### Table 6. Methods for Shallow Trench Isolation (STI) Fill with Min-Fill objective. Notation: orig H: the original height di erence of the layout; nal H: the post- ll height di erence of the layout; GreedyI: the Greedy method, which terminates after satisfying the given height di erence bound; MCI: the Monte-Carlo method, which terminates after satisfying the given height di erence bound; GreedyII: the Greedy method with a ll removal phase; MCII: the Monte- Carlo method with a ll removal phase; L/W/r: layout / window size / r-dissection; Area: the amount of inserted dummy features; CPU: the run time. The data in bold denote the best results. test case GreedyI MCI GreedyII MCII

in Monte-Carlo Methods For Chemical-Mechanical Planarization on Multiple-Layer and Dual-Material Models

"... In PAGE 11: ...6 9.3 The performances of the simple Greedy Min-Fill method, Monte-Carlo Min-Fill method, Greedy Min-Fill method with a ll removal phase, and Monte-Carlo Min-Fill method with a ll removal phase are compared in Table6 . Our results indicate that the simple Greedy Min-Fill method does not yield near-optimal results.... ..."

### Table 4. Monte Carlo method

"... In PAGE 5: ...4615 0.4342 7175 430 500 Table4 . contains results obtained by standard Monte Carlo method with 10 million points, presented in [2].... ..."

### Table 1: Prices computed by alternative methods under the 2-factor GBM model

2000

"... In PAGE 13: ... 4.2 Computational Results Table1 documents the spread option prices across a range of strikes under the two factor Geo- metric Brownian motion model [22], computed by three di erent techniques: one-dimensional integration (analytic), the fast Fourier Transform and the Monte Carlo method. The values for the FFT methods shown are the \lower quot; prices, computed over , regions that approach the the true exercise region from below and are therefore all less than the analytic price in the rst column.... ..."

Cited by 5

### Table 5. Methods for Shallow Trench Isolation (STI) Fill under the Min-Var objective. Notation: orig H: the original height di erence of the layout; H: the post- ll height di erence of the layout; L/W/r: layout / window size / r-dissection; CPU: the run time. The data in bold denote the best results. test case Greedy MC IGreedy IMC

in Monte-Carlo Methods For Chemical-Mechanical Planarization on Multiple-Layer and Dual-Material Models

"... In PAGE 11: ... We compare the performances of di erent Monte-Carlo and Greedy methods for both the Min-Var and Min-Fill objectives. Table5 shows the post- ll layout height di erence from the Greedy, Monte-Carlo, Iterated Greedy, and Iterated Monte-Carlo methods. The data indicates that for the Min-Var objective, the performance of the Monte-Carlo (MC), Iterated Greedy (IGreedy) and Iterated Monte-Carlo (IMC) methods are all better than the simple Greedy approach.... ..."

### Table 1: Monte Carlo Results

in Bo Honor'e

1998

"... In PAGE 17: ...he Buckley-James estimator is inconsistent when the errors are t(1)(i.e., Cauchy) distributed or heteroskedastic. The results in Table1 indicate that the estimation methods proposed here perform almost as well as the Buckley-James estimator under normality, and that the superiority of the latter disappears when the errors are nonnormal. As might be expected, the procedures proposed here, which do not impose homoskedasticity of the error terms, are superior to Buckley-James when the errors are heteroskedastic.... In PAGE 40: ...Table1 : Monte Carlo Results (continued) Standard Normal Buckley-James CLAD ( = 0:50) STLS True Values -1.000 1.... ..."

Cited by 5

### Table 1: Monte Carlo Results

1998

"... In PAGE 17: ...he Buckley-James estimator is inconsistent when the errors are t(1)(i.e., Cauchy) distributed or heteroskedastic. The results in Table1 indicate that the estimation methods proposed here perform almost as well as the Buckley-James estimator under normality, and that the superiority of the latter disappears when the errors are nonnormal. As might be expected, the procedures proposed here, which do not impose homoskedasticity of the error terms, are superior to Buckley-James when the errors are heteroskedastic.... In PAGE 39: ...Table1 : Monte Carlo Results (continued) Standard Normal Buckley-James CLAD ( = 0:50) STLS True Values -1.000 1.... ..."

Cited by 5

### Table 8: Estimation of the variation of the Monte Carlo method.

2004

"... In PAGE 8: ... ^ (t) is used at the end for estimating P(Rn lt; t) and V ar(^ (t)) can be es- timated by ^ V ar(^ (t)) = ^ 2(t) B . Table8 gives the ^ (t) and a2 ^ V ar(^ (t)) based on n1 = 2000 and B = 200 samples. 8.... ..."

Cited by 4

### Table 4: Prices computed by alternative methods under the 3-factor SV model

2000

"... In PAGE 15: ... For both methods however, increasing the number of strikes does not result in dramatic increases in the com- putational times. Table4 shows the spread option prices for di erent strikes under the three factor SV model. The Monte Carlo prices with a discretisation of 2000 time steps oscillate around those computed by the FFT method.... ..."

Cited by 5

### Table 1: Comparison of Monte Carlo and quasi-Monte Carlo methods used to value a coupon bond

1998

"... In PAGE 21: ... For random Monte Carlo, the constant c is the standard deviation, and = :5. Table1 summarizes the results. For each method, the estimated size of the error at N = 10000 (based on the linear t), the convergence rate , and the approximate computation time for one run with this N are given.... In PAGE 25: ... Figure 2 displays these results in terms of the estimated computation time. In Table1 it can be seen that there is in fact a computational advantage to using quasi-random sequences over random for this problem. This is due to the time required for sequence generation.... ..."

Cited by 15