Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution

Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper ta...
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Autor*in:	Băncescu, Irina [verfasserIn] Chivu, Luminiţa [verfasserIn] Preda, Vasile [verfasserIn] Puente-Ajovín, Miguel [verfasserIn] Ramos, Arturo [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Mixture of log-normal models Pareto upper and lower tails GB2 distribution City-size distribution

Übergeordnetes Werk:	Enthalten in: Physica / A - Amsterdam : North Holland Publ. Co., 1975, 526
Übergeordnetes Werk:	volume:526

DOI / URN:	10.1016/j.physa.2019.04.253

Katalog-ID:	ELV002470616

Internformat


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520			\|a Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data.
650		4	\|a Mixture of log-normal models
650		4	\|a Pareto upper and lower tails
650		4	\|a GB2 distribution
650		4	\|a City-size distribution
700	1		\|a Chivu, Luminiţa \|e verfasserin \|4 aut
700	1		\|a Preda, Vasile \|e verfasserin \|4 aut
700	1		\|a Puente-Ajovín, Miguel \|e verfasserin \|4 aut
700	1		\|a Ramos, Arturo \|e verfasserin \|4 aut
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912			\|a GBV_ILN_2027
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912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 33.25 \|j Thermodynamik \|j statistische Physik
936	b	k	\|a 31.00 \|j Mathematik: Allgemeines
951			\|a AR
952			\|d 526

Indexfelder

author_variant	i b ib l c lc v p vp m p a mpa a r ar
matchkey_str	article:18732119:2019----::oprsnolgomlitradaeoalg2ronrabdormnaa
hierarchy_sort_str	2019
bklnumber	33.25 31.00
publishDate	2019
allfields	10.1016/j.physa.2019.04.253 doi (DE-627)ELV002470616 (ELSEVIER)S0378-4371(19)30627-2 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Băncescu, Irina verfasserin aut Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data. Mixture of log-normal models Pareto upper and lower tails GB2 distribution City-size distribution Chivu, Luminiţa verfasserin aut Preda, Vasile verfasserin aut Puente-Ajovín, Miguel verfasserin aut Ramos, Arturo verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 526 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:526 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 526
spelling	10.1016/j.physa.2019.04.253 doi (DE-627)ELV002470616 (ELSEVIER)S0378-4371(19)30627-2 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Băncescu, Irina verfasserin aut Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data. Mixture of log-normal models Pareto upper and lower tails GB2 distribution City-size distribution Chivu, Luminiţa verfasserin aut Preda, Vasile verfasserin aut Puente-Ajovín, Miguel verfasserin aut Ramos, Arturo verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 526 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:526 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 526
allfields_unstemmed	10.1016/j.physa.2019.04.253 doi (DE-627)ELV002470616 (ELSEVIER)S0378-4371(19)30627-2 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Băncescu, Irina verfasserin aut Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data. Mixture of log-normal models Pareto upper and lower tails GB2 distribution City-size distribution Chivu, Luminiţa verfasserin aut Preda, Vasile verfasserin aut Puente-Ajovín, Miguel verfasserin aut Ramos, Arturo verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 526 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:526 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 526
allfieldsGer	10.1016/j.physa.2019.04.253 doi (DE-627)ELV002470616 (ELSEVIER)S0378-4371(19)30627-2 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Băncescu, Irina verfasserin aut Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data. Mixture of log-normal models Pareto upper and lower tails GB2 distribution City-size distribution Chivu, Luminiţa verfasserin aut Preda, Vasile verfasserin aut Puente-Ajovín, Miguel verfasserin aut Ramos, Arturo verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 526 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:526 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 526
allfieldsSound	10.1016/j.physa.2019.04.253 doi (DE-627)ELV002470616 (ELSEVIER)S0378-4371(19)30627-2 DE-627 ger DE-627 rda eng 500 DE-600 33.25 bkl 31.00 bkl Băncescu, Irina verfasserin aut Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data. Mixture of log-normal models Pareto upper and lower tails GB2 distribution City-size distribution Chivu, Luminiţa verfasserin aut Preda, Vasile verfasserin aut Puente-Ajovín, Miguel verfasserin aut Ramos, Arturo verfasserin aut Enthalten in Physica / A Amsterdam : North Holland Publ. Co., 1975 526 Online-Ressource (DE-627)266015077 (DE-600)1466577-3 (DE-576)074959832 1873-2119 nnns volume:526 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-MAT 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_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_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 33.25 Thermodynamik statistische Physik 31.00 Mathematik: Allgemeines AR 526
language	English
source	Enthalten in Physica / A 526 volume:526
sourceStr	Enthalten in Physica / A 526 volume:526
format_phy_str_mv	Article
bklname	Thermodynamik statistische Physik Mathematik: Allgemeines
institution	findex.gbv.de
topic_facet	Mixture of log-normal models Pareto upper and lower tails GB2 distribution City-size distribution
dewey-raw	500
isfreeaccess_bool	false
container_title	Physica / A
authorswithroles_txt_mv	Băncescu, Irina @@aut@@ Chivu, Luminiţa @@aut@@ Preda, Vasile @@aut@@ Puente-Ajovín, Miguel @@aut@@ Ramos, Arturo @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	266015077
dewey-sort	3500
id	ELV002470616
language_de	englisch
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author	Băncescu, Irina
spellingShingle	Băncescu, Irina ddc 500 bkl 33.25 bkl 31.00 misc Mixture of log-normal models misc Pareto upper and lower tails misc GB2 distribution misc City-size distribution Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution
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topic_title	500 DE-600 33.25 bkl 31.00 bkl Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution Mixture of log-normal models Pareto upper and lower tails GB2 distribution City-size distribution
topic	ddc 500 bkl 33.25 bkl 31.00 misc Mixture of log-normal models misc Pareto upper and lower tails misc GB2 distribution misc City-size distribution
topic_unstemmed	ddc 500 bkl 33.25 bkl 31.00 misc Mixture of log-normal models misc Pareto upper and lower tails misc GB2 distribution misc City-size distribution
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title	Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution
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title_full	Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution
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author_browse	Băncescu, Irina Chivu, Luminiţa Preda, Vasile Puente-Ajovín, Miguel Ramos, Arturo
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author-letter	Băncescu, Irina
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title_sort	comparisons of log-normal mixture and pareto tails, gb2 or log-normal body of romania’s all cities size distribution
title_auth	Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution
abstract	Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data.
abstractGer	Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data.
abstract_unstemmed	Modeling demographic data has been on the agenda of statisticians for many years. Some of the distributions used are Pareto, reverse Pareto, q -exponential and log-normal models. An approach to this problem is to consider three statistical models: one for the upper tail, one for the middle range, and another for the lower tail. This paper deals with the size distribution of urban and rural agglomerations in Romania for the 1992–2017 period, by comparing the recently introduced three log-normal mixture (3LN), Pareto tails log-normal (PTLN), and threshold double Pareto Generalized Beta of second kind (tdPGB2) models. The tdPGB2 statistical model has the PTLN distribution as a limiting case. The maximum likelihood estimates of the distributions are computed, and goodness-of-fit tests are performed using the Kolmogorov–Smirnov (KS), Cramér–von Mises (CM) and Anderson–Darling (AD) statistics. Also, we use the Vuong and Bayes factor log-likelihood tests. Using both graphical and formal statistical tests, our results rigorously confirm that the 3LN model is statistically equivalent to PTLN and tdPGB2 distributions, the preferred model being the PTLN probability law. Both the PTLN and tdPGB2 distributions have Pareto tails but the 3LN model does not. All the three models prove to be very well suited parameterizations of Romania’s city size data.
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title_short	Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution
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author2	Chivu, Luminiţa Preda, Vasile Puente-Ajovín, Miguel Ramos, Arturo
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Comparisons of log-normal mixture and Pareto tails, GB2 or log-normal body of Romania’s all cities size distribution

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