noted by JMW, their formula underestimates the non-linear amplitude of for this spectrum.... ..."

### Table 4: Non-linear Test Results

2007

"... In PAGE 12: ... However, for all models, in both time periods, the np test suggests that there is no cointegration, while the kpss test suggests that there is. Table4 presents the results of the various random field based tests for nonlin- earity. For the first time period, 1959-1972, the tests nearly always reject the null hypothesis of linearity.... In PAGE 13: ...odels. The results are interesting and need careful interpretation. The most ob- vious result is that in the second period, it proved impossible to get the numerical optimisation algorithms to converge for Model 1 when no trend was present, and for Model 2 when a trend was present. It is for these two models that the tests for nonlinearity, reported in Table4 , often fail to reject the null hypothesis of linearity. Also, from Table 3, it is the no-trend version of Model 1 that is more likely to be a cointegrating relationship, according to the results of the adf test.... ..."

### Table 5: Non-linearity test results.

in The Commissioning of the Arcetri Near-Infrared Camera ARNICA: I. Characterization of the Detector