Estimating the number of true null hypotheses in multiple hypothesis testing
Abstract The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setti...
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| Autor*in: |
Hwang, Yi-Ting [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
Adaptive FDR controlling procedure |
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| Anmerkung: |
© Springer Science+Business Media New York 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Statistics and computing - Springer US, 1991, 24(2013), 3 vom: 08. Feb., Seite 399-416 |
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| Übergeordnetes Werk: |
volume:24 ; year:2013 ; number:3 ; day:08 ; month:02 ; pages:399-416 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11222-013-9377-5 |
|---|
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OLC2033746127 |
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| 520 | |a Abstract The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m0) to overcome this problem. Since m0 is unknown, estimators of m0 are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m0, which is shown to overestimate m0 (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m0. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings. | ||
| 650 | 4 | |a Adaptive FDR controlling procedure | |
| 650 | 4 | |a False discovery rate | |
| 650 | 4 | |a Multiple hypothesis testing | |
| 650 | 4 | |a Number of true null hypotheses | |
| 650 | 4 | |a Sensitivity | |
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| 700 | 1 | |a Wang, Chun-Chao |4 aut | |
| 700 | 1 | |a Lee, Meng Feng |4 aut | |
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10.1007/s11222-013-9377-5 doi (DE-627)OLC2033746127 (DE-He213)s11222-013-9377-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Hwang, Yi-Ting verfasserin aut Estimating the number of true null hypotheses in multiple hypothesis testing 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m0) to overcome this problem. Since m0 is unknown, estimators of m0 are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m0, which is shown to overestimate m0 (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m0. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings. Adaptive FDR controlling procedure False discovery rate Multiple hypothesis testing Number of true null hypotheses Sensitivity Kuo, Hsun-Chih aut Wang, Chun-Chao aut Lee, Meng Feng aut Enthalten in Statistics and computing Springer US, 1991 24(2013), 3 vom: 08. Feb., Seite 399-416 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:24 year:2013 number:3 day:08 month:02 pages:399-416 https://doi.org/10.1007/s11222-013-9377-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2012 GBV_ILN_4126 AR 24 2013 3 08 02 399-416 |
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10.1007/s11222-013-9377-5 doi (DE-627)OLC2033746127 (DE-He213)s11222-013-9377-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Hwang, Yi-Ting verfasserin aut Estimating the number of true null hypotheses in multiple hypothesis testing 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m0) to overcome this problem. Since m0 is unknown, estimators of m0 are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m0, which is shown to overestimate m0 (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m0. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings. Adaptive FDR controlling procedure False discovery rate Multiple hypothesis testing Number of true null hypotheses Sensitivity Kuo, Hsun-Chih aut Wang, Chun-Chao aut Lee, Meng Feng aut Enthalten in Statistics and computing Springer US, 1991 24(2013), 3 vom: 08. Feb., Seite 399-416 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:24 year:2013 number:3 day:08 month:02 pages:399-416 https://doi.org/10.1007/s11222-013-9377-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2012 GBV_ILN_4126 AR 24 2013 3 08 02 399-416 |
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10.1007/s11222-013-9377-5 doi (DE-627)OLC2033746127 (DE-He213)s11222-013-9377-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Hwang, Yi-Ting verfasserin aut Estimating the number of true null hypotheses in multiple hypothesis testing 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m0) to overcome this problem. Since m0 is unknown, estimators of m0 are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m0, which is shown to overestimate m0 (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m0. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings. Adaptive FDR controlling procedure False discovery rate Multiple hypothesis testing Number of true null hypotheses Sensitivity Kuo, Hsun-Chih aut Wang, Chun-Chao aut Lee, Meng Feng aut Enthalten in Statistics and computing Springer US, 1991 24(2013), 3 vom: 08. Feb., Seite 399-416 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:24 year:2013 number:3 day:08 month:02 pages:399-416 https://doi.org/10.1007/s11222-013-9377-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2012 GBV_ILN_4126 AR 24 2013 3 08 02 399-416 |
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10.1007/s11222-013-9377-5 doi (DE-627)OLC2033746127 (DE-He213)s11222-013-9377-5-p DE-627 ger DE-627 rakwb eng 004 620 VZ Hwang, Yi-Ting verfasserin aut Estimating the number of true null hypotheses in multiple hypothesis testing 2013 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2013 Abstract The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m0) to overcome this problem. Since m0 is unknown, estimators of m0 are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m0, which is shown to overestimate m0 (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m0. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings. Adaptive FDR controlling procedure False discovery rate Multiple hypothesis testing Number of true null hypotheses Sensitivity Kuo, Hsun-Chih aut Wang, Chun-Chao aut Lee, Meng Feng aut Enthalten in Statistics and computing Springer US, 1991 24(2013), 3 vom: 08. Feb., Seite 399-416 (DE-627)131007963 (DE-600)1087487-2 (DE-576)052732894 0960-3174 nnns volume:24 year:2013 number:3 day:08 month:02 pages:399-416 https://doi.org/10.1007/s11222-013-9377-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2012 GBV_ILN_4126 AR 24 2013 3 08 02 399-416 |
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Estimating the number of true null hypotheses in multiple hypothesis testing |
| abstract |
Abstract The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m0) to overcome this problem. Since m0 is unknown, estimators of m0 are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m0, which is shown to overestimate m0 (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m0. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings. © Springer Science+Business Media New York 2013 |
| abstractGer |
Abstract The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m0) to overcome this problem. Since m0 is unknown, estimators of m0 are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m0, which is shown to overestimate m0 (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m0. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings. © Springer Science+Business Media New York 2013 |
| abstract_unstemmed |
Abstract The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m0) to overcome this problem. Since m0 is unknown, estimators of m0 are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m0, which is shown to overestimate m0 (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m0. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings. © Springer Science+Business Media New York 2013 |
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| title_short |
Estimating the number of true null hypotheses in multiple hypothesis testing |
| url |
https://doi.org/10.1007/s11222-013-9377-5 |
| remote_bool |
false |
| author2 |
Kuo, Hsun-Chih Wang, Chun-Chao Lee, Meng Feng |
| author2Str |
Kuo, Hsun-Chih Wang, Chun-Chao Lee, Meng Feng |
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131007963 |
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| hochschulschrift_bool |
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| doi_str |
10.1007/s11222-013-9377-5 |
| up_date |
2024-07-03T18:16:43.569Z |
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7.3999977 |