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On the modification of M-out-of-N bootstrap method for heavy-tailed distributions
This paper is on the modification of š-out-of-š bootstrap method for heavy-tailed distributions such as income distribution. The objective of this paper is to present a modified š-out-of-š bootstrap method (šššš) and compare its performance with the existing m-out-of-n bootstrap method (ššoš). The n...
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2015
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/7710 | ||
| 042 | |aĀ dcĀ | ||
| 720 | |aĀ Opayinka, H. F.Ā |eĀ authorĀ | ||
| 720 | |aĀ Adepoju, A.A.Ā |eĀ authorĀ | ||
| 260 | |cĀ 2015Ā | ||
| 520 | |aĀ This paper is on the modification of -out-of- bootstrap method for heavy-tailed distributions such as income distribution. The objective of this paper is to present a modified -out-of- bootstrap method () and compare its performance with the existing m-out-of-n bootstrap method (o). The nature of the upper tail of a distribution is the major reason for the poor performance of classical bootstrap methods even in large samples. The āā bootstrap method was therefore, proposed as an alternative method to āā bootstrap method. The distribution involved has finite variance. The simulated data sets used was drawn from Singh-Maddala distribution. The methodology involved decomposing the empirical distribution and sampling only nā times with replacement from a sample size n, such that nā āā as nāā, and nā/n ā0. The performances are judged using standard error; absolute bias; coefficient of variation and root mean square error. The findings showed that āā performed better than in moderate and larger samples and it converged fasterĀ | ||
| 024 | 8 | |aĀ 2313-4402Ā | |
| 024 | 8 | |aĀ ui_art_opayinka_modification_2015Ā | |
| 024 | 8 | |aĀ American Scientific Research Journal for Engineering, Technology, and Sciences 14(1). Pp. 142 - 155Ā | |
| 024 | 8 | |aĀ http://ir.library.ui.edu.ng/handle/123456789/7710Ā | |
| 653 | |aĀ BootstrapĀ | ||
| 653 | |aĀ DecompositionĀ | ||
| 653 | |aĀ Heavy-tailed distributionsĀ | ||
| 653 | |aĀ Singh-Maddala distributionĀ | ||
| 245 | 0 | 0 | |aĀ On the modification of M-out-of-N bootstrap method for heavy-tailed distributionsĀ |