### Table 4.1: The Notation for Multivariable Time Series B A multivariable time series data sequence, lt; b1;1; b1;2; : : : ; b2;1; b2;2; : : : gt; , each bi;j is a real num- ber at the ith channel and jth time stamp. B[i; :] ith channel of data

in ABSTRACT

### Table 1 in the Appendix lists (skJ} for

1987

### Table 2 Joint Tests Of Time Series Beta Significance For 15 Portfolios In Each Asset Set

"... In PAGE 15: ...4.1 Multivariate Time-Series Betas In Table2 , F-probabilites are listed. These probabilities are associated with maintaining the null that the coefficients for the listed right-hand-side variables are jointly zero across the 15 portfolios in each group.... ..."

### Table 2, and are described in detail in the forthcoming sections.

"... In PAGE 6: ... Redundancy type Redundancy meaning Appropriate models Temporal relates values of one variable for different time instants time series or lagged-variable models like AR, ARIMA Spatial relates values of several variables for one time instant multivariate static models like linear regression, PCA, nonlinear models like NNPCA, hypersurface Spatial and temporal relates values of several variables for different time instants multivariate time series models like VAR, VARMA, transfer function models like ARX, ARMAX, BJ Table2 : Redundancy types and the corresponding models 2.6.... ..."

### Table 6 Multivariate model-fitting results for anxiety, neuroticism, somatic anxiety and depression (two series of analyses for 1997 data: with BDI and with YASR depression)

"... In PAGE 7: ...different genes influenced anxiety and depression in males and females. These results for the genetic architecture of anxiety, depression and neuroticism are very similar to results obtained in Australian3 and American twin studies4,53 Goodness-of-fit chi-squared statistics for the multi- variate genetic analyses of the twin data on anxiety, neuroticism, somatic-anxiety and depression col- lected in 1991, 1993 and 1997 are summarised in Table6 (for the 1997 data two series of analyses were carried out: once with YASR-depression and once with BDI-depression scales). These analyses showed very stable results across time: significant sex differ- ences in parameter estimates and no contribution of common family environment to resemblance of family members.... In PAGE 10: ... The genetic covariance between measures was due to one com- mon genetic factor. The largest part of the heritability in all anxious depression indices could be attributed to this common genetic factor ( Table6 ). These multivariate analyses confirmed the large overlap in the genes conveying susceptibility to anxiety, neu- roticism, somatic anxiety and depression, as pre- viously reported by others.... ..."

### Table 5. Multivariate contrasts

2007

"... In PAGE 19: ...Table5 shows the obtained results for the main and interaction effects of each component on understandability time and efficiency when applying multivariate contrast indicators (using a cutting edge of 0.05).... In PAGE 19: ...As we can see in Table5 , with respect to the time, all the components and their combinations (except the SSF component) were significant. As an example, the relationship between the components SSF and AWS is described in Figure 3.... ..."

### Table 2 Multivariate analysis

1791

"... In PAGE 3: ...01) or dichotomous variables (Table 2). Patients with a low CRS or a high FGA also had a longer disease-free survival, although the association of CRS and FGA with disease-free survival did not reach statistical significance in the multivariate analysis ( Table2 ). The median survival time in patients with a low CRS and a high FGA was 38 months (mean, 47) compared with 18 months (mean, 20) for low CRS and a low FGA (P = 0.... ..."

### Table I, and with the factors and the residuals sampled from a multivariate normal distribution. For the GMM estimations, we present three specification tests. The first two tests are from the second and third stage GMM when the identity matrix is used as the initial weighting matrix. The third test is from the second stage GMM when the sample estimate of the optimal weighting matrix is used as the initial weighting matrix. Results are presented for different lengths of time series observations (T), and they are based on 10,000 simulations.

2001

Cited by 1

### TABLE 8 Real-Time PCR Confirmation of Selected Genes, Including Cell-Cycle Related Genes, following 6.0 mg TCDD/kg

2005

### Table 2. Multivariate survival analyses

"... In PAGE 5: ... Thus, the observed difference in log rank was found to occur by chance 179 times out of 1000. Multivariate results Results from fitting Cox proportional hazards models to pairs of factors univariately significant are summar- ized in Table2 . The table shows prognostic factors grouped by univariately significant factors.... ..."