On the use of the covariance matrix to fit correlated data
Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ^2. This paper shows that the effect is a direct consequence of the hypot...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1994 |
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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 Section A: - Amsterdam : Elsevier, 346(1994), 1-2, Seite 306-311 |
| Übergeordnetes Werk: |
volume:346 ; year:1994 ; number:1-2 ; pages:306-311 |
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| 520 | |a Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ^2. This paper shows that the effect is a direct consequence of the hypothesis used to estimate the empirical covariance matrix, namely the linearization on which the usual error propagation relies. The bias can become unacceptable if the normalization error is large, or a large number of data points are fitted. | ||
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(DE-627)NLEJ181687763 (DE-599)GBVNLZ181687763 DE-627 ger DE-627 rakwb eng On the use of the covariance matrix to fit correlated data 1994 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ^2. This paper shows that the effect is a direct consequence of the hypothesis used to estimate the empirical covariance matrix, namely the linearization on which the usual error propagation relies. The bias can become unacceptable if the normalization error is large, or a large number of data points are fitted. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 D'Agostini, G. oth in Nuclear Instruments and Methods in Physics Research Section A: Amsterdam : Elsevier 346(1994), 1-2, Seite 306-311 (DE-627)NLEJ180854372 (DE-600)1466532-3 0168-9002 nnns volume:346 year:1994 number:1-2 pages:306-311 http://dx.doi.org/10.1016/0168-9002(94)90719-6 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 346 1994 1-2 306-311 |
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(DE-627)NLEJ181687763 (DE-599)GBVNLZ181687763 DE-627 ger DE-627 rakwb eng On the use of the covariance matrix to fit correlated data 1994 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ^2. This paper shows that the effect is a direct consequence of the hypothesis used to estimate the empirical covariance matrix, namely the linearization on which the usual error propagation relies. The bias can become unacceptable if the normalization error is large, or a large number of data points are fitted. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 D'Agostini, G. oth in Nuclear Instruments and Methods in Physics Research Section A: Amsterdam : Elsevier 346(1994), 1-2, Seite 306-311 (DE-627)NLEJ180854372 (DE-600)1466532-3 0168-9002 nnns volume:346 year:1994 number:1-2 pages:306-311 http://dx.doi.org/10.1016/0168-9002(94)90719-6 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 346 1994 1-2 306-311 |
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(DE-627)NLEJ181687763 (DE-599)GBVNLZ181687763 DE-627 ger DE-627 rakwb eng On the use of the covariance matrix to fit correlated data 1994 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ^2. This paper shows that the effect is a direct consequence of the hypothesis used to estimate the empirical covariance matrix, namely the linearization on which the usual error propagation relies. The bias can become unacceptable if the normalization error is large, or a large number of data points are fitted. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 D'Agostini, G. oth in Nuclear Instruments and Methods in Physics Research Section A: Amsterdam : Elsevier 346(1994), 1-2, Seite 306-311 (DE-627)NLEJ180854372 (DE-600)1466532-3 0168-9002 nnns volume:346 year:1994 number:1-2 pages:306-311 http://dx.doi.org/10.1016/0168-9002(94)90719-6 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 346 1994 1-2 306-311 |
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(DE-627)NLEJ181687763 (DE-599)GBVNLZ181687763 DE-627 ger DE-627 rakwb eng On the use of the covariance matrix to fit correlated data 1994 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ^2. This paper shows that the effect is a direct consequence of the hypothesis used to estimate the empirical covariance matrix, namely the linearization on which the usual error propagation relies. The bias can become unacceptable if the normalization error is large, or a large number of data points are fitted. Elsevier Journal Backfiles on ScienceDirect 1907 - 2002 D'Agostini, G. oth in Nuclear Instruments and Methods in Physics Research Section A: Amsterdam : Elsevier 346(1994), 1-2, Seite 306-311 (DE-627)NLEJ180854372 (DE-600)1466532-3 0168-9002 nnns volume:346 year:1994 number:1-2 pages:306-311 http://dx.doi.org/10.1016/0168-9002(94)90719-6 GBV_USEFLAG_H ZDB-1-SDJ GBV_NL_ARTICLE AR 346 1994 1-2 306-311 |
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Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ^2. This paper shows that the effect is a direct consequence of the hypothesis used to estimate the empirical covariance matrix, namely the linearization on which the usual error propagation relies. The bias can become unacceptable if the normalization error is large, or a large number of data points are fitted. |
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Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ^2. This paper shows that the effect is a direct consequence of the hypothesis used to estimate the empirical covariance matrix, namely the linearization on which the usual error propagation relies. The bias can become unacceptable if the normalization error is large, or a large number of data points are fitted. |
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Best fits to data which are affected by systematic uncertainties on the normalization factor have the tendency to produce curves lower than expected if the covariance matrix of the data points is used in the definition of the χ^2. This paper shows that the effect is a direct consequence of the hypothesis used to estimate the empirical covariance matrix, namely the linearization on which the usual error propagation relies. The bias can become unacceptable if the normalization error is large, or a large number of data points are fitted. |
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