### Table 3: Average powers for detecting differences in trends in vegetation from data collected annually, estimated using the auto-regressive model. The differences in trends (Change over interval) are assumed to be between two equally sized groups formed from the stated total number of sites.

"... In PAGE 75: ... For the birds, high (86%) mean power is calculated for a 4% difference in annual trends after 12 years with 100 sites, but the corresponding mean power with 30 sites is only 52%. For vegetation ( Table3 ), the power calculations decrease with length of monitoring because they are based on hypothesised differences in change in indices over the time interval, hence the differences in rates of change decrease as the monitoring interval increases. The mean power associated with a difference of index change of 0.... ..."

### Table 1. Classification of algorithms solving the serial spatial auto-regression model Exact Approximate Applying Direct Sparse Matrix Algorithms [25] ML based Matrix Exponential Specification [26] Eigenvalue based 1-D Surface Partitioning [16] Graph Theory Approach [32] Taylor Series Approximation [23] Chebyshev Polynomial Approximation Method [30]

in Comparing exact and approximate spatial auto-regression model solutions for spatial data analysis

2004

"... In PAGE 2: ...A number of researchers who have been attracted to SAR because of its high com- putational complexities have proposed efficient methods of solving the model. These solutions, summarized in Table1 , can be classified into exact and approximate solu-... ..."

Cited by 3

### Table 1: The response times (in seconds) for the serial spatial auto-regression model solution to show hot- spots (i.e. bottlenecks)

"... In PAGE 13: ... The total response time also includes fitting to spatial auto-regression model parameters. The exact serial response time numbers are shown in Table1 at the end of section 4. The update_lowtri_matrix loop has great potential for parallelization because there is no output dependency between the variables and arrays used in that loop.... ..."

### Table 2: RMSE for market prediction, linear and nonlinear models, as well as estimated pro t threshold for indirect forecast

1996

"... In PAGE 7: ... In the case of forward rate agreements, the implied forward rates at present can be used as market expectations for the future rate. The resulting series of di erences between market expectations and actual future rates was again modeled using linear and nonlinear models (see Table2 ). Two- layer feedforward networks in a nonlinear auto-regressive setting were used.... ..."

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### Table 5: Average powers for detecting trends in birds at a particular site from data collected annually, every three years or every six years estimated using the auto-regressive model.

### TABLE 1.5: RESULTS OF AUTO-REGRESSION TEST (DAILY RETURN SERIES): -

### Table 6: Auto-regressive distributed lags model with log(K) as endogenous variable. Sensitivity to risk Sensitivity to Trade Credit Sensitivity to Equipement Goods

### Table 1 Ordinary Least Squares Consumption Models Based Upon Country Data: Annual Observations 1975-1999 and State Data: Quarterly Observations 1982-1999 Country/State Fixed Effects All variables are real (deflated by GDP deflator) and measured per capita in logarithms (t ratios in parentheses) Dependent variable: Consumption per capita

"... In PAGE 19: ... In each of the four tables, the first three columns present regression results for the panel of countries (228 observations on 14 countries), while the next three columns report the results for the panel of states (3498 observations on 50 states and the District of Columbia).9 Table1 presents basic ordinary least squares relationships between per capita consumption, income, and the two measures of wealth. As the table indicates, in the simplest formulation, the estimated effect of housing market wealth on consumption is significant and large.... In PAGE 21: ... Consumption changes are highly dependent on changes in income and housing wealth, but not stock market wealth. Appendix Table1 presents tests for the presence of unit roots in the individual time series data we analyze. For most, but not all, of the state series we can reject the hypothesis of unit roots in the data.... In PAGE 21: ... Note that the lagged ratio of consumption to income has a coefficient that is negative and significant in all 10 These models rely on sequential estimation using the Prais-Winsten estimator. 11 The specific test we report in Appendix Table1 uses a model with no intercept and no trend in conducting the augmented Dickey-Fuller (ADF) tests. The table also relies upon a four-quarter lag for the state panel, and a one-year lag for the international panel.... In PAGE 31: ...Table1 (cont apos;d) B. Individual Countries Variable Country Consumption Income Stock Wealth Housing Wealth Belgium 0.... ..."