### Table 1 Poisson jump-di usion parameter estimates for the DAX across di erent subperiods (Standard errors in parentheses) Panel A: Daily returns

1995

"... In PAGE 10: ... A weekly rate of return is de ned as the di erence between the logarithm of two successive Wednesday prices. Table1 summarizes the Poisson jump-di usion parameter estimates for the DAX stock in- dex returns across di erent subperiods. In addition to the ve parameters to be estimated (instantaneous mean D and variance 2 D of the di usion component, the mean number of abnormal information arrivals (jumps) per unit time , the mean J and variance 2 J of the (logarithmic) jump size) the table reports on the annualized total standard de- viation (volatility) of the jump-di usion process (VOLA)8, the log-likelihood value and the likelihood ratio test statistic ( ).... ..."

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### Table 3: Example Data for Data Schema Read and Path

"... In PAGE 5: ... Figure 2(a) shows the schema of the single event approach. Table3 contains some example entries 1. During data staging no additional processing has to be done.... In PAGE 5: ... However, a major problem remains: Maintaining the path information in every tuple induces an intolerable space consumption. Table3 shows example entries based on this schema. 1This table contains an additional attribute path reader, which is used by the following data schema and... ..."

### Table V: Dynamics of Jump Arrival in Individual Equities and the S amp;P 500 Index y

2007

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### Table 3 Timing of First Jump Bid and Number of Jump Bids for Each Bidder

"... In PAGE 18: ... A bidder is more likely to place a jump bid early rather than late within her individual bidding. Table3 , Panel A, presents data concerning the timing of the first jump bid by all bidders in the sample. Of the 41% of bidders who placed jump bids, 91% placed their first jump bid as their first bid, around 7% as their second bid, and less than 2% any later than that.... In PAGE 19: ... Once again the evidence is largely consistent with the theoretical prediction. ( Table3 about here) Figure 2 present s a scatter plot of all 202 auctions, with the horizontal axis representing the ratio of the number of bidders to the number of units available, and the vertical axis representing the percentage of bids which were jump bids of any size. Note that for all ratios under 1 there is no jump-bidding observed.... ..."

### Table 5: Sample of Generated Data Arrival Time Arrival Way Degree

### Table III. Pedestrian arrivals and waiting times by intersection and decision point

46

### Table 1. Strategies For Active Bus Priority At Traffic Signals Bus Arrival Period Bus Phase Bus Phase

"... In PAGE 19: ... Active Priority at Signals The various strategies to be assessed could include dedicated bus phases, bus phase queue jump, absolute bus priority, selective bus priority. Typical phasing associated with each of these methods of priority are shown in Table1 . The use of active bus priority was modelled using SIDRA to assess the effects of changing the traffic signal phase times.... In PAGE 34: ...- 34 - LIST OF TABLES AND FIGURES Table1 . Strategies For Active Bus Priority At Traffic Signals Table 2.... ..."

### Table 2: Short-run marginal cost payment scheme with all vehicles.

"... In PAGE 6: ... Imagine a cumulative arrival and departure pattern as in Figure 2. This is represented numerically in Table2 , where the numbers 1 - 9 indicate the 1st through 9th vehicle. Each row is a time increment (or turn) for instance a two second headway, reflecting the capacity of the roadway of 1800 vehicles per hour.... ..."

### Table 4: Number of jumps for large t.

"... In PAGE 17: ...absolute di#0Berence relative di#0Berence t#3Ct c gain AU high gain AU t c #3Ct#3Ct #03 gain AU decreasing gain AU decreasing t = t #03 AU and SU equal AU and SU equal t#3Et #03 loss AU increasing loss AU increasing t !1 loss AU to in#0Cnity loss AU to constant Table 6: Gain and loss of AU with respect to SU, as function of time t of interest. Then we can derive for the necessary number of steps in the uniformization schemes the results of Table4 . Table 5 gives the total number of instructions necessary to carry out the uniformization algorithm for the corresponding needed number of epochs #28note the relation between Table 5 and Table 3#29.... In PAGE 17: ... In the #0Crst tworows the results for AU and SU are given, expressed in N a #28t#29 and N s #28t#29. In the third and fourth row the di#0Berences in complexity are given, now with the results of Table4 #0Clled in for N a #28t#29 and N s #28t#29. We see that both MVM #01 #28t#29 ! #15t m #282#11 o , #11 d #29 and GEN #01 #28t#29 converge #28for GEN #01 #28t#29 the limiting value depends on how many di#0Berent matrices have to be computed#29.... ..."

### Table 1: Adversarial arrival sequence.

2003

"... In PAGE 11: ... Now we will present two arrival sequences. The two arrival sequences are described in Table1 . Both sequences have a common prologue till time 9 as described in the rst column of the table.... ..."

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