Ajustement linéaire lorsque les deux variables sont soumises à des erreurs de variances hétérogènes
Alfred J. Lotka and the mathematics of population.
Algebraic Methods for Studying Interactions Between Epidemiological Variables
BackgroundIndependence models among variables is one of the most relevant topics in epidemiology, particularly in molecular epidemiology for the study of gene-gene and gene-environment interactions. They have been studied using three main kinds of analysis: regression analysis, data mining approaches and Bayesian model selection. Recently, methods of algebraic statistics have been extensively used for applications to biology. In this paper we present...
Algebraic methods in discrete linear estimation
Algebraic structure for the crossing of balanced and stair nested designs
Stair 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...
Algebraic structureof step nesting designs
Step nesting designs may be very useful since they require fewer observations than the usual balanced nesting models. The number of treatments in balanced nesting design is the product of the number of levels in each factor. This number may be too large. As an alternative, in step nesting designs the number of treatments is the sum of the factor levels. Thus these models lead to a great economy and it is easy to carry out inference. To study the algebraic structure of step nesting designs we introduce...
Algorithm 27. Statistical prediction by the method of harmonical weights
Algorithm 32. Construction of polynomial values orthogonal on given set of one-dimensional variable
Algorithm 33. Test for difference in trends in a two-factor experiment with repeated measurements
Algorithm 34. Use of orthogonal components in tests for trend
Algorithm 35. Calculation of measures of stochastical dependence
Algorithm 38. Calculation of all marginal means from an n-way table
Algorithm 39. Evaluation of corrected sums of squares for analysis of variance in a factorial design with n factors
Algorithm 40. Search for marginal means at given factor levels in an n-way table containing data scores and all marginal means
Algorithm 44. An abbreviated method of calculating the Mahalanobis distance or residual sum of squares in a linear regression model
Algorithm. 45. Bayes factor test. An algorithm giving tables for a non-parametric two-sample test of the Bayesian discrimination power of an observed factor
Algorithm 55. Multiple regression with stepwise selection of variables
Algorithm 72. Quasi-optimal ordering of objects
Algorithm 82. Stepwise selection of discriminative variables by the use of the Wilks criterion
Algorithm 83. Stepwise selection of discriminative variables by the use of the trace criterion