Un exemple de plan d'expériences
En este trabajo se introduce un nuevo estimador de la recta de regresión cuando la varianza de los errores aleatorios no es homogénea. La consideración de que la función varianza sea suave nos permite estimarla mediante métodos de estimación no paramétrica para luego a través de tales estimaciones definir un estimador mínimo cuadrático ponderado. Se prueba que tal estimador es asintóticamente optimal en el sentido de la mínima varianza.
Este artículo describe un método para identificar casos extremos de un modelo de regresión lineal susceptibles de alterar la detección de una multicolinearidad. El método está basado en una aproximación del cambio que produce la eliminación de un reducido grupo de casos en los autovalores de la matriz de correlación. Varios ejemplos ilustran las aplicaciones prácticas del método.
The aim of the paper is to show some possible statistical solution of the estimation of the dispersion of the GPS receiver. The presented method (based on theory of linear model with additional constraints of type I) can serve for an improvement of the accuracy of estimators of coordinates acquired from the GPS receiver.
The aim of the paper is to determine an influence of uncertainties in design and covariance matrices on estimators in linear regression model.
A large number of parameters in regression models can be serious obstacle for processing and interpretation of experimental data. One way how to overcome it is an elimination of some parameters. In some cases it need not deteriorate statistical properties of estimators of useful parameters and can help to interpret them. The problem is to find conditions which enable us to decide whether such favourable situation occurs.
The simplest classification task is to divide a set of objects into two classes, but most of the problems we find in real life applications are multi-class. There are many methods of decomposing such a task into a set of smaller classification problems involving two classes only. Among the methods, pairwise coupling proposed by Hastie and Tibshirani (1998) is one of the best known. Its principle is to separate each pair of classes ignoring the remaining ones. Then all objects are tested against...