Abstract
Problem statement: In this article we considered pairs bootstrap thro ugh a truncated geometric bootstrap method for stationary time seri es data. Construction of valid inferential procedur es through the estimates of standard error, coefficien t of variation and other measures of statistical precision such as bootstrap confidence interval wer e considered. The method was used to confirm the correlation between Silicon Oxide (S iO2) and Aluminum Oxide (Al 2O3) from a geological data. A typical problem is that can these components exist together or they are mutually exclusive. Approach: We attempt to solve these problems through bootstr ap approach to correlation analysis and show that pair bootstrap method through truncated g eometric bootstrap method for stationary process revealed the correlation coefficient between Silico n Oxide (SiO 2) and Aluminum Oxide (Al 2O3) from the same geological field. Results: The computed measure of statistical precisions suc h as standard error, coefficient of variation and bootstrap-t con fidence interval revealed the correlation analysis of the bivariate stochastic processes of SiO 2 and Al 2O3 components from the same geological field. Conclusion: The correlation analysis of the bivariate stochasti c process of SiO 2 and Al 2O3 components through bootstrap method discussed in this study re vealed that the correlation coefficients are negati ve and bootstrap confidence intervals are negatively s kewed for all bootstrap replicates. This implies th at as one component increases, the other component decreases, which means that the two components are mutually exclusive and the abundance of one mineral prevents the other in the same oil reservoir of th e same geological field.
Highlights
INTRODUCTIONStorch and Zwiers (2001). The stationary bootstrap of Politis and Romano (1994) and the moving block
Since its introduction by Efron (1979), the bootstrap of Kunsch (1989) are obvious methods to bootstrap has become a method of choice for assessing solve these problems
The present study employs the use of pairs often used statistical tools, in geosciences, is bootstrap through truncated geometric bootstrap
Summary
Storch and Zwiers (2001). The stationary bootstrap of Politis and Romano (1994) and the moving block. Pearson’s correlation coefficient rxy, which measures method of Olatayo (2010), with block length the degree of (linear) interrelation between two sample proportional to the estimated time dependent of the (data size n) variables, x and y. The study is based on the use of some chemical are measured over time and a typical aim in correlation components of the soil to determine the presence or the analysis of such bivariate time series is to value the abundance of oil in an area under exploration. Since geological interpretation of a during chemostratigraphy study These components are detected correlation requires knowledge about the statistical precision, a confidence interval for rxy or at least, a test of the hypothesis “population correlation coefficient rxy = 0” is required. We attempt to solve the problem through bootstrap approach to correlation analysis
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