COMPARISON OF 2 SAMPLING THEORIES TO CALCULATE THE INTEGRATION ERROR FOR ONE-DIMENSIONAL PROCESSES

The integration errors in some simulated and real time series were cal culated as a function of the sampling interval. The were calculated fr om the variogram according to Gy's theory [1] and from the autocorrela tion function according to Kateman and Muskens' theory [2]. The latter theory has been derived for first-order autoregressive processes. The calculated errors were compared with each other and with the true sam pling error when this could be calculated. The true integration error and the errors calculated with the two theories were quite similar in the simulated first-order processes which were only slightly internall y correlated. The processes with strong internal correlation were crea ted with two simulation methods. In these processes Gy's integration e rror was smaller than the error by Kateman et al. and the true error. The normally distributed random noise added to the simulated processes increased Gy's integration error, but did not affect the error by Kat eman et al. The error by Kateman et al. can be compared only with the non-periodic continuous term of Gy's integration error. The errors wer e calculated with two sample sizes in the simulated processes which we re slightly internally correlated. The smaller the sample size, the la rger the integration errors. The characteristics of the methods to cal culate the errors with minimum sampling interval are discussed. Neithe r theories could calculate the error in a periodic process. Gy's integ ration error is much smaller than the error by Kateman et al. in a rea l periodic process.