Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data
Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-param...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kakamu, Kazuhiko [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2018 |
|---|
| Schlagwörter: |
Generalized beta (GB) distribution Generalized beta distribution of the second kind (GB2 distribution) Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm |
|---|
| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Computational economics - Springer US, 1993, 54(2018), 2 vom: 21. Aug., Seite 625-645 |
|---|---|
| Übergeordnetes Werk: |
volume:54 ; year:2018 ; number:2 ; day:21 ; month:08 ; pages:625-645 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10614-018-9843-4 |
|---|
| Katalog-ID: |
OLC2027003940 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2027003940 | ||
| 003 | DE-627 | ||
| 005 | 20230503034312.0 | ||
| 007 | tu | ||
| 008 | 200819s2018 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10614-018-9843-4 |2 doi | |
| 035 | |a (DE-627)OLC2027003940 | ||
| 035 | |a (DE-He213)s10614-018-9843-4-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 330 |a 650 |a 004 |q VZ |
| 084 | |a 3,2 |2 ssgn | ||
| 100 | 1 | |a Kakamu, Kazuhiko |e verfasserin |0 (orcid)0000-0003-1370-5520 |4 aut | |
| 245 | 1 | 0 | |a Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © Springer Science+Business Media, LLC, part of Springer Nature 2018 | ||
| 520 | |a Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions. | ||
| 650 | 4 | |a Dagum distribution | |
| 650 | 4 | |a Generalized beta (GB) distribution | |
| 650 | 4 | |a Generalized beta distribution of the second kind (GB2 distribution) | |
| 650 | 4 | |a Gini coefficient | |
| 650 | 4 | |a Grouped data | |
| 650 | 4 | |a Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm | |
| 700 | 1 | |a Nishino, Haruhisa |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Computational economics |d Springer US, 1993 |g 54(2018), 2 vom: 21. Aug., Seite 625-645 |w (DE-627)131178121 |w (DE-600)1142021-2 |w (DE-576)033040664 |x 0927-7099 |7 nnns |
| 773 | 1 | 8 | |g volume:54 |g year:2018 |g number:2 |g day:21 |g month:08 |g pages:625-645 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s10614-018-9843-4 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-WIW | ||
| 912 | |a GBV_ILN_26 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_4012 | ||
| 951 | |a AR | ||
| 952 | |d 54 |j 2018 |e 2 |b 21 |c 08 |h 625-645 | ||
| author_variant |
k k kk h n hn |
|---|---|
| matchkey_str |
article:09277099:2018----::aeinsiainfeayeitiuinaaees |
| hierarchy_sort_str |
2018 |
| publishDate |
2018 |
| allfields |
10.1007/s10614-018-9843-4 doi (DE-627)OLC2027003940 (DE-He213)s10614-018-9843-4-p DE-627 ger DE-627 rakwb eng 330 650 004 VZ 3,2 ssgn Kakamu, Kazuhiko verfasserin (orcid)0000-0003-1370-5520 aut Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions. Dagum distribution Generalized beta (GB) distribution Generalized beta distribution of the second kind (GB2 distribution) Gini coefficient Grouped data Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm Nishino, Haruhisa aut Enthalten in Computational economics Springer US, 1993 54(2018), 2 vom: 21. Aug., Seite 625-645 (DE-627)131178121 (DE-600)1142021-2 (DE-576)033040664 0927-7099 nnns volume:54 year:2018 number:2 day:21 month:08 pages:625-645 https://doi.org/10.1007/s10614-018-9843-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_26 GBV_ILN_70 GBV_ILN_4012 AR 54 2018 2 21 08 625-645 |
| spelling |
10.1007/s10614-018-9843-4 doi (DE-627)OLC2027003940 (DE-He213)s10614-018-9843-4-p DE-627 ger DE-627 rakwb eng 330 650 004 VZ 3,2 ssgn Kakamu, Kazuhiko verfasserin (orcid)0000-0003-1370-5520 aut Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions. Dagum distribution Generalized beta (GB) distribution Generalized beta distribution of the second kind (GB2 distribution) Gini coefficient Grouped data Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm Nishino, Haruhisa aut Enthalten in Computational economics Springer US, 1993 54(2018), 2 vom: 21. Aug., Seite 625-645 (DE-627)131178121 (DE-600)1142021-2 (DE-576)033040664 0927-7099 nnns volume:54 year:2018 number:2 day:21 month:08 pages:625-645 https://doi.org/10.1007/s10614-018-9843-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_26 GBV_ILN_70 GBV_ILN_4012 AR 54 2018 2 21 08 625-645 |
| allfields_unstemmed |
10.1007/s10614-018-9843-4 doi (DE-627)OLC2027003940 (DE-He213)s10614-018-9843-4-p DE-627 ger DE-627 rakwb eng 330 650 004 VZ 3,2 ssgn Kakamu, Kazuhiko verfasserin (orcid)0000-0003-1370-5520 aut Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions. Dagum distribution Generalized beta (GB) distribution Generalized beta distribution of the second kind (GB2 distribution) Gini coefficient Grouped data Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm Nishino, Haruhisa aut Enthalten in Computational economics Springer US, 1993 54(2018), 2 vom: 21. Aug., Seite 625-645 (DE-627)131178121 (DE-600)1142021-2 (DE-576)033040664 0927-7099 nnns volume:54 year:2018 number:2 day:21 month:08 pages:625-645 https://doi.org/10.1007/s10614-018-9843-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_26 GBV_ILN_70 GBV_ILN_4012 AR 54 2018 2 21 08 625-645 |
| allfieldsGer |
10.1007/s10614-018-9843-4 doi (DE-627)OLC2027003940 (DE-He213)s10614-018-9843-4-p DE-627 ger DE-627 rakwb eng 330 650 004 VZ 3,2 ssgn Kakamu, Kazuhiko verfasserin (orcid)0000-0003-1370-5520 aut Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions. Dagum distribution Generalized beta (GB) distribution Generalized beta distribution of the second kind (GB2 distribution) Gini coefficient Grouped data Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm Nishino, Haruhisa aut Enthalten in Computational economics Springer US, 1993 54(2018), 2 vom: 21. Aug., Seite 625-645 (DE-627)131178121 (DE-600)1142021-2 (DE-576)033040664 0927-7099 nnns volume:54 year:2018 number:2 day:21 month:08 pages:625-645 https://doi.org/10.1007/s10614-018-9843-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_26 GBV_ILN_70 GBV_ILN_4012 AR 54 2018 2 21 08 625-645 |
| allfieldsSound |
10.1007/s10614-018-9843-4 doi (DE-627)OLC2027003940 (DE-He213)s10614-018-9843-4-p DE-627 ger DE-627 rakwb eng 330 650 004 VZ 3,2 ssgn Kakamu, Kazuhiko verfasserin (orcid)0000-0003-1370-5520 aut Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions. Dagum distribution Generalized beta (GB) distribution Generalized beta distribution of the second kind (GB2 distribution) Gini coefficient Grouped data Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm Nishino, Haruhisa aut Enthalten in Computational economics Springer US, 1993 54(2018), 2 vom: 21. Aug., Seite 625-645 (DE-627)131178121 (DE-600)1142021-2 (DE-576)033040664 0927-7099 nnns volume:54 year:2018 number:2 day:21 month:08 pages:625-645 https://doi.org/10.1007/s10614-018-9843-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_26 GBV_ILN_70 GBV_ILN_4012 AR 54 2018 2 21 08 625-645 |
| language |
English |
| source |
Enthalten in Computational economics 54(2018), 2 vom: 21. Aug., Seite 625-645 volume:54 year:2018 number:2 day:21 month:08 pages:625-645 |
| sourceStr |
Enthalten in Computational economics 54(2018), 2 vom: 21. Aug., Seite 625-645 volume:54 year:2018 number:2 day:21 month:08 pages:625-645 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Dagum distribution Generalized beta (GB) distribution Generalized beta distribution of the second kind (GB2 distribution) Gini coefficient Grouped data Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm |
| dewey-raw |
330 |
| isfreeaccess_bool |
false |
| container_title |
Computational economics |
| authorswithroles_txt_mv |
Kakamu, Kazuhiko @@aut@@ Nishino, Haruhisa @@aut@@ |
| publishDateDaySort_date |
2018-08-21T00:00:00Z |
| hierarchy_top_id |
131178121 |
| dewey-sort |
3330 |
| id |
OLC2027003940 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2027003940</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503034312.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10614-018-9843-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2027003940</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10614-018-9843-4-p</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">330</subfield><subfield code="a">650</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kakamu, Kazuhiko</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1370-5520</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dagum distribution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generalized beta (GB) distribution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generalized beta distribution of the second kind (GB2 distribution)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gini coefficient</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Grouped data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nishino, Haruhisa</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational economics</subfield><subfield code="d">Springer US, 1993</subfield><subfield code="g">54(2018), 2 vom: 21. Aug., Seite 625-645</subfield><subfield code="w">(DE-627)131178121</subfield><subfield code="w">(DE-600)1142021-2</subfield><subfield code="w">(DE-576)033040664</subfield><subfield code="x">0927-7099</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:54</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:2</subfield><subfield code="g">day:21</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:625-645</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10614-018-9843-4</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">54</subfield><subfield code="j">2018</subfield><subfield code="e">2</subfield><subfield code="b">21</subfield><subfield code="c">08</subfield><subfield code="h">625-645</subfield></datafield></record></collection>
|
| author |
Kakamu, Kazuhiko |
| spellingShingle |
Kakamu, Kazuhiko ddc 330 ssgn 3,2 misc Dagum distribution misc Generalized beta (GB) distribution misc Generalized beta distribution of the second kind (GB2 distribution) misc Gini coefficient misc Grouped data misc Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data |
| authorStr |
Kakamu, Kazuhiko |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)131178121 |
| format |
Article |
| dewey-ones |
330 - Economics 650 - Management & auxiliary services 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0927-7099 |
| topic_title |
330 650 004 VZ 3,2 ssgn Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data Dagum distribution Generalized beta (GB) distribution Generalized beta distribution of the second kind (GB2 distribution) Gini coefficient Grouped data Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm |
| topic |
ddc 330 ssgn 3,2 misc Dagum distribution misc Generalized beta (GB) distribution misc Generalized beta distribution of the second kind (GB2 distribution) misc Gini coefficient misc Grouped data misc Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm |
| topic_unstemmed |
ddc 330 ssgn 3,2 misc Dagum distribution misc Generalized beta (GB) distribution misc Generalized beta distribution of the second kind (GB2 distribution) misc Gini coefficient misc Grouped data misc Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm |
| topic_browse |
ddc 330 ssgn 3,2 misc Dagum distribution misc Generalized beta (GB) distribution misc Generalized beta distribution of the second kind (GB2 distribution) misc Gini coefficient misc Grouped data misc Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Computational economics |
| hierarchy_parent_id |
131178121 |
| dewey-tens |
330 - Economics 650 - Management & public relations 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Computational economics |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)131178121 (DE-600)1142021-2 (DE-576)033040664 |
| title |
Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data |
| ctrlnum |
(DE-627)OLC2027003940 (DE-He213)s10614-018-9843-4-p |
| title_full |
Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data |
| author_sort |
Kakamu, Kazuhiko |
| journal |
Computational economics |
| journalStr |
Computational economics |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
300 - Social sciences 600 - Technology 000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2018 |
| contenttype_str_mv |
txt |
| container_start_page |
625 |
| author_browse |
Kakamu, Kazuhiko Nishino, Haruhisa |
| container_volume |
54 |
| class |
330 650 004 VZ 3,2 ssgn |
| format_se |
Aufsätze |
| author-letter |
Kakamu, Kazuhiko |
| doi_str_mv |
10.1007/s10614-018-9843-4 |
| normlink |
(ORCID)0000-0003-1370-5520 |
| normlink_prefix_str_mv |
(orcid)0000-0003-1370-5520 |
| dewey-full |
330 650 004 |
| title_sort |
bayesian estimation of beta-type distribution parameters based on grouped data |
| title_auth |
Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data |
| abstract |
Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-WIW GBV_ILN_26 GBV_ILN_70 GBV_ILN_4012 |
| container_issue |
2 |
| title_short |
Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data |
| url |
https://doi.org/10.1007/s10614-018-9843-4 |
| remote_bool |
false |
| author2 |
Nishino, Haruhisa |
| author2Str |
Nishino, Haruhisa |
| ppnlink |
131178121 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10614-018-9843-4 |
| up_date |
2024-07-03T13:21:59.480Z |
| _version_ |
1803564267055611904 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2027003940</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503034312.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2018 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10614-018-9843-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2027003940</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10614-018-9843-4-p</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">330</subfield><subfield code="a">650</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kakamu, Kazuhiko</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1370-5520</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bayesian Estimation of Beta-type Distribution Parameters Based on Grouped Data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC, part of Springer Nature 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This study considers a method of estimating generalized beta (GB) distribution parameters based on grouped data from a Bayesian point of view and explores the possibility of the GB distribution focusing on the goodness-of-fit because the GB distribution is one of the most typical five-parameter distributions. It uses a tailored randomized block Metropolis–Hastings (TaRBMH) algorithm to estimate the GB distribution parameters and this method is then applied to one simulated and two real datasets. Moreover, the fit of the GB distribution is compared with those of the generalized beta distribution of the second kind (GB2 distribution) and Dagum (DA) distribution by using the marginal likelihood. The estimation results of simulated and real datasets show that the GB distributions are preferred to the DA distributions in general, while the GB2 distributions have similar performances to the GB distributions. In other words, the GB2 distribution could be adopted as well as the GB distribution in terms of the smallest possible number of parameters, although our TaRBMH algorithm can estimate the GB distribution parameters efficiently and accurately. The accuracy of the Gini coefficients also suggests the use of the GB2 distributions.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dagum distribution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generalized beta (GB) distribution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Generalized beta distribution of the second kind (GB2 distribution)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gini coefficient</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Grouped data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tailored randomized block Metropolis–Hastings (TaRBMH) algorithm</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nishino, Haruhisa</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Computational economics</subfield><subfield code="d">Springer US, 1993</subfield><subfield code="g">54(2018), 2 vom: 21. Aug., Seite 625-645</subfield><subfield code="w">(DE-627)131178121</subfield><subfield code="w">(DE-600)1142021-2</subfield><subfield code="w">(DE-576)033040664</subfield><subfield code="x">0927-7099</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:54</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:2</subfield><subfield code="g">day:21</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:625-645</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10614-018-9843-4</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">54</subfield><subfield code="j">2018</subfield><subfield code="e">2</subfield><subfield code="b">21</subfield><subfield code="c">08</subfield><subfield code="h">625-645</subfield></datafield></record></collection>
|
| score |
7.402135 |