Browsing by Author "Moreira, Elsa"
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- Balanced prime basis factorial fixed effects model with random number of observationsPublication . Oliveira, Sandra; Nunes, Célia; Moreira, Elsa; Fonseca, Miguel; Mexia, João T.Factorial designs are in general more efficient for experiments that involve the study of the effects of two or more factors. In this paper we consider a p^U factorial model with U factors, each one having a p prime number of levels. We consider a balanced (r replicates per treatment) prime factorial with fixed effects. Our goal is to extend these models to the case where it is not possible to known in advance the number of treatments replicates, r. In these situations is more appropriate to consider r as a realization of a random variable R, which will be assumed to be geometrically distributed. The proposed approach is illustrated through an application considering simulated data.
- Estimation and Orthogonal Block StructurePublication . Ferreira, Sandra S.; Nunes, Célia; Ferreira, Dário; Moreira, Elsa; Mexia, João T.Estimators with good behaviors for estimable vectors and variance components are obtained for a class of models that contains the well known models with orthogonal block structure, OBS, see [15], [16] and [1], [2]. The study observations of these estimators uses commutative Jordan Algebras, CJA, and extends the one given for a more restricted class of models, the models with commutative orthogonal block structure, COBS, in which the orthogonal projection matrix on the space spanned by the means vector commute with all variance-covariance matrices, see [7].
- Exact Estimators for Normal Linear Mixed ModelsPublication . Ferreira, Sandra S.; Ferreira, Dário; Moreira, Elsa; Mexia, João T.
- Orthogonal fixed effects ANOVA with random sample sizesPublication . Mexia, J. T.; Nunes, Célia; Ferreira, Dário; Ferreira, Sandra S.; Moreira, ElsaIn many relevant situations, such as in medical research, sample sizes may not be previously known. We extend ANOVA to those situations starting with one-way and then the general orthogonal situation. Sample sizes will be assumed to be random.