The problem of prediction is considered in a multidimensional setting. Extending an idea presented by Barndorff-Nielsen and Cox, a predictive density for a multivariate random variable of interest is proposed. This density has the form of an estimative density plus a correction term. It gives simultaneous prediction regions with coverage error of smaller asymptotic order than the estimative density. A simulation study is also presented showing the magnitude of the improvement with respect to the estimative method
We consider methods of evaluating multivariate density forecasts. A recently proposed method is foun...
Using a Bayesian framework this paper provides a multivariate combination approach to prediction bas...
Abstract. This paper combines multivariate density forecasts of output growth, inflation and interes...
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
A predictive density function g' is obtained for the multilevel model which is optimal in minimizing...
In this work we address the problem of the construction of prediction regions and distribution funct...
AbstractAssuming a general linear model with known covariance matrix, several linear and nonlinear p...
This paper combines multivariate density forecasts of output growth, inflation and interest rates fr...
Abstract: An approach to solving the problem of heterogeneous multivariate time series analysis with...
To make a prediction of a response variable from an explanatory one which takes into account feature...
Many real problems such as stock market prediction, weather forecasting etc has inherent randomness ...
This talk will address the estimation of predictive densities and their efficiency as measured by fr...
textabstractWe propose a Bayesian combination approach for multivariate predictive densities which r...
We consider methods of evaluating multivariate density forecasts. A recently proposed method is foun...
The aim of this paper is to define prediction intervals based on multiplicative combination of elem...
We consider methods of evaluating multivariate density forecasts. A recently proposed method is foun...
Using a Bayesian framework this paper provides a multivariate combination approach to prediction bas...
Abstract. This paper combines multivariate density forecasts of output growth, inflation and interes...
The problem of prediction is considered in a multidimensional setting. Extending an idea presented b...
A predictive density function g' is obtained for the multilevel model which is optimal in minimizing...
In this work we address the problem of the construction of prediction regions and distribution funct...
AbstractAssuming a general linear model with known covariance matrix, several linear and nonlinear p...
This paper combines multivariate density forecasts of output growth, inflation and interest rates fr...
Abstract: An approach to solving the problem of heterogeneous multivariate time series analysis with...
To make a prediction of a response variable from an explanatory one which takes into account feature...
Many real problems such as stock market prediction, weather forecasting etc has inherent randomness ...
This talk will address the estimation of predictive densities and their efficiency as measured by fr...
textabstractWe propose a Bayesian combination approach for multivariate predictive densities which r...
We consider methods of evaluating multivariate density forecasts. A recently proposed method is foun...
The aim of this paper is to define prediction intervals based on multiplicative combination of elem...
We consider methods of evaluating multivariate density forecasts. A recently proposed method is foun...
Using a Bayesian framework this paper provides a multivariate combination approach to prediction bas...
Abstract. This paper combines multivariate density forecasts of output growth, inflation and interes...