Zobrazeno 1 - 10
of 43
pro vyhledávání: '"John I. Marden"'
Autor:
Kyungmee Choi, John I. Marden
Publikováno v:
Journal of Nonparametric Statistics. 17:167-183
Kendall’s τ for univariate samples is extended to the multivariate case using unit direction vectors in place of signs. Depending on whether the design matrix is fixed, this statistic yields a test for the multivariate analysis of variance or mult
Autor:
Kyungmee Choi, John I. Marden
Publikováno v:
Sociological Methods & Research. 30:341-366
The authors consider multivariate analysis of variance procedures based on the multivariate spatial ranks. Two models are considered: the location-family model and the fully nonparametric model. Procedures for testing main and interaction effects are
Autor:
John I. Marden
Publikováno v:
Encyclopedia of Statistics in Behavioral Science
In a paired comparison experiment, several judges compare several objects. Each judge performs a number of paired comparisons, that is, considers a pair of the objects, and indicates which of the two is preferred. The Bradley–Terry model is a simpl
Autor:
Yonghong Gao, John I. Marden
Publikováno v:
Algebraic Methods in Statistics and Probability. :97-109
Autor:
John I. Marden
Publikováno v:
Journal of the American Statistical Association. 95:1316-1320
(2000). Hypothesis Testing: From p Values to Bayes Factors. Journal of the American Statistical Association: Vol. 95, No. 452, pp. 1316-1320.
Autor:
N. Locantore, J. S. Marron, D. G. Simpson, N. Tripoli, J. T. Zhang, K. L. Cohen, Graciela Boente, Ricardo Fraiman, Babette Brumback, Christophe Croux, Jianqing Fan, Alois Kneip, John I. Marden, Daniel Peña, Javier Prieto, Jim O. Ramsay, Mariano J. Valderrama, Ana M. Aguilera
Publikováno v:
Test. 8:1-73
A method for exploring the structure of populations of complex objects, such as images, is considered. The objects are summarized by feature vectors. The statistical backbone is Principal Component Analysis in the space of feature vectors. Visual ins
Publikováno v:
Journal of Educational Measurement. 35:1-30
A new approach for partitioning test items into dimensionally distinct item clusters is introduced. The core of the approach is a new item-pair conditional-covariancebased proximity measure that can be used with hierarchical cluster analysis. An exte