Hierarchical crop yield linear model
Abstract This research investigates which statistical procedure—panel random effect model or hierarchical linear model accounts for the observed spatial random variation in crop yields. Identification of the statistical procedure is accomplished using Akaike information criteria, covariance test of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Shaik, Saleem [verfasserIn] Bhattacharjee, Sanjoy [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Letters in spatial and resource sciences - Berlin : Springer, 2008, 9(2015), 2 vom: 24. Juni, Seite 219-231 |
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| Übergeordnetes Werk: |
volume:9 ; year:2015 ; number:2 ; day:24 ; month:06 ; pages:219-231 |
| Links: |
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| DOI / URN: |
10.1007/s12076-015-0153-3 |
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| Katalog-ID: |
SPR024214116 |
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| 520 | |a Abstract This research investigates which statistical procedure—panel random effect model or hierarchical linear model accounts for the observed spatial random variation in crop yields. Identification of the statistical procedure is accomplished using Akaike information criteria, covariance test of the spatial random variations and out-of-sample performance using holdout sample. Following the identification of the statistical procedure, normality of crop yield residuals are statistically tested using skewness, kurtosis and omnibus test. An empirical application to United States county yields of 20 crops, grown across 48 states during 1957–2013 suggests the need to account for random variation based on the multi-level hierarchy. | ||
| 650 | 4 | |a Crop yield model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Panel random effects model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Hierarchical linear model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a AIC, covariance test |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Out-of-sample performance |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Crop yield normality |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a US county crop yields, 1957–2013 |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Bhattacharjee, Sanjoy |e verfasserin |4 aut | |
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10.1007/s12076-015-0153-3 doi (DE-627)SPR024214116 (SPR)s12076-015-0153-3-e DE-627 ger DE-627 rakwb eng 330 ASE Shaik, Saleem verfasserin aut Hierarchical crop yield linear model 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This research investigates which statistical procedure—panel random effect model or hierarchical linear model accounts for the observed spatial random variation in crop yields. Identification of the statistical procedure is accomplished using Akaike information criteria, covariance test of the spatial random variations and out-of-sample performance using holdout sample. Following the identification of the statistical procedure, normality of crop yield residuals are statistically tested using skewness, kurtosis and omnibus test. An empirical application to United States county yields of 20 crops, grown across 48 states during 1957–2013 suggests the need to account for random variation based on the multi-level hierarchy. Crop yield model (dpeaa)DE-He213 Panel random effects model (dpeaa)DE-He213 Hierarchical linear model (dpeaa)DE-He213 AIC, covariance test (dpeaa)DE-He213 Out-of-sample performance (dpeaa)DE-He213 Crop yield normality (dpeaa)DE-He213 US county crop yields, 1957–2013 (dpeaa)DE-He213 Bhattacharjee, Sanjoy verfasserin aut Enthalten in Letters in spatial and resource sciences Berlin : Springer, 2008 9(2015), 2 vom: 24. Juni, Seite 219-231 (DE-627)571612679 (DE-600)2436019-3 1864-404X nnns volume:9 year:2015 number:2 day:24 month:06 pages:219-231 https://dx.doi.org/10.1007/s12076-015-0153-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_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 AR 9 2015 2 24 06 219-231 |
| spelling |
10.1007/s12076-015-0153-3 doi (DE-627)SPR024214116 (SPR)s12076-015-0153-3-e DE-627 ger DE-627 rakwb eng 330 ASE Shaik, Saleem verfasserin aut Hierarchical crop yield linear model 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This research investigates which statistical procedure—panel random effect model or hierarchical linear model accounts for the observed spatial random variation in crop yields. Identification of the statistical procedure is accomplished using Akaike information criteria, covariance test of the spatial random variations and out-of-sample performance using holdout sample. Following the identification of the statistical procedure, normality of crop yield residuals are statistically tested using skewness, kurtosis and omnibus test. An empirical application to United States county yields of 20 crops, grown across 48 states during 1957–2013 suggests the need to account for random variation based on the multi-level hierarchy. Crop yield model (dpeaa)DE-He213 Panel random effects model (dpeaa)DE-He213 Hierarchical linear model (dpeaa)DE-He213 AIC, covariance test (dpeaa)DE-He213 Out-of-sample performance (dpeaa)DE-He213 Crop yield normality (dpeaa)DE-He213 US county crop yields, 1957–2013 (dpeaa)DE-He213 Bhattacharjee, Sanjoy verfasserin aut Enthalten in Letters in spatial and resource sciences Berlin : Springer, 2008 9(2015), 2 vom: 24. Juni, Seite 219-231 (DE-627)571612679 (DE-600)2436019-3 1864-404X nnns volume:9 year:2015 number:2 day:24 month:06 pages:219-231 https://dx.doi.org/10.1007/s12076-015-0153-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_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 AR 9 2015 2 24 06 219-231 |
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10.1007/s12076-015-0153-3 doi (DE-627)SPR024214116 (SPR)s12076-015-0153-3-e DE-627 ger DE-627 rakwb eng 330 ASE Shaik, Saleem verfasserin aut Hierarchical crop yield linear model 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This research investigates which statistical procedure—panel random effect model or hierarchical linear model accounts for the observed spatial random variation in crop yields. Identification of the statistical procedure is accomplished using Akaike information criteria, covariance test of the spatial random variations and out-of-sample performance using holdout sample. Following the identification of the statistical procedure, normality of crop yield residuals are statistically tested using skewness, kurtosis and omnibus test. An empirical application to United States county yields of 20 crops, grown across 48 states during 1957–2013 suggests the need to account for random variation based on the multi-level hierarchy. Crop yield model (dpeaa)DE-He213 Panel random effects model (dpeaa)DE-He213 Hierarchical linear model (dpeaa)DE-He213 AIC, covariance test (dpeaa)DE-He213 Out-of-sample performance (dpeaa)DE-He213 Crop yield normality (dpeaa)DE-He213 US county crop yields, 1957–2013 (dpeaa)DE-He213 Bhattacharjee, Sanjoy verfasserin aut Enthalten in Letters in spatial and resource sciences Berlin : Springer, 2008 9(2015), 2 vom: 24. Juni, Seite 219-231 (DE-627)571612679 (DE-600)2436019-3 1864-404X nnns volume:9 year:2015 number:2 day:24 month:06 pages:219-231 https://dx.doi.org/10.1007/s12076-015-0153-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-WIW SSG-OLC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 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_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_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 AR 9 2015 2 24 06 219-231 |
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Shaik, Saleem |
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Shaik, Saleem ddc 330 misc Crop yield model misc Panel random effects model misc Hierarchical linear model misc AIC, covariance test misc Out-of-sample performance misc Crop yield normality misc US county crop yields, 1957–2013 Hierarchical crop yield linear model |
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330 ASE Hierarchical crop yield linear model Crop yield model (dpeaa)DE-He213 Panel random effects model (dpeaa)DE-He213 Hierarchical linear model (dpeaa)DE-He213 AIC, covariance test (dpeaa)DE-He213 Out-of-sample performance (dpeaa)DE-He213 Crop yield normality (dpeaa)DE-He213 US county crop yields, 1957–2013 (dpeaa)DE-He213 |
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ddc 330 misc Crop yield model misc Panel random effects model misc Hierarchical linear model misc AIC, covariance test misc Out-of-sample performance misc Crop yield normality misc US county crop yields, 1957–2013 |
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ddc 330 misc Crop yield model misc Panel random effects model misc Hierarchical linear model misc AIC, covariance test misc Out-of-sample performance misc Crop yield normality misc US county crop yields, 1957–2013 |
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Hierarchical crop yield linear model |
| abstract |
Abstract This research investigates which statistical procedure—panel random effect model or hierarchical linear model accounts for the observed spatial random variation in crop yields. Identification of the statistical procedure is accomplished using Akaike information criteria, covariance test of the spatial random variations and out-of-sample performance using holdout sample. Following the identification of the statistical procedure, normality of crop yield residuals are statistically tested using skewness, kurtosis and omnibus test. An empirical application to United States county yields of 20 crops, grown across 48 states during 1957–2013 suggests the need to account for random variation based on the multi-level hierarchy. |
| abstractGer |
Abstract This research investigates which statistical procedure—panel random effect model or hierarchical linear model accounts for the observed spatial random variation in crop yields. Identification of the statistical procedure is accomplished using Akaike information criteria, covariance test of the spatial random variations and out-of-sample performance using holdout sample. Following the identification of the statistical procedure, normality of crop yield residuals are statistically tested using skewness, kurtosis and omnibus test. An empirical application to United States county yields of 20 crops, grown across 48 states during 1957–2013 suggests the need to account for random variation based on the multi-level hierarchy. |
| abstract_unstemmed |
Abstract This research investigates which statistical procedure—panel random effect model or hierarchical linear model accounts for the observed spatial random variation in crop yields. Identification of the statistical procedure is accomplished using Akaike information criteria, covariance test of the spatial random variations and out-of-sample performance using holdout sample. Following the identification of the statistical procedure, normality of crop yield residuals are statistically tested using skewness, kurtosis and omnibus test. An empirical application to United States county yields of 20 crops, grown across 48 states during 1957–2013 suggests the need to account for random variation based on the multi-level hierarchy. |
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Hierarchical crop yield linear model |
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https://dx.doi.org/10.1007/s12076-015-0153-3 |
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Bhattacharjee, Sanjoy |
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| doi_str |
10.1007/s12076-015-0153-3 |
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2024-07-03T23:59:38.207Z |
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