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- Crossing balanced and stair nested desingsPublication . Fernandes, Célia; Ramos, Paulo; Mexia, João TiagoBalanced nesting is the most usual form of nesting and originates, when used singly or with crossing of such sub-models, orthogonal models. In balanced nesting we are forced to divide repeatedly the plots and we have few degrees of freedom for the first levels. If we apply stair nesting we will have plots all of the same size rendering the designs easier to apply. The stair nested designs are a valid alternative for the balanced nested designs because we can work with fewer observations, the amount of information for the different factors is more evenly distributed and we obtain good results. The inference for models with balanced nesting is already well studied. For models with stair nesting it is easy to carry out inference because it is very similar to that for balanced nesting. Furthermore stair nested designs being unbalanced have an orthogonal structure. Other alternative to the balanced nesting is the staggered nesting that is the most popular unbalanced nested design which also has the advantage of requiring fewer observations. However staggered nested designs are not orthogonal, unlike the stair nested designs. In this work we start with the algebraic structure of the balanced, the stair and the staggered nested designs and we finish with the structure of the cross between balanced and stair nested designs.
- Cobs and stair nesting – segregation and crossingPublication . Fernandes, Célia; Ramos, Paulo; Mexia, JoãoStair nesting leads to very light models since the number of their treatments is additive on the numbers of observations in which only the level of one factor various. These groups of observations will be the steps of the design. In stair nested designs we work with fewer observations when compared with balanced nested designs and the amount of information for the different factors is more evenly distributed. We now integrate these models into a special class of models with orthogonal block structure for which there are interesting properties.
- Algebraic structure for the crossing of balanced and stair nested designsPublication . Fernandes, Célia; Ramos, Paulo; Mexia, João TiagoStair nesting allows us to work with fewer observations than the most usual form of nesting, the balanced nesting. In the case of stair nesting the amount of information for the different factors is more evenly distributed. This new design leads to greater economy, because we can work with fewer observations. In this work we present the algebraic structure of the cross of balanced nested and stair nested designs, using binary operations on commutative Jordan algebras. This new cross requires fewer observations than the usual cross balanced nested designs and it is easy to carry out inference.
- Algebraic structure for interaction on mixed modelsPublication . Ramos, Paulo; Fernandes, Célia; Mexia, João TiagoBinary operations on commutative Jordan algebras, CJA, can be used to study interactions between sets of factors belonging to a pair of models in which one nests the other. It should be noted that from two CJA we can, through these binary operations, build CJA. So when we nest the treatments from one model in each treatment of another model, we can study the interactions between sets of factors of the first and the second models.
- Sparse designs for estimating variance components of nested factors with random effectsPublication . Bailey, R. A.; Fernandes, Célia; Ramos, PauloA new class of designs is introduced for both estimating the variance components of nested factors and testing hypotheses about those variance components. These designs are flexible, and can be chosen so that the degrees of freedom are more evenly spread among the factors than they are in balanced nested designs. The variances of the estimators are smaller than those in stair nested designs of comparable size. The mean squares used in the estimation process are mutually independent, which avoids some of the problems with staggered nested designs.
- Joining models with stair nestingPublication . Fernandes, Célia; Ramos, PauloIn the stair nested designs with u factors we have u steps and a(1), ... , a(u) "active" levels. We would have a(1) observations with different levels for the first factor each of them nesting a single level of each of the remaining factors; next a(2) observations with level a(1) + 1 for the first factor and distinct levels for the second factor each nesting a fixed level of each of the remaining factors, and so on. So the number of level combinations is Sigma(u)(i=1) a(i). In meta-analysis joint treatment of different experiments is considered. Joining the corresponding models may be useful to carry out that analysis. In this work we want joining L models with stair nesting.
- A method to minimize the sum of the variances of the estimators of the variance components in stair nested designsPublication . Fernandes, Célia; Ramos, PauloStair nested designs may be a good alternative to balanced nested designs since we can work with fewer observations and the amount of information for the different factors is more evenly distributed. In stair nested designs the number of treatments is the sum of the "actice" factor levels so these designs lead to a great economy. A method will be used to minimize the sum of the variances of the estimators of the variance components.
- Study of the interactions in a three-way crossed classification modelPublication . Ramos, Paulo; Fernandes, CéliaCrossed classification models are applied in many investigations taking in consideration the existence of interaction between all factors or, in alternative, excluding all interactions, and in this case only the effects and the error term are considered. In this work we use commutative Jordan algebras in the study of the algebraic structure of these designs and we use them to obtain similar designs where only some of the interactions are considered. We finish presenting the expressions of the variance componentes estimators.