Efficient algorithms for estimating the width of nearly normal distributions
Typical physics data samples often conform to Gaussian distributions with admixtures of more slowly varying backgrounds. Under such circumstances the standard deviation is known to be a poor statistical measure of distribution width. As an alternative, the performance of Gini's mean difference...
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| Autor*in: |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1983 |
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| Reproduktion: |
Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 |
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| Übergeordnetes Werk: |
in: Nuclear Instruments and Methods In Physics Research - Amsterdam : Elsevier, 211(1983), 2-3, Seite 439-445 |
| Übergeordnetes Werk: |
volume:211 ; year:1983 ; number:2-3 ; pages:439-445 |
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| 520 | |a Typical physics data samples often conform to Gaussian distributions with admixtures of more slowly varying backgrounds. Under such circumstances the standard deviation is known to be a poor statistical measure of distribution width. As an alternative, the performance of Gini's mean difference is compared with the standard deviation and the mean deviation. Variants which sum over subsets of all possible pairs are shown to have statistical efficiencies comparable to the mean difference and mean deviation but do not require extensive data storage or a priori knowledge of the sample mean. These statistics are reasonable candidates for monitoring the distribution width of a real time data stream. | ||
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(DE-627)NLEJ180604910 (DE-599)GBVNLZ180604910 DE-627 ger DE-627 rakwb eng Efficient algorithms for estimating the width of nearly normal distributions 1983 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Typical physics data samples often conform to Gaussian distributions with admixtures of more slowly varying backgrounds. Under such circumstances the standard deviation is known to be a poor statistical measure of distribution width. As an alternative, the performance of Gini's mean difference is compared with the standard deviation and the mean deviation. Variants which sum over subsets of all possible pairs are shown to have statistical efficiencies comparable to the mean difference and mean deviation but do not require extensive data storage or a priori knowledge of the sample mean. These statistics are reasonable candidates for monitoring the distribution width of a real time data stream. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Akerlof, C.W. oth in Nuclear Instruments and Methods In Physics Research Amsterdam : Elsevier 211(1983), 2-3, Seite 439-445 (DE-627)NLEJ180600451 (DE-600)1466532-3 0167-5087 nnns volume:211 year:1983 number:2-3 pages:439-445 http://linkinghub.elsevier.com/retrieve/pii/0167-5087(83)90272-7 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 211 1983 2-3 439-445 |
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(DE-627)NLEJ180604910 (DE-599)GBVNLZ180604910 DE-627 ger DE-627 rakwb eng Efficient algorithms for estimating the width of nearly normal distributions 1983 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Typical physics data samples often conform to Gaussian distributions with admixtures of more slowly varying backgrounds. Under such circumstances the standard deviation is known to be a poor statistical measure of distribution width. As an alternative, the performance of Gini's mean difference is compared with the standard deviation and the mean deviation. Variants which sum over subsets of all possible pairs are shown to have statistical efficiencies comparable to the mean difference and mean deviation but do not require extensive data storage or a priori knowledge of the sample mean. These statistics are reasonable candidates for monitoring the distribution width of a real time data stream. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Akerlof, C.W. oth in Nuclear Instruments and Methods In Physics Research Amsterdam : Elsevier 211(1983), 2-3, Seite 439-445 (DE-627)NLEJ180600451 (DE-600)1466532-3 0167-5087 nnns volume:211 year:1983 number:2-3 pages:439-445 http://linkinghub.elsevier.com/retrieve/pii/0167-5087(83)90272-7 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 211 1983 2-3 439-445 |
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(DE-627)NLEJ180604910 (DE-599)GBVNLZ180604910 DE-627 ger DE-627 rakwb eng Efficient algorithms for estimating the width of nearly normal distributions 1983 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Typical physics data samples often conform to Gaussian distributions with admixtures of more slowly varying backgrounds. Under such circumstances the standard deviation is known to be a poor statistical measure of distribution width. As an alternative, the performance of Gini's mean difference is compared with the standard deviation and the mean deviation. Variants which sum over subsets of all possible pairs are shown to have statistical efficiencies comparable to the mean difference and mean deviation but do not require extensive data storage or a priori knowledge of the sample mean. These statistics are reasonable candidates for monitoring the distribution width of a real time data stream. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Akerlof, C.W. oth in Nuclear Instruments and Methods In Physics Research Amsterdam : Elsevier 211(1983), 2-3, Seite 439-445 (DE-627)NLEJ180600451 (DE-600)1466532-3 0167-5087 nnns volume:211 year:1983 number:2-3 pages:439-445 http://linkinghub.elsevier.com/retrieve/pii/0167-5087(83)90272-7 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 211 1983 2-3 439-445 |
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(DE-627)NLEJ180604910 (DE-599)GBVNLZ180604910 DE-627 ger DE-627 rakwb eng Efficient algorithms for estimating the width of nearly normal distributions 1983 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Typical physics data samples often conform to Gaussian distributions with admixtures of more slowly varying backgrounds. Under such circumstances the standard deviation is known to be a poor statistical measure of distribution width. As an alternative, the performance of Gini's mean difference is compared with the standard deviation and the mean deviation. Variants which sum over subsets of all possible pairs are shown to have statistical efficiencies comparable to the mean difference and mean deviation but do not require extensive data storage or a priori knowledge of the sample mean. These statistics are reasonable candidates for monitoring the distribution width of a real time data stream. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 Akerlof, C.W. oth in Nuclear Instruments and Methods In Physics Research Amsterdam : Elsevier 211(1983), 2-3, Seite 439-445 (DE-627)NLEJ180600451 (DE-600)1466532-3 0167-5087 nnns volume:211 year:1983 number:2-3 pages:439-445 http://linkinghub.elsevier.com/retrieve/pii/0167-5087(83)90272-7 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 211 1983 2-3 439-445 |
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efficient algorithms for estimating the width of nearly normal distributions |
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Efficient algorithms for estimating the width of nearly normal distributions |
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Typical physics data samples often conform to Gaussian distributions with admixtures of more slowly varying backgrounds. Under such circumstances the standard deviation is known to be a poor statistical measure of distribution width. As an alternative, the performance of Gini's mean difference is compared with the standard deviation and the mean deviation. Variants which sum over subsets of all possible pairs are shown to have statistical efficiencies comparable to the mean difference and mean deviation but do not require extensive data storage or a priori knowledge of the sample mean. These statistics are reasonable candidates for monitoring the distribution width of a real time data stream. |
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Typical physics data samples often conform to Gaussian distributions with admixtures of more slowly varying backgrounds. Under such circumstances the standard deviation is known to be a poor statistical measure of distribution width. As an alternative, the performance of Gini's mean difference is compared with the standard deviation and the mean deviation. Variants which sum over subsets of all possible pairs are shown to have statistical efficiencies comparable to the mean difference and mean deviation but do not require extensive data storage or a priori knowledge of the sample mean. These statistics are reasonable candidates for monitoring the distribution width of a real time data stream. |
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Typical physics data samples often conform to Gaussian distributions with admixtures of more slowly varying backgrounds. Under such circumstances the standard deviation is known to be a poor statistical measure of distribution width. As an alternative, the performance of Gini's mean difference is compared with the standard deviation and the mean deviation. Variants which sum over subsets of all possible pairs are shown to have statistical efficiencies comparable to the mean difference and mean deviation but do not require extensive data storage or a priori knowledge of the sample mean. These statistics are reasonable candidates for monitoring the distribution width of a real time data stream. |
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