Fuzzy multiple regressions for Cross-Section and Panel data

A classical multiple regression is a framework defined a priori verifying several limiting assumptions and easily misused. We propose a fuzzy alternative approach to classical multiple regressions for cross-sectional and panel data. As illustration, we estimate and analyze the effect of the annual G...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Belhadj, Besma [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Fuzzy endogenous regressor Fuzzy parameters Fuzzy mathematical modeling Cross-sectional data Panel data

Übergeordnetes Werk:	Enthalten in: Socio-economic planning sciences - Amsterdam [u.a.] : Elsevier Science, 1967, 91
Übergeordnetes Werk:	volume:91

DOI / URN:	10.1016/j.seps.2023.101761

Katalog-ID:	ELV066570786

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spelling	10.1016/j.seps.2023.101761 doi (DE-627)ELV066570786 (ELSEVIER)S0038-0121(23)00273-2 DE-627 ger DE-627 rda eng 300 330 VZ 83.00 bkl Belhadj, Besma verfasserin aut Fuzzy multiple regressions for Cross-Section and Panel data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A classical multiple regression is a framework defined a priori verifying several limiting assumptions and easily misused. We propose a fuzzy alternative approach to classical multiple regressions for cross-sectional and panel data. As illustration, we estimate and analyze the effect of the annual GDP growth rate, unemployment rate, inflation rate and annual population growth rate on poverty in the Middle East and North Africa (MENA) region. Fuzzy endogenous regressor Fuzzy parameters Fuzzy mathematical modeling Cross-sectional data Panel data Enthalten in Socio-economic planning sciences Amsterdam [u.a.] : Elsevier Science, 1967 91 Online-Ressource (DE-627)302466495 (DE-600)1491145-0 (DE-576)079719449 0038-0121 nnns volume:91 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 83.00 Volkswirtschaft: Allgemeines VZ AR 91
allfields_unstemmed	10.1016/j.seps.2023.101761 doi (DE-627)ELV066570786 (ELSEVIER)S0038-0121(23)00273-2 DE-627 ger DE-627 rda eng 300 330 VZ 83.00 bkl Belhadj, Besma verfasserin aut Fuzzy multiple regressions for Cross-Section and Panel data 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier A classical multiple regression is a framework defined a priori verifying several limiting assumptions and easily misused. We propose a fuzzy alternative approach to classical multiple regressions for cross-sectional and panel data. As illustration, we estimate and analyze the effect of the annual GDP growth rate, unemployment rate, inflation rate and annual population growth rate on poverty in the Middle East and North Africa (MENA) region. Fuzzy endogenous regressor Fuzzy parameters Fuzzy mathematical modeling Cross-sectional data Panel data Enthalten in Socio-economic planning sciences Amsterdam [u.a.] : Elsevier Science, 1967 91 Online-Ressource (DE-627)302466495 (DE-600)1491145-0 (DE-576)079719449 0038-0121 nnns volume:91 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 83.00 Volkswirtschaft: Allgemeines VZ AR 91
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author	Belhadj, Besma
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topic_title	300 330 VZ 83.00 bkl Fuzzy multiple regressions for Cross-Section and Panel data Fuzzy endogenous regressor Fuzzy parameters Fuzzy mathematical modeling Cross-sectional data Panel data
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title	Fuzzy multiple regressions for Cross-Section and Panel data
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title_full	Fuzzy multiple regressions for Cross-Section and Panel data
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title_sort	fuzzy multiple regressions for cross-section and panel data
title_auth	Fuzzy multiple regressions for Cross-Section and Panel data
abstract	A classical multiple regression is a framework defined a priori verifying several limiting assumptions and easily misused. We propose a fuzzy alternative approach to classical multiple regressions for cross-sectional and panel data. As illustration, we estimate and analyze the effect of the annual GDP growth rate, unemployment rate, inflation rate and annual population growth rate on poverty in the Middle East and North Africa (MENA) region.
abstractGer	A classical multiple regression is a framework defined a priori verifying several limiting assumptions and easily misused. We propose a fuzzy alternative approach to classical multiple regressions for cross-sectional and panel data. As illustration, we estimate and analyze the effect of the annual GDP growth rate, unemployment rate, inflation rate and annual population growth rate on poverty in the Middle East and North Africa (MENA) region.
abstract_unstemmed	A classical multiple regression is a framework defined a priori verifying several limiting assumptions and easily misused. We propose a fuzzy alternative approach to classical multiple regressions for cross-sectional and panel data. As illustration, we estimate and analyze the effect of the annual GDP growth rate, unemployment rate, inflation rate and annual population growth rate on poverty in the Middle East and North Africa (MENA) region.
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title_short	Fuzzy multiple regressions for Cross-Section and Panel data
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Fuzzy multiple regressions for Cross-Section and Panel data

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