Unequal Priors in Linear Discriminant Analysis
Abstract Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting t...
Ausführliche Beschreibung
Autor*in: |
van Meegen, Carmen [verfasserIn] Schnackenberg, Sarah [verfasserIn] Ligges, Uwe [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Journal of classification - New York, NY : Springer, 1984, 37(2019), 3 vom: 24. Juli, Seite 598-615 |
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Übergeordnetes Werk: |
volume:37 ; year:2019 ; number:3 ; day:24 ; month:07 ; pages:598-615 |
Links: |
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DOI / URN: |
10.1007/s00357-019-09336-2 |
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Katalog-ID: |
SPR042119235 |
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10.1007/s00357-019-09336-2 doi (DE-627)SPR042119235 (SPR)s00357-019-09336-2-e DE-627 ger DE-627 rakwb eng 020 ASE 020 510 ASE 31.73 bkl 06.74 bkl van Meegen, Carmen verfasserin aut Unequal Priors in Linear Discriminant Analysis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting that GDA and FDA yield the same classification results for any number of classes and features. We discuss in which ways unequal priors have to enter these two methods in theory as well as algorithms. This may be of particular interest if prior knowledge is available and should be included in the discriminant rule. Various estimators that use prior probabilities in different places (e.g. prior-based weighting of the covariance matrix) are compared both in theory and by means of simulations. Unequal priors (dpeaa)DE-He213 Linear discriminant analysis (dpeaa)DE-He213 Fisher (dpeaa)DE-He213 Schnackenberg, Sarah verfasserin aut Ligges, Uwe verfasserin aut Enthalten in Journal of classification New York, NY : Springer, 1984 37(2019), 3 vom: 24. Juli, Seite 598-615 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:37 year:2019 number:3 day:24 month:07 pages:598-615 https://dx.doi.org/10.1007/s00357-019-09336-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.73 ASE 06.74 ASE AR 37 2019 3 24 07 598-615 |
spelling |
10.1007/s00357-019-09336-2 doi (DE-627)SPR042119235 (SPR)s00357-019-09336-2-e DE-627 ger DE-627 rakwb eng 020 ASE 020 510 ASE 31.73 bkl 06.74 bkl van Meegen, Carmen verfasserin aut Unequal Priors in Linear Discriminant Analysis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting that GDA and FDA yield the same classification results for any number of classes and features. We discuss in which ways unequal priors have to enter these two methods in theory as well as algorithms. This may be of particular interest if prior knowledge is available and should be included in the discriminant rule. Various estimators that use prior probabilities in different places (e.g. prior-based weighting of the covariance matrix) are compared both in theory and by means of simulations. Unequal priors (dpeaa)DE-He213 Linear discriminant analysis (dpeaa)DE-He213 Fisher (dpeaa)DE-He213 Schnackenberg, Sarah verfasserin aut Ligges, Uwe verfasserin aut Enthalten in Journal of classification New York, NY : Springer, 1984 37(2019), 3 vom: 24. Juli, Seite 598-615 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:37 year:2019 number:3 day:24 month:07 pages:598-615 https://dx.doi.org/10.1007/s00357-019-09336-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.73 ASE 06.74 ASE AR 37 2019 3 24 07 598-615 |
allfields_unstemmed |
10.1007/s00357-019-09336-2 doi (DE-627)SPR042119235 (SPR)s00357-019-09336-2-e DE-627 ger DE-627 rakwb eng 020 ASE 020 510 ASE 31.73 bkl 06.74 bkl van Meegen, Carmen verfasserin aut Unequal Priors in Linear Discriminant Analysis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting that GDA and FDA yield the same classification results for any number of classes and features. We discuss in which ways unequal priors have to enter these two methods in theory as well as algorithms. This may be of particular interest if prior knowledge is available and should be included in the discriminant rule. Various estimators that use prior probabilities in different places (e.g. prior-based weighting of the covariance matrix) are compared both in theory and by means of simulations. Unequal priors (dpeaa)DE-He213 Linear discriminant analysis (dpeaa)DE-He213 Fisher (dpeaa)DE-He213 Schnackenberg, Sarah verfasserin aut Ligges, Uwe verfasserin aut Enthalten in Journal of classification New York, NY : Springer, 1984 37(2019), 3 vom: 24. Juli, Seite 598-615 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:37 year:2019 number:3 day:24 month:07 pages:598-615 https://dx.doi.org/10.1007/s00357-019-09336-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.73 ASE 06.74 ASE AR 37 2019 3 24 07 598-615 |
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10.1007/s00357-019-09336-2 doi (DE-627)SPR042119235 (SPR)s00357-019-09336-2-e DE-627 ger DE-627 rakwb eng 020 ASE 020 510 ASE 31.73 bkl 06.74 bkl van Meegen, Carmen verfasserin aut Unequal Priors in Linear Discriminant Analysis 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting that GDA and FDA yield the same classification results for any number of classes and features. We discuss in which ways unequal priors have to enter these two methods in theory as well as algorithms. This may be of particular interest if prior knowledge is available and should be included in the discriminant rule. Various estimators that use prior probabilities in different places (e.g. prior-based weighting of the covariance matrix) are compared both in theory and by means of simulations. Unequal priors (dpeaa)DE-He213 Linear discriminant analysis (dpeaa)DE-He213 Fisher (dpeaa)DE-He213 Schnackenberg, Sarah verfasserin aut Ligges, Uwe verfasserin aut Enthalten in Journal of classification New York, NY : Springer, 1984 37(2019), 3 vom: 24. Juli, Seite 598-615 (DE-627)253769558 (DE-600)1459289-7 1432-1343 nnns volume:37 year:2019 number:3 day:24 month:07 pages:598-615 https://dx.doi.org/10.1007/s00357-019-09336-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-BBI SSG-OPC-MAT SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.73 ASE 06.74 ASE AR 37 2019 3 24 07 598-615 |
language |
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Enthalten in Journal of classification 37(2019), 3 vom: 24. Juli, Seite 598-615 volume:37 year:2019 number:3 day:24 month:07 pages:598-615 |
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van Meegen, Carmen @@aut@@ Schnackenberg, Sarah @@aut@@ Ligges, Uwe @@aut@@ |
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van Meegen, Carmen |
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van Meegen, Carmen ddc 020 bkl 31.73 bkl 06.74 misc Unequal priors misc Linear discriminant analysis misc Fisher Unequal Priors in Linear Discriminant Analysis |
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unequal priors in linear discriminant analysis |
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Unequal Priors in Linear Discriminant Analysis |
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Abstract Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting that GDA and FDA yield the same classification results for any number of classes and features. We discuss in which ways unequal priors have to enter these two methods in theory as well as algorithms. This may be of particular interest if prior knowledge is available and should be included in the discriminant rule. Various estimators that use prior probabilities in different places (e.g. prior-based weighting of the covariance matrix) are compared both in theory and by means of simulations. |
abstractGer |
Abstract Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting that GDA and FDA yield the same classification results for any number of classes and features. We discuss in which ways unequal priors have to enter these two methods in theory as well as algorithms. This may be of particular interest if prior knowledge is available and should be included in the discriminant rule. Various estimators that use prior probabilities in different places (e.g. prior-based weighting of the covariance matrix) are compared both in theory and by means of simulations. |
abstract_unstemmed |
Abstract Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting that GDA and FDA yield the same classification results for any number of classes and features. We discuss in which ways unequal priors have to enter these two methods in theory as well as algorithms. This may be of particular interest if prior knowledge is available and should be included in the discriminant rule. Various estimators that use prior probabilities in different places (e.g. prior-based weighting of the covariance matrix) are compared both in theory and by means of simulations. |
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Unequal Priors in Linear Discriminant Analysis |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR042119235</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110174140.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201126s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00357-019-09336-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR042119235</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00357-019-09336-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">020</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">020</subfield><subfield code="a">510</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">31.73</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">06.74</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">van Meegen, Carmen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Unequal Priors in Linear Discriminant Analysis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Dealing with unequal priors in both linear discriminant analysis (LDA) based on Gaussian distribution (GDA) and in Fisher’s linear discriminant analysis (FDA) is frequently used in practice but almost described in neither any textbook nor papers. This is one of the first papers exhibiting that GDA and FDA yield the same classification results for any number of classes and features. We discuss in which ways unequal priors have to enter these two methods in theory as well as algorithms. This may be of particular interest if prior knowledge is available and should be included in the discriminant rule. Various estimators that use prior probabilities in different places (e.g. prior-based weighting of the covariance matrix) are compared both in theory and by means of simulations.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Unequal priors</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Linear discriminant analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fisher</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schnackenberg, Sarah</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ligges, Uwe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of classification</subfield><subfield code="d">New York, NY : Springer, 1984</subfield><subfield code="g">37(2019), 3 vom: 24. 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