### Table 1: Frequency error in percentage of halftones. Results for methods 1 to 6 are extracted from [2].

### Table 1: Parameter Estimates for the Coordinating and Non-Coordinating Models

"... In PAGE 28: ... For 1994 and 1998 there is a signi#0Ccant tendency for electors who have higher values of #12 i to be more likely to vote than electors who havelower values of #12 i : conservative electors were especially mobilized in those two elections. *** Table1 about here *** In every year, the coordinating model passes the parameter-based tests of the conditions neces- sary for it to describe coordinating behavior. Table 2 reports the LR test statistics for the constraint #0B = 1, imposed separately for eachyear.... In PAGE 29: ... The House position was expected to be closer to the Democratic position in 1978, 1982, 1986 and 1990, closer to the Republican position in 1994 and 1998. The MLEs for #0B in the coordinating model are less than :5inevery year except one #28see Table1 #29, suggesting that electors expected the Presidenttobeweaker than the House in determining post-midterm policy. *** Table 4 about here *** The distribution of the ordering of electors apos; ideal points with respect to the post-election policies electors expect according to the coordinating model shows that the moderating mechanism of the coordinating model is capable of generating a midterm cycle of the kind emphasized by Alesina and Rosenthal #281989; 1995#29, though it need not do so.... In PAGE 37: ... NES survey respondents mayoverreport the frequency with which they vote. Among the 9,639 cases from years 1978#7B98 that we use to compute the parameter estimates reported in Table1 , the ! i -weighted percentage reporting having voted is, by year: 47.... In PAGE 38: ... 19. Table1 shows #0B 90 , #0B 94 , #1A 78 , #1A 86 , #1A 90 and #1A 98 to have MLEs equal to either 0:0or1:0, on the conceptual boundary of the parameter space. Consequently, the asymptotic distributions of the MLEs and the LR test statistics are complicated #28Moran 1971; Self and Liang 1987#29.... In PAGE 39: ...Table1 to tabulate that mixture distribution and estimate the con#0Cdence intervals of Table 3. 20.... In PAGE 48: ...524 .455 Note: Computed using the parameter MLEs in Table1 and 1978#7B98 ANES data. Table 5: Orderings of Ideal Points and Expected PartyPolicy Positions, byYear Ordering year #12 i #3C ~ #12 Mi ; ~ #12 i ~ #12 Mi #3C#12 i #3C ~ #12 i ~ #12 i #3C#12 i #3C ~ #12 Mi ~ #12 Mi ; ~ #12 i #3C#12 i #12 Di = #12 Ri amp; i =0 1978 19.... In PAGE 48: ... Entries show the percentage of electors in eachyear who have #12 Di #3C#12 Ri and the indicated ordering of ideal point and expected policy positions, or who have #12 Di = #12 Ri , or who lack policy position values #28 amp; i = 0#29. Computed using the parameter MLEs in Table1 and 1978#7B98 ANES data. Percentages for those with #12 Di #3E#12 Ri are, byyear: #12 i #3C ~ #12 i #285.... ..."

### Table I shows the estimators of the fundamental frequency and the residual errors for the matrix-based algorithm [1] and for the proposed resonator-based method. It is clearly visible that the two algorithms gave very similar results. The difference is due to the fact that the resonator-based method uses the sample mean values as amplitude estimators, instead of the true LS solution. Example 2: The convergence rates of the two algorithms are compared in Fig. 4, where the frequency error is shown as a function of the iteration steps. The test signal was similar to that of Example 1, but now it contained 10 000 samples. The initial frequency estimator was 105.1 Hz for the matrix-based method

2002

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### Table 2. Frequency of errors

1987

"... In PAGE 9: .... involving more than one of the above) 8 I I 1 Table2 lists the frequencies of dserent types of errors. Notice that about 17% of errors are classified as multiple errors (MULT).... ..."

### Table 2: Bayesian estimation of a long-memory stochastic volatility model where d = 0:1, QMLE is the approximate frequency domain, quasi-maximum likelihood estimator, and GPH is the log-periodogram regression using [T1=2] frequencies. errors associated with regression theory (the regression theory standard errors are found in the parenthesis of Tables 2 - 4). The ine ciency measures (Ine .) of Tables 2 - 4 indicate that our MCMC algorithm mixes well and produces near independent draws from the posterior distribution. All of the ine ciency measures are below ve with the largest (4:4087) being found in Table 3 for 2 when d = 0:25, T = 4096, and 2 = 0:5. The long-memory parameters ine ciency mea- sures are all less than four. Furthermore, the Metropolis-Hasting probability of acceptance, ( ; 0), are nearly all greater than 80%, with the lone aberration (54%) found in Table 2 for d = 0:1, T = 4096, and 2 = 0:5. These results along with the unreported rapid decay 14

"... In PAGE 18: ...early all the other cases. The same can not be said for the GPH estimator of d. In every case the GPH estimate of the simulated LMSV data apos;s d is substantially smaller than our Bayesian long-memory parameter estimator and the true long-memory parameter. We also nd in Table2 the GPH estimator infering the opposite of long-memory, in other words, anti-persistent behavior (negative value of d), in the volatility of those simulated LMSV data sets with T = 4096 and d = 0:1. The numerical standard errors found in Tables 2 - 4 are similar to the theoretical and regression theory standard errors of the GPH based estimator and to the approximate-MLE simulated standard errors reported by Breidt et al.... ..."

### Table 4. Manufacturer Discretion: Maximum Likelihood Estimates

"... In PAGE 19: ... On their own, the average price sold, the number of dealers in the network and the age of the network explain around 70% of the variation in the allocation of completion rights by these contracts. [ Note: Table4 here, with allocation of rights by MLE] The regressors are also economically significant. An increase in price of automobiles of one standard deviation (Pta- 1.... In PAGE 20: ...88) increases manufacturer discretion by 1 clause. The main observation that can be derived from using MLE methods to estimate the relation between decision rights and network characteristics as showed in Table4 is that the results of such a procedure are entirely consistent with those in Table 3. None of the 28 signs of the dependent variables is altered by the change in methods.... In PAGE 20: ... Our confidence in these results is increased by the analysis of the individual clause variation presented in Table 5. 13 The signs of the individual effects are overwhelmingly the ones that Table4 has led us to expect: of 60 possible signs (15 regressions times 4 independent variables), only 4 are different than in Tables 3 and 4 and all of those 4 are insignificantly different than 0. As in Tables 3 and 4, particularly robust appear the results on Car Price, Number of Dealers in the Network, and the Asia dummy.... ..."

### Table 1.|SNP Estimation { Maximum Likelihood Surface.

"... In PAGE 10: ... Thus, for a given SNP speci cation, the smaller the R-squared of the regressions, the better the SNP model approximates the true density. In Table1 we present the maximum likelihood surface for three key models: (1) the basic ARCH(1) model, which is SNP(11100); (2) the ARCH model with many lags in the variance (given by SNP(1h100), which has 17 lags in the variance) to approx- imate the GARCH(1,1) speci cation; and (3) the preferred model from our selection procedure, which is SNP(1c121). The preferred model is a general nonlinear process with heterogeneous innovations.... In PAGE 10: ...ith 816 observations for each of three series implying a saturation ratio of 26.3.9 Table 1 indicates that the preferred model performs substantially better than the other two models according to all three model selection criteria. (Insert Table1 here.) The superior performance of the preferred model in matching the data is also re-... ..."

### Table 2: Total Variance Explained Component Extraction Sums of Squared Loadings

"... In PAGE 5: ... 3.3 Factor Analysis The percentage of variance explained was calculated by factor analysis, applying the maximum likelihood method and Varimax rotation ( Table2 ). The flrst two factors ex- plain 28.... ..."

### Table 1. The precision and the speed of the proposed algorithm. K: The order of expansions in FMM; N: the cut-o frequency; error: relative error of the results in the max norm; Tf:timefor our algorithm; Td: time for lazy direct computation; T0:estimated time for full direct computation; Td=Tf: the speed-up rate against the lazy direct computation; T0=Tf: the speed-up rate against the full direct computation. The times are measured in seconds.

in Article electronically published on November 28, 2001 A FAST SPHERICAL HARMONICS TRANSFORM ALGORITHM

"... In PAGE 11: ... The implementations, especially that of the FMM, are rather rough, and they can be re ned and tuned for higher performance. Table1 reports the precision and the performance of our algorithm. K and N are the order of the expansions in the FMM and the cut-o frequency, respectively.... ..."

### Table 1. Evaluation of three front-ends. Listed are percent seg- mentation errors (del + ins) and frequency errors ( F). Total error rates are given with their 95% confidence intervals.

2004

"... In PAGE 4: ...We tested all the acoustic front-ends on the three test sets. The measured errors (see Table1 ) are deleted and inserted notes (= seg- mentation errors), and notes whose MIDI-code differs more than 1 from that of the corresponding correct note (=frequency errors). The MAMI front-end outperforms the other front-ends on all three... ..."

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