Multivariate Regression with Unequal Number of Observations

Autor: Sentana, Enrique

Publikováno v: Econometric Theory, 1997 Aug 01. 13(4), 613-614.

Externí odkaz: https://www.jstor.org/stable/3532719

Further Monte Carlo Evidence on Seemingly Unrelated Regressions with Unequal Number of Observations

Autor: Baltagi, Badi H., Garvin, Susan, Kerman, Stephen

Publikováno v: Annales d'Économie et de Statistique, 1989 Apr 01(14), 103-115.

Externí odkaz: https://www.jstor.org/stable/20075741

Adjusting the Scott-Knott cluster analyses for unbalanced designs

Autor: Thiago Vincenzi Conrado, Daniel Furtado Ferreira, Carlos Alberto Scapim, Wilson Roberto Maluf

Publikováno v: Crop Breeding and Applied Biotechnology, Vol 17, Iss 1, Pp 1-9 (2016)

The Scott-Knott cluster analysis is an alternative approach to mean comparisons with high power and no subset overlapping. It is well suited for the statistical challenges in agronomy associated with testing new cultivars, crop treatments, or methods

Externí odkaz: https://doaj.org/article/c8c93f82fc26489a9eb72c635bb421fa

Further Monte Carlo Evidence on Seemingly Unrelated Regressions with Unequal Number of Observations

Autor: Badi H. BALTAGI, Susan GARVIN, Stephen KERMAN

Publikováno v: Annals of Economics and Statistics. (14):103-115

Schmidt's [1977] results on seemingly unrelated regressions with unequal number of observations are replicated. These results are shown to be robust to the type of addtional observations available i. e., whether they are time series or cross-sectiona

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od_______645::c08aa5b68eeffade3436326d93b90db7
http://www.jstor.org/stable/20075741

Adjusting the Scott-Knott cluster analyses for unbalanced designs

Autor: Wilson Roberto Maluf, Daniel Furtado Ferreira, Thiago Vincenzi Conrado, Carlos Alberto Scapim

Publikováno v: Crop Breeding and Applied Biotechnology, Volume: 17, Issue: 1, Pages: 1-9, Published: MAR 2017
Crop Breeding and Applied Biotechnology, Vol 17, Iss 1, Pp 1-9 (2016)
Crop Breeding and Applied Biotechnology v.17 n.1 2017
Crop Breeding and Applied Biotechnology
Sociedade Brasileira de Melhoramento de Plantas
instacron:CBAB

The Scott-Knott cluster analysis is an alternative approach to mean comparisons with high power and no subset overlapping. It is well suited for the statistical challenges in agronomy associated with testing new cultivars, crop treatments, or methods

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0ec8e1aa991cbd9b863bcad2b54f0f0
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1984-70332017000100001&lng=en&tlng=en