### Table 2: Free and xed elements in the discrete time parameter matrices of the EDM

"... In PAGE 16: ... Model speci cation While different models were speci ed, all had the same measurement equation part, which will be addressed rst: yti = Ctixti + dti + vti with cov(vti) = Rti : (20) The parameter matrices for successive observation time points t0; t1; t2; t3 are shown in Table 2. By xing the factor loading of the One-Minute-Test Form A at value 1 and its measurement origin at value 0 (in the rst row of Ct0 and dt0 in Table2 ) we equalled the variance and mean of the latent Decoding Skill (DS) variable at the initial time point t0 to the true variance (total variance minus measurement error variance) and mean of the One-Minute-Test Form A at that time point. In the same manner, the true variance and mean of Cito Reading Comprehension Test 2 de ned the variance and mean of the latent Reading Comprehension (RC) variable at t0.... In PAGE 18: ....H.L. Oud The parameter matrices of the discrete time state equation xt = A xt t + + b + wt t with cov(wt t) = Q (21) are also shown in Table2 . As they contain 21 unknown parameters, the total number of pa- rameters to be estimated is 38.... In PAGE 18: ... The stochastic differential equation dx(t) dt = Ax(t) + + b + GdW(t) dt ; (22) describes the development of the latent variables in continuous time, containing in particular con- tinuously contributing traits and constants b. The EDM relates the continuous time parameter matrices in Equation (22) as follows to the discrete time parameter matrices in Table2 (Oud amp; Jansen, 2000): A = eA t ; b = A 1[A I] b ; Q = irow[(A I + I A) 1(A A I I) row(GG0)] ; = A 1[A I] [A0 I]A0 1 ; ;xt0 = A 1[A I] ;xt0 : (23) Here, is the Kronecker product, row is the rowvec operation, putting the elements of a matrix rowwise in a column vector, irow the inverse operation. Because the time intervals between the measurements were approximately half a year, we started by xing t for the intervals t1 t0; t2 t1; t3 t2 at = 0:50.... ..."

### Table 1. Discrete-time simulation results for quadratic friction case.

"... In PAGE 7: ...0. The results in Table1 show that although the pure discrete wheel model simulation executes at a faster rate, its accuracy leaves much to be desired. Figure 7.... ..."

### Table 1. Discrete-time simulation results for quadratic friction case.

"... In PAGE 7: ...0. The results in Table1 show that although the pure discrete wheel model simulation executes at a faster rate, its accuracy leaves much to be desired. Figure 7.... ..."

### Table 9: Estimated Gender Differences in Quits

"... In PAGE 23: ... This applies both to quits to another job and quits out of the workforce. Table9 reports our estimates of gender differences in the hazard of quitting the current job post-promotion, after controlling for a large number of characteristics. The numbers in Table 9 are hazard ratios rather than coefficients.... In PAGE 23: ...en. This applies both to quits to another job and quits out of the workforce. Table 9 reports our estimates of gender differences in the hazard of quitting the current job post-promotion, after controlling for a large number of characteristics. The numbers in Table9 are hazard ratios rather than coefficients. Therefore a value of unity to... In PAGE 24: ...interpreted as a lower female than male quit rate, while a value greater than unity represents a higher female quit rate. As with the raw data, the results in Table9 reveal that there are positive but insignificantly higher quit rates for promoted women (compared to promoted men) to another job or out of the workforce, and higher quits of unpromoted women (compared to unpromoted men) out of the workforce. In contrast to the raw data, women who have never been promoted during the sample period are less likely to quit to another job than men.... ..."

### Table 1: Discrete Time SCPV Peaks Correspondences

in The catchment feature model: A device for multimodal fusion and a bridge between signal and sense

2002

"... In PAGE 24: ... A total of 75 peaks were found in the SCPV. Table1 summarizes the discourse events that... ..."

Cited by 3

### Table 25: De nitions of discrete time operators (a 2 A )

2001

"... In PAGE 38: ...3. The explicit de nitions needed are given in Table25 . Notice that the operators abs, abs and abs of ACPdatp are simply de ned as the operators abs, abs and abs of ACPsatIp restricted in their rst argument to N.... In PAGE 38: ... Notice that the operators abs, abs and abs of ACPdatp are simply de ned as the operators abs, abs and abs of ACPsatIp restricted in their rst argument to N. We will establish the existence of an embedding by proving that for closed terms the axioms of ACPdatp are derivable from the axioms of ACPsatIp and the explicit de nitions given in Table25 . However, we rst take another look at the connection between ACPsatIp and ACPdatp by introducing the notions of a discretized real time process and a discretely initialized real time process.... ..."

Cited by 30

### Table 3 reports results for discrete-time problems with op(A) = AT . The problems were generated using the Matlab statements

"... In PAGE 3: ...73e-14 2.86e-14 Table3 : Comparison between SB03MD and Matlab re- sults (discrete-time case, relative errors in X). Time Relative errors in X n SB03MD Matlab SB03MD Matlab 16 0.... ..."

### Table 3 reports results for discrete-time problems with op(A) = AT . The problems were generated using the Matlab statements

"... In PAGE 3: ...73e-14 2.86e-14 Table3 : Comparison between SB03MD and Matlab re- sults (discrete-time case, relative errors in X). Time Relative errors in X n SB03MD Matlab SB03MD Matlab 16 0.... ..."

### Table 7. Estimates of Unobserved Components Models: Discrete Time and Continuous Time

"... In PAGE 28: ... The results are contained in Tables 7 and 8. Table7 contains the results of estimating the discrete time and continuous time trend- plus-cycle models. The discrete time estimates are taken directly from Harvey (1989, p.... In PAGE 30: ...2843 Figures in parentheses are standard errors. misspeci ed in some way, and Harvey (1989) does indeed nd that the discrete time model in Table7 is inferior to a cyclical trend model in which t also depends on t 1 and yt = t+ t. Further investigations with continuous time cyclical trend models may be fruitful, but are beyond the scope of this simple illustration.... ..."

### Table 2: Discrete Time Hazard Estimation of Age at Marriage Males with Primary

"... In PAGE 18: ...17 5. Empirical Results In this section we discuss separately the effects of each of the covariates on the age at marriage ( Table2 ), age at first birth (Table 3) and the duration of subsequent birth intervals (Tables 4 and 5). Although we have also estimated the intervals from marriage to a first birth, we do not discuss the results due to some problems.... ..."