Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Julie Gershunskaya"'
Autor:
Julie Gershunskaya
Publikováno v:
Statistics in Transition New Series. 21:23-29
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d95c6e1b993ca462be519bdb11fee7c
https://doi.org/10.21307/stattrans-2020-023
https://doi.org/10.21307/stattrans-2020-023
Publikováno v:
Journal of Survey Statistics and Methodology. 8:181-205
Small domain estimation models, like the Fay-Herriot (FH), often assume a normally distributed latent process centered on a linear mean function. The linearity assumption may be violated for domains that express idiosyncratic phenomena not captured b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98a0a72264cac21c135c7041b21df527
https://doi.org/10.1093/jssam/smz004
https://doi.org/10.1093/jssam/smz004
Autor:
Julie Gershunskaya, Partha Lahiri
Publikováno v:
Calcutta Statistical Association Bulletin. 69:183-204
We propose a new robust empirical best estimation approach to estimate small area finite population means that are relatively insensitive to a model misspecification or to the presence of outliers. This important robustness property is achieved by re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df6a2fc77c445294e4e39df3faa3301d
https://doi.org/10.1177/0008068317722297
https://doi.org/10.1177/0008068317722297
Publikováno v:
Journal of Survey Statistics and Methodology. 5:433-453
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c13bf0d7d8af25b3ef4c3f730941589
https://doi.org/10.1093/jssam/smx021
https://doi.org/10.1093/jssam/smx021
Autor:
Julie Gershunskaya
Publikováno v:
Statistics in Transition, Vol 21, Iss 4 (2020)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::ac7b664c87340b054496c261524b5615
https://doi.org/10.21307/stattrans-2020-023
https://doi.org/10.21307/stattrans-2020-023
Publikováno v:
Journal of Official Statistics, Vol 30, Iss 4, Pp 787-810 (2014)
Large-scale establishment surveys often exhibit substantial temporal or cross-sectional variability in their published standard errors. This article uses a framework defined by survey generalized variance functions to develop three sets of analytic t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a678b9a30e1e9b30535119a9f6db9988
https://doi.org/10.2478/jos-2014-0048
https://doi.org/10.2478/jos-2014-0048
Publikováno v:
Journal of Official Statistics, Vol 30, Iss 1, Pp 63-90 (2014)
Two sets of diagnostics are presented to evaluate the properties of generalized variance functions (GVFs) for a given sample survey. The first set uses test statistics for the coefficients of multiple regression forms of GVF models. The second set us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c54f0a5122a17200a69c54d08a9ec62a
https://doaj.org/article/710a989576484fcc900ecbcdea4e2c3f
https://doaj.org/article/710a989576484fcc900ecbcdea4e2c3f