Propose a new prediction model averaging (PMA) estimator.Prove that the PMA estimator is asymptotically optimal.Show that the PMA estimator has good performance in simulation.Demonstrate that PMA can lead to large gains in box office prediction accuracy....
Jackknife Model Averaging for Composite Quantile Regression Model averaging considers the model uncertainty and is an alternative to model selection. In this paper, we propose a frequentist model averaging estimator... M Wang,G Zou 被引量: 0发表: 2019年 加载更多来源...
Model averaging marginal regression for high dimensional conditional quantile predictionIn this article, we propose a high dimensional semiparametric model average approach to predict the conditional quantile of the response variable. Firstly, we approximate the multivariate conditional quantile function by an...
Motivated by the cold rolling mill process, it is attempted to propose an efficient method since the problem of online prediction often involves uncertainty in the use of appropriate prediction model, as a result a method called dynamic model averaging (DMA) is proposed in which a state space ...
Gianola, "Model averaging for genome-enabled prediction with reproducing kernel Hilbert spaces: a case study with pig litter size and wheat yield," Journal of Animal Breeding and Genetics, vol. 131, no. 2, pp. 105-115, 2014.Tusell, L., Perez-Rodriguez, P., Forni, S., and Gianola, D...
Zhang XY, Ullah A, Zhao SW (2016) On the dominance of Mallows model averaging estimator over ordinary least squares estimator. Econ Lett 142:69–73 MathSciNet MATH Google Scholar Zhao W, Tao T, Zio E (2015) System reliability prediction by support vector regression with analytic selection ...
[21,28], the unweighted averaging might be a reasonable ensemble for similar base learners of comparable performance. In this research the model averaging (unweighted) can be calculated by combining the softmax probabilities from three different classifications model. The mean class the probability is...
We propose a novel framework to address class imbalance by utilizing the concept of Bayesian model averaging. Specifically, we assign unequal weights to machine learning sub-models that incorporate different combinations of variables, thereby creating an accurate and robust model for predicting consumer ...
The main framework of BNNPT is based on a permutation test30 of the square error of a bagging nearest neighbor estimator. For two vectors X and Y of length N, we first construct a bagging neighborhood structure based on X only. The neighborhood structure is an index matrix of N rows and...
The MSE of an estimator measures the average squared difference between the estimated values and the actual value, which is defined as:MSE=1n∑i=1n(Yi−Y^i)2 Where Yi and Y^i represent the actual and predicted value. 3. Results 3.1. Demographic, clinical and behavioral measures The demog...