Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1179-1187 |
| Seitenumfang | 9 |
| Fachzeitschrift | Journal of Statistical Computation and Simulation |
| Jahrgang | 83 |
| Ausgabenummer | 6 |
| Frühes Online-Datum | 30 Jan. 2012 |
| Publikationsstatus | Veröffentlicht - 2013 |
Abstract
The geographical location and the monsoon climate render Bangladesh highly vulnerable to natural hazards, deteriorating the country's socio-economic stability. This study is based on 500 randomly chosen rural households from the Household Income and Expenditure Survey [Bangladesh Bureau of Statistics, Planning Division, Ministry of Planning, Government of the People's Republic of Bangladesh, Dhaka, 2006]. The objectives are to estimate the income vulnerability of rural households and to check whether the Bayesian approaches (natural conjugate prior and non-informative prior estimates) have any superiority over the classical (feasible generalized least square (FGLS)) method. The poverty level, measured from the data, is 24%; whereas the vulnerability estimates, using FGLS, natural conjugate prior and non-informative prior are 31%, 69% and 82%, respectively. Vulnerability estimates by the Bayesian natural conjugate prior approach is found to have greater efficiency compared with FGLS and non-informative prior approaches.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Statistik und Wahrscheinlichkeit
- Mathematik (insg.)
- Modellierung und Simulation
- Entscheidungswissenschaften (insg.)
- Statistik, Wahrscheinlichkeit und Ungewissheit
- Mathematik (insg.)
- Angewandte Mathematik
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in: Journal of Statistical Computation and Simulation, Jahrgang 83, Nr. 6, 2013, S. 1179-1187.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Income vulnerability of rural households in Bangladesh
T2 - a comparison between Bayesian and classical methods
AU - Islam, Md Ershadul
AU - Grote, Ulrike
AU - Rayhan, Md Israt
PY - 2013
Y1 - 2013
N2 - The geographical location and the monsoon climate render Bangladesh highly vulnerable to natural hazards, deteriorating the country's socio-economic stability. This study is based on 500 randomly chosen rural households from the Household Income and Expenditure Survey [Bangladesh Bureau of Statistics, Planning Division, Ministry of Planning, Government of the People's Republic of Bangladesh, Dhaka, 2006]. The objectives are to estimate the income vulnerability of rural households and to check whether the Bayesian approaches (natural conjugate prior and non-informative prior estimates) have any superiority over the classical (feasible generalized least square (FGLS)) method. The poverty level, measured from the data, is 24%; whereas the vulnerability estimates, using FGLS, natural conjugate prior and non-informative prior are 31%, 69% and 82%, respectively. Vulnerability estimates by the Bayesian natural conjugate prior approach is found to have greater efficiency compared with FGLS and non-informative prior approaches.
AB - The geographical location and the monsoon climate render Bangladesh highly vulnerable to natural hazards, deteriorating the country's socio-economic stability. This study is based on 500 randomly chosen rural households from the Household Income and Expenditure Survey [Bangladesh Bureau of Statistics, Planning Division, Ministry of Planning, Government of the People's Republic of Bangladesh, Dhaka, 2006]. The objectives are to estimate the income vulnerability of rural households and to check whether the Bayesian approaches (natural conjugate prior and non-informative prior estimates) have any superiority over the classical (feasible generalized least square (FGLS)) method. The poverty level, measured from the data, is 24%; whereas the vulnerability estimates, using FGLS, natural conjugate prior and non-informative prior are 31%, 69% and 82%, respectively. Vulnerability estimates by the Bayesian natural conjugate prior approach is found to have greater efficiency compared with FGLS and non-informative prior approaches.
KW - Bangladesh
KW - feasible generalized least square
KW - natural conjugate prior
KW - non-informative prior estimate
KW - vulnerability
UR - http://www.scopus.com/inward/record.url?scp=84878982489&partnerID=8YFLogxK
U2 - 10.1080/00949655.2012.656310
DO - 10.1080/00949655.2012.656310
M3 - Article
AN - SCOPUS:84878982489
VL - 83
SP - 1179
EP - 1187
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
SN - 0094-9655
IS - 6
ER -