Letter to the editor

AbstractThis letter shows how the main result contained in a paper recently appeared in the Journal of Multivariate Analysis was in fact a particular case of a more general theorem published three years before

Model confidence sets and forecast combination: an application to age-specific mortality

Background: Model averaging combines forecasts obtained from a range of models, and it often produces more accurate forecasts than a forecast from a single model. Objective: The crucial part of forecast accuracy improvement in using the model averaging lies in the determination of optimal weights from a finite sample. If the weights are selected sub-optimally, this can affect the accuracy of the model-averaged forecasts. Instead of choosing the optimal weights, we consider trimming a set of models before equally averaging forecasts from the selected superior models. Motivated by Hansen et al. (2011), we apply and evaluate the model confidence set procedure when combining mortality forecasts. Data & Methods: The proposed model averaging procedure is motivated by Samuels and Sekkel (2017) based on the concept of model confidence sets as proposed by Hansen et al. (2011) that incorporates the statistical significance of the forecasting performance. As the model confidence level increases, the set of superior models generally decreases. The proposed model averaging procedure is demonstrated via national and sub-national Japanese mortality for retirement ages between 60 and 100+. Results: Illustrated by national and sub-national Japanese mortality for ages between 60 and 100+, the proposed model-average procedure gives the smallest interval forecast errors, especially for males. Conclusion: We find that robust out-of-sample point and interval forecasts may be obtained from the trimming method. By robust, we mean robustness against model misspecification

The random Tukey depth

The computation of the Tukey depth, also called halfspace depth, is very demanding, even in low dimensional spaces, because it requires that all possible one-dimensional projections be considered. A random depth which approximates the Tukey depth is proposed. It only takes into account a finite number of one-dimensional projections which are chosen at random. Thus, this random depth requires a reasonable computation time even in high dimensional spaces. Moreover, it is easily extended to cover the functional framework. Some simulations indicating how many projections should be considered depending on the kind of problem, sample size and dimension of the sample space among others are presented. It is noteworthy that the random depth, based on a very low number of projections, obtains results very similar to those obtained with the Tukey depth.

Optimal Transportation Plans and Convergence in Distribution

AbstractExplicit expression of mappings optimal transportation plans for the Wasserstein distance in Rp,p>1, are not generally available. Therefore, it is of great interest to provide results which justify the practical use of simulation techniques to obtain approximate optimal transportation plans. This is done in this paper, where we obtain the consistency of the empirical optimal transportation plans. Our results can also be employed to justify a definition of multidimensional complete dependence

Some stochastics on monotone functions

AbstractA measure of departures of monotonicity of a given function, the Lr-DIP, 1 ⩽ r ⩽ ∞, is introduced. Our analysis is performed to cover two different situations: When the function is known our interest is related to its behavior in a stochastic model. However, in most cases, the knowledge of the function is obtained through a preliminary estimation of the function. In both situations the aim focuses in the obtainment of strong consistency results