In the recent years, there has been a growing interest in proposing covariance models for multivariate Gaussian random fields. Some of these covariance models are very flexible and can capture both ...

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Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variables under consideration. Multivariate analysis techniques may be used for several ...

To study the genetic and environmental determination of variation in Heath–Carter somatotype (ST) components (endomorphy, mesomorphy and ectomorphy). The results indicate the significance of sex ...

Traditional methods for covariate adjustment of treatment means in designed experiments are inherently conditional on the observed covariate values. In order to develop a coherent general methodology ...

Bojinov, Iavor I., Natesh S. Pillai, and Donald B. Rubin. "Diagnosing Missing Always at Random in Multivariate Data." Biometrika 107, no. 1 (March 2020): 246–253.

In semiconductor manufacturing, especially in electrical test data, but also in other parameters, there are often sets of parameters that are very highly correlated. Even a change in the correlation ...

Four poor prognostic factors were identified and a simple prognostic index was devised. Information from this analysis can be used to aid clinical decision-making, help individual patient risk ...

Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variable under consideration. Multivariate analysis techniques may be used for several ...