Multiple test functions and adjusted p -values for test statistics with discrete distributions
The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testin...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Habiger, Joshua D. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Schlagwörter: |
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| Umfang: |
13 |
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| Übergeordnetes Werk: |
Enthalten in: Experimental investigation of long-wavelength optical lattice vibrations in quaternary Al x In y Ga1−x−y N alloys and comparison with results from the pseudo-unit cell model - 2011transfer abstract, JSPI, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:167 ; year:2015 ; pages:1-13 ; extent:13 |
| Links: |
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| DOI / URN: |
10.1016/j.jspi.2015.06.003 |
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ELV039794350 |
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| 520 | |a The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. | ||
| 520 | |a The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. | ||
| 650 | 7 | |a Adjusted p -value |2 Elsevier | |
| 650 | 7 | |a Test function |2 Elsevier | |
| 650 | 7 | |a Abstract randomized p -value |2 Elsevier | |
| 650 | 7 | |a Decision function |2 Elsevier | |
| 650 | 7 | |a False discovery rate |2 Elsevier | |
| 650 | 7 | |a Fuzzy p -value |2 Elsevier | |
| 650 | 7 | |a Sequential hypothesis testing |2 Elsevier | |
| 650 | 7 | |a Mid- p -value |2 Elsevier | |
| 650 | 7 | |a Randomized p -value |2 Elsevier | |
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10.1016/j.jspi.2015.06.003 doi GBVA2015012000025.pica (DE-627)ELV039794350 (ELSEVIER)S0378-3758(15)00117-2 DE-627 ger DE-627 rakwb eng 510 000 310 510 DE-600 000 DE-600 310 DE-600 530 VZ 540 VZ 51.30 bkl Habiger, Joshua D. verfasserin aut Multiple test functions and adjusted p -values for test statistics with discrete distributions 2015transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. Adjusted p -value Elsevier Test function Elsevier Abstract randomized p -value Elsevier Decision function Elsevier False discovery rate Elsevier Fuzzy p -value Elsevier Sequential hypothesis testing Elsevier Mid- p -value Elsevier Randomized p -value Elsevier Enthalten in North-Holland Publ. Co Experimental investigation of long-wavelength optical lattice vibrations in quaternary Al x In y Ga1−x−y N alloys and comparison with results from the pseudo-unit cell model 2011transfer abstract JSPI Amsterdam (DE-627)ELV020955464 volume:167 year:2015 pages:1-13 extent:13 https://doi.org/10.1016/j.jspi.2015.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_26 GBV_ILN_60 GBV_ILN_160 GBV_ILN_2009 GBV_ILN_2099 51.30 Werkstoffprüfung Werkstoffuntersuchung VZ AR 167 2015 1-13 13 045F 510 |
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10.1016/j.jspi.2015.06.003 doi GBVA2015012000025.pica (DE-627)ELV039794350 (ELSEVIER)S0378-3758(15)00117-2 DE-627 ger DE-627 rakwb eng 510 000 310 510 DE-600 000 DE-600 310 DE-600 530 VZ 540 VZ 51.30 bkl Habiger, Joshua D. verfasserin aut Multiple test functions and adjusted p -values for test statistics with discrete distributions 2015transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. Adjusted p -value Elsevier Test function Elsevier Abstract randomized p -value Elsevier Decision function Elsevier False discovery rate Elsevier Fuzzy p -value Elsevier Sequential hypothesis testing Elsevier Mid- p -value Elsevier Randomized p -value Elsevier Enthalten in North-Holland Publ. Co Experimental investigation of long-wavelength optical lattice vibrations in quaternary Al x In y Ga1−x−y N alloys and comparison with results from the pseudo-unit cell model 2011transfer abstract JSPI Amsterdam (DE-627)ELV020955464 volume:167 year:2015 pages:1-13 extent:13 https://doi.org/10.1016/j.jspi.2015.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_26 GBV_ILN_60 GBV_ILN_160 GBV_ILN_2009 GBV_ILN_2099 51.30 Werkstoffprüfung Werkstoffuntersuchung VZ AR 167 2015 1-13 13 045F 510 |
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10.1016/j.jspi.2015.06.003 doi GBVA2015012000025.pica (DE-627)ELV039794350 (ELSEVIER)S0378-3758(15)00117-2 DE-627 ger DE-627 rakwb eng 510 000 310 510 DE-600 000 DE-600 310 DE-600 530 VZ 540 VZ 51.30 bkl Habiger, Joshua D. verfasserin aut Multiple test functions and adjusted p -values for test statistics with discrete distributions 2015transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. Adjusted p -value Elsevier Test function Elsevier Abstract randomized p -value Elsevier Decision function Elsevier False discovery rate Elsevier Fuzzy p -value Elsevier Sequential hypothesis testing Elsevier Mid- p -value Elsevier Randomized p -value Elsevier Enthalten in North-Holland Publ. Co Experimental investigation of long-wavelength optical lattice vibrations in quaternary Al x In y Ga1−x−y N alloys and comparison with results from the pseudo-unit cell model 2011transfer abstract JSPI Amsterdam (DE-627)ELV020955464 volume:167 year:2015 pages:1-13 extent:13 https://doi.org/10.1016/j.jspi.2015.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_26 GBV_ILN_60 GBV_ILN_160 GBV_ILN_2009 GBV_ILN_2099 51.30 Werkstoffprüfung Werkstoffuntersuchung VZ AR 167 2015 1-13 13 045F 510 |
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10.1016/j.jspi.2015.06.003 doi GBVA2015012000025.pica (DE-627)ELV039794350 (ELSEVIER)S0378-3758(15)00117-2 DE-627 ger DE-627 rakwb eng 510 000 310 510 DE-600 000 DE-600 310 DE-600 530 VZ 540 VZ 51.30 bkl Habiger, Joshua D. verfasserin aut Multiple test functions and adjusted p -values for test statistics with discrete distributions 2015transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. Adjusted p -value Elsevier Test function Elsevier Abstract randomized p -value Elsevier Decision function Elsevier False discovery rate Elsevier Fuzzy p -value Elsevier Sequential hypothesis testing Elsevier Mid- p -value Elsevier Randomized p -value Elsevier Enthalten in North-Holland Publ. Co Experimental investigation of long-wavelength optical lattice vibrations in quaternary Al x In y Ga1−x−y N alloys and comparison with results from the pseudo-unit cell model 2011transfer abstract JSPI Amsterdam (DE-627)ELV020955464 volume:167 year:2015 pages:1-13 extent:13 https://doi.org/10.1016/j.jspi.2015.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_26 GBV_ILN_60 GBV_ILN_160 GBV_ILN_2009 GBV_ILN_2099 51.30 Werkstoffprüfung Werkstoffuntersuchung VZ AR 167 2015 1-13 13 045F 510 |
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10.1016/j.jspi.2015.06.003 doi GBVA2015012000025.pica (DE-627)ELV039794350 (ELSEVIER)S0378-3758(15)00117-2 DE-627 ger DE-627 rakwb eng 510 000 310 510 DE-600 000 DE-600 310 DE-600 530 VZ 540 VZ 51.30 bkl Habiger, Joshua D. verfasserin aut Multiple test functions and adjusted p -values for test statistics with discrete distributions 2015transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. Adjusted p -value Elsevier Test function Elsevier Abstract randomized p -value Elsevier Decision function Elsevier False discovery rate Elsevier Fuzzy p -value Elsevier Sequential hypothesis testing Elsevier Mid- p -value Elsevier Randomized p -value Elsevier Enthalten in North-Holland Publ. Co Experimental investigation of long-wavelength optical lattice vibrations in quaternary Al x In y Ga1−x−y N alloys and comparison with results from the pseudo-unit cell model 2011transfer abstract JSPI Amsterdam (DE-627)ELV020955464 volume:167 year:2015 pages:1-13 extent:13 https://doi.org/10.1016/j.jspi.2015.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_26 GBV_ILN_60 GBV_ILN_160 GBV_ILN_2009 GBV_ILN_2099 51.30 Werkstoffprüfung Werkstoffuntersuchung VZ AR 167 2015 1-13 13 045F 510 |
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Enthalten in Experimental investigation of long-wavelength optical lattice vibrations in quaternary Al x In y Ga1−x−y N alloys and comparison with results from the pseudo-unit cell model Amsterdam volume:167 year:2015 pages:1-13 extent:13 |
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Enthalten in Experimental investigation of long-wavelength optical lattice vibrations in quaternary Al x In y Ga1−x−y N alloys and comparison with results from the pseudo-unit cell model Amsterdam volume:167 year:2015 pages:1-13 extent:13 |
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Experimental investigation of long-wavelength optical lattice vibrations in quaternary Al x In y Ga1−x−y N alloys and comparison with results from the pseudo-unit cell model |
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Multiple test functions and adjusted p -values for test statistics with discrete distributions |
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The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. |
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The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. |
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The randomized p -value, (nonrandomized) mid- p -value and abstract randomized p -value have all been recommended for testing a null hypothesis whenever the test statistic has a discrete distribution. This paper provides a unifying framework for these approaches and extends it to the multiple testing setting. In particular, multiplicity adjusted versions of the aforementioned p -values and multiple test functions are developed. It is demonstrated that, whenever the usual nonrandomized and randomized decisions to reject or retain the null hypothesis may differ, the (adjusted) abstract randomized p -value and test function should be reported, especially when the number of tests is large. It is shown that the proposed approach dominates the traditional randomized and nonrandomized approaches in terms of bias and variability. Tools for plotting adjusted abstract randomized p -values and for computing multiple test functions are developed. Examples are used to illustrate the method and to motivate a new type of multiplicity adjusted mid- p -value. |
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