Add to ORKGIn this work we address the problem of prediction in a multidimensional setting. Generalizing a result presented in Ueki and Fueda (2007), we propose a method for correcting estimative predictive regions to reduce their coverage error to third order accuracy. The improved prediction regions are easy to calculate using a suitable bootstrap procedure. An example of application is also included, showing the performance of the proposed method
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
This paper proposes a method to construct well-calibrated frequentist prediction regions, with parti...
We consider longitudinal data and the problem of prediction of subpopulation (domain) characteristic...
In this work we address the problem of prediction in a multidimensional setting. Generalizing a resu...
In this work we address the problem of prediction in a multidimensional setting. Generalizing a resu...
In this work we address the problem of prediction in a multidimensional setting. Generalizing a resu...
In this work we address the problem of prediction in a multidimensional setting. Generalizing a resu...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
The particularly wide range of applications of small area prediction, e.g. in policy making decision...
This paper proposes a method to construct well-calibrated frequentist prediction regions, with parti...
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
This paper proposes a method to construct well-calibrated frequentist prediction regions, with parti...
We consider longitudinal data and the problem of prediction of subpopulation (domain) characteristic...
In this work we address the problem of prediction in a multidimensional setting. Generalizing a resu...
In this work we address the problem of prediction in a multidimensional setting. Generalizing a resu...
In this work we address the problem of prediction in a multidimensional setting. Generalizing a resu...
In this work we address the problem of prediction in a multidimensional setting. Generalizing a resu...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
In this work we address the problem of the construction of prediction regions and distribution funct...
The particularly wide range of applications of small area prediction, e.g. in policy making decision...
This paper proposes a method to construct well-calibrated frequentist prediction regions, with parti...
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
This paper proposes a method to construct well-calibrated frequentist prediction regions, with parti...
We consider longitudinal data and the problem of prediction of subpopulation (domain) characteristic...