Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models
Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise....
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Pandit, Pramit [verfasserIn] Dey, Prithwiraj [verfasserIn] Krishnamurthy, K. N. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: SN Computer Science - Singapore : Springer Singapore, 2020, 2(2021), 2 vom: 06. Feb. |
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| Übergeordnetes Werk: |
volume:2 ; year:2021 ; number:2 ; day:06 ; month:02 |
| Links: |
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| DOI / URN: |
10.1007/s42979-021-00473-3 |
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| Katalog-ID: |
SPR043056660 |
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| 520 | |a Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. | ||
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| 650 | 4 | |a Multicollinearity |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multiple linear regression |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Dey, Prithwiraj |e verfasserin |4 aut | |
| 700 | 1 | |a Krishnamurthy, K. N. |e verfasserin |4 aut | |
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10.1007/s42979-021-00473-3 doi (DE-627)SPR043056660 (DE-599)SPRs42979-021-00473-3-e (SPR)s42979-021-00473-3-e DE-627 ger DE-627 rakwb eng Pandit, Pramit verfasserin aut Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. Average width (dpeaa)DE-He213 Fuzzy linear regression (dpeaa)DE-He213 Model efficiency (dpeaa)DE-He213 Multicollinearity (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Dey, Prithwiraj verfasserin aut Krishnamurthy, K. N. verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 2(2021), 2 vom: 06. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:2 year:2021 number:2 day:06 month:02 https://dx.doi.org/10.1007/s42979-021-00473-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 2 06 02 |
| spelling |
10.1007/s42979-021-00473-3 doi (DE-627)SPR043056660 (DE-599)SPRs42979-021-00473-3-e (SPR)s42979-021-00473-3-e DE-627 ger DE-627 rakwb eng Pandit, Pramit verfasserin aut Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. Average width (dpeaa)DE-He213 Fuzzy linear regression (dpeaa)DE-He213 Model efficiency (dpeaa)DE-He213 Multicollinearity (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Dey, Prithwiraj verfasserin aut Krishnamurthy, K. N. verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 2(2021), 2 vom: 06. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:2 year:2021 number:2 day:06 month:02 https://dx.doi.org/10.1007/s42979-021-00473-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 2 06 02 |
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10.1007/s42979-021-00473-3 doi (DE-627)SPR043056660 (DE-599)SPRs42979-021-00473-3-e (SPR)s42979-021-00473-3-e DE-627 ger DE-627 rakwb eng Pandit, Pramit verfasserin aut Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. Average width (dpeaa)DE-He213 Fuzzy linear regression (dpeaa)DE-He213 Model efficiency (dpeaa)DE-He213 Multicollinearity (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Dey, Prithwiraj verfasserin aut Krishnamurthy, K. N. verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 2(2021), 2 vom: 06. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:2 year:2021 number:2 day:06 month:02 https://dx.doi.org/10.1007/s42979-021-00473-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 2 06 02 |
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10.1007/s42979-021-00473-3 doi (DE-627)SPR043056660 (DE-599)SPRs42979-021-00473-3-e (SPR)s42979-021-00473-3-e DE-627 ger DE-627 rakwb eng Pandit, Pramit verfasserin aut Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. Average width (dpeaa)DE-He213 Fuzzy linear regression (dpeaa)DE-He213 Model efficiency (dpeaa)DE-He213 Multicollinearity (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Dey, Prithwiraj verfasserin aut Krishnamurthy, K. N. verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 2(2021), 2 vom: 06. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:2 year:2021 number:2 day:06 month:02 https://dx.doi.org/10.1007/s42979-021-00473-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 2 06 02 |
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10.1007/s42979-021-00473-3 doi (DE-627)SPR043056660 (DE-599)SPRs42979-021-00473-3-e (SPR)s42979-021-00473-3-e DE-627 ger DE-627 rakwb eng Pandit, Pramit verfasserin aut Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. Average width (dpeaa)DE-He213 Fuzzy linear regression (dpeaa)DE-He213 Model efficiency (dpeaa)DE-He213 Multicollinearity (dpeaa)DE-He213 Multiple linear regression (dpeaa)DE-He213 Dey, Prithwiraj verfasserin aut Krishnamurthy, K. N. verfasserin aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 2(2021), 2 vom: 06. Feb. (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:2 year:2021 number:2 day:06 month:02 https://dx.doi.org/10.1007/s42979-021-00473-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 2 2021 2 06 02 |
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Pandit, Pramit |
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comparative assessment of multiple linear regression and fuzzy linear regression models |
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Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models |
| abstract |
Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. |
| abstractGer |
Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. |
| abstract_unstemmed |
Abstract Prognosticating crop yield still remains as one of the challenging tasks in agriculture. Even though multiple linear regression methodology has dominated the area of predictive modelling, it is constrained to the assumption that the underlying relationship is assumed to be crisp or precise. Consequently, it often fails to provide satisfactory results when this assumption is violated in realistic situations. Fuzzy linear regression methodology is one of the promising and potential techniques to overcome this lacuna. Moreover, this fuzzy methodology can efficiently handle the problem of multicollinearity. In this paper, an attempt has been made to comparatively assess the efficiency of conventional regression models with their fuzzy counterparts using data on sweet corn yield (t/ha), total weed dry matter (g/$ m^{2} $) at 30 DAS and total weed density (no./$ m^{2} $) at 30 DAS. Model efficiency is computed in terms of average width of the prediction intervals. Efficiency of the models is also assessed in the presence of correlated explanatory variables. Outcomes emanated from the study clearly show the higher relative efficiency of fuzzy linear regression technique in comparison with the widely used simple and multiple linear regression techniques. This study also reveals that the fuzzy methodology has clear advantages over the conventional regression methodology in dealing with correlated explanatory variables. |
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Comparative Assessment of Multiple Linear Regression and Fuzzy Linear Regression Models |
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Dey, Prithwiraj Krishnamurthy, K. N. |
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10.1007/s42979-021-00473-3 |
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