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Univariate sampling bootstrap proced...

Beasley, William Howard, IV.

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Univariate sampling bootstrap procedures using prior information.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Univariate sampling bootstrap procedures using prior information./
作者:	Beasley, William Howard, IV.
面頁冊數:	71 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3978.
Contained By:	Dissertation Abstracts International71-06B.
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3404972
ISBN:	9781124029443

Univariate sampling bootstrap procedures using prior information.
Beasley, William Howard, IV.

Univariate sampling bootstrap procedures using prior information. - 71 p.

Source: Dissertation Abstracts International, Volume: 71-06, Section: B, page: 3978.

Thesis (Ph.D.)--The University of Oklahoma, 2010.

Analyses that test nonzero correlations and incorporate prior information can help accumulate knowledge and advance research at a faster pace than typical analyses that disregard previous studies and continue to test unreasonable nil hypotheses. The performance of several bootstrap and parametric procedures are evaluated using populations that had varying degrees of correlation and nonnormality. With correlated heteroscedastic variables, the parametric procedures produced robust point estimates, but showed liberal error rates that worsened as sample sizes grew to NObs = 1,000. This paper proposes two Bayesian univariate sampling bootstrap procedures (the SlotHI and SlotOI) that exhibit much better error rates across all evaluated populations and prior distributions. Based on this simulation, we suggest that the univariate sampling bootstraps are preferred when testing nonzero correlations in nonnormal populations, regardless if prior information is considered.

ISBN: 9781124029443Subjects--Topical Terms:

517247
Statistics.

Univariate sampling bootstrap procedures using prior information.
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502 $a Thesis (Ph.D.)--The University of Oklahoma, 2010.
520 $a Analyses that test nonzero correlations and incorporate prior information can help accumulate knowledge and advance research at a faster pace than typical analyses that disregard previous studies and continue to test unreasonable nil hypotheses. The performance of several bootstrap and parametric procedures are evaluated using populations that had varying degrees of correlation and nonnormality. With correlated heteroscedastic variables, the parametric procedures produced robust point estimates, but showed liberal error rates that worsened as sample sizes grew to NObs = 1,000. This paper proposes two Bayesian univariate sampling bootstrap procedures (the SlotHI and SlotOI) that exhibit much better error rates across all evaluated populations and prior distributions. Based on this simulation, we suggest that the univariate sampling bootstraps are preferred when testing nonzero correlations in nonnormal populations, regardless if prior information is considered.
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650 4 $a Statistics. $3 517247
650 4 $a Psychology, Psychometrics. $3 1017742
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710 2 $a The University of Oklahoma. $b Department of Psychology. $3 1018422
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790 1 0 $a Rodgers, Joseph L., $e advisor
790 1 0 $a Mendoza, Jorge L. $e committee member
790 1 0 $a Toothaker, Larry E. $e committee member
790 1 0 $a Terry, Robert A. $e committee member
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790 1 0 $a Grasse, Kevin A. $e committee member
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