Abstract
Significant reduction in the value of the number of bootstrap resamples may be achieved when exponential tilting is used to estimate a tail probability or a quantile, associated with a small probability, say p. However, exponential tilting is based on the assumption that the bootstrap distribution is approximately normal. In this paper, chisquare tilting is introduced when the bootstrap distribution of the statistics of interest is assumed to have a skewed distribution. In the case of small value of the new tilting reduces the asymptotic variance by at least a factor of 6, implies a large saving in bootstrap resamples. Calculations show that the variance reduction is very close to the asymptotic one even for sample sizes as small as 15. Some asymptotic results are given. Computer simulation as well as real data is used to illustrate the computation for the lower bootstrap confidence limit of the bootstrap sample variance. Although, bootstrap confidence intervals for a variance are known to perform very badly in practice even when sampling from a normal distribution, see Schenker (1987), our simulation indicates that our method perform reasonably well.
| Original language | English |
|---|---|
| Pages (from-to) | 181-198 |
| Number of pages | 18 |
| Journal | Metron |
| Volume | 60 |
| Issue number | 3-4 |
| Publication status | Published - 2002 |
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Keywords
- Bootstrap
- Chi-square Tilting
- Confidence limits
- Exponential Tilting
- Importance resample
- Sample variance
ASJC Scopus subject areas
- Statistics and Probability
Cite this
Importance resampling using Chi-Square Tilting. / Samawi, Hani M.; Mustafa, Ahmad S Bany; Ahmed, Mohammad S.
In: Metron, Vol. 60, No. 3-4, 2002, p. 181-198.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Importance resampling using Chi-Square Tilting
AU - Samawi, Hani M.
AU - Mustafa, Ahmad S Bany
AU - Ahmed, Mohammad S.
PY - 2002
Y1 - 2002
N2 - Significant reduction in the value of the number of bootstrap resamples may be achieved when exponential tilting is used to estimate a tail probability or a quantile, associated with a small probability, say p. However, exponential tilting is based on the assumption that the bootstrap distribution is approximately normal. In this paper, chisquare tilting is introduced when the bootstrap distribution of the statistics of interest is assumed to have a skewed distribution. In the case of small value of the new tilting reduces the asymptotic variance by at least a factor of 6, implies a large saving in bootstrap resamples. Calculations show that the variance reduction is very close to the asymptotic one even for sample sizes as small as 15. Some asymptotic results are given. Computer simulation as well as real data is used to illustrate the computation for the lower bootstrap confidence limit of the bootstrap sample variance. Although, bootstrap confidence intervals for a variance are known to perform very badly in practice even when sampling from a normal distribution, see Schenker (1987), our simulation indicates that our method perform reasonably well.
AB - Significant reduction in the value of the number of bootstrap resamples may be achieved when exponential tilting is used to estimate a tail probability or a quantile, associated with a small probability, say p. However, exponential tilting is based on the assumption that the bootstrap distribution is approximately normal. In this paper, chisquare tilting is introduced when the bootstrap distribution of the statistics of interest is assumed to have a skewed distribution. In the case of small value of the new tilting reduces the asymptotic variance by at least a factor of 6, implies a large saving in bootstrap resamples. Calculations show that the variance reduction is very close to the asymptotic one even for sample sizes as small as 15. Some asymptotic results are given. Computer simulation as well as real data is used to illustrate the computation for the lower bootstrap confidence limit of the bootstrap sample variance. Although, bootstrap confidence intervals for a variance are known to perform very badly in practice even when sampling from a normal distribution, see Schenker (1987), our simulation indicates that our method perform reasonably well.
KW - Bootstrap
KW - Chi-square Tilting
KW - Confidence limits
KW - Exponential Tilting
KW - Importance resample
KW - Sample variance
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UR - http://www.scopus.com/inward/citedby.url?scp=84891468601&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84891468601
VL - 60
SP - 181
EP - 198
JO - Metron
JF - Metron
SN - 0026-1424
IS - 3-4
ER -