An intensive simulation study to compare the spatio–temporal prediction performances among various space–time models is presented. The models having separable spatio–temporal covariance functions and nonseparable ones, under various scenarios, are also considered. The computational performance among the various selected models are compared. The issue of how to select an appropriate space–time m...

The objectives of this study were: (i) to compare five models (Wood, Cobby & Le Du, Wilmink, Cappio Borlino, Djikstra) for describing the lactation curve of Chios sheep, (ii) to identify variation in lactation parameters related to environmental factors (season) and animal factors (parity). A data base on 61,705 recordings of daily milk production obtained from an automatic milking system was u...

The Cp selection criterion is a popular method to choose the smoothing parameter in spline regression. Another widely used method is the generalized maximum likelihood (GML) derived from a normal-theory empirical Bayes framework. These two seemingly unrelated methods, have been shown in Efron (Ann. Statist. 29 (2001) 470) and Kou and Efron (J. Amer. Statist. Assoc. 97 (2002) 766) to be actually...

Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularization methods in terms of maximum a posteriori estimation and has motivated Bayesian interpretations of kernel methods. In this paper we pursue a Bayesian interpretation of sparsity in the kernel setting by making use of...

The Bayesian Information Criterion (BIC) was presented to obtain the appropriate structure, via the number of hidden nodes, and a new algorithm was proposed to improve the convergence speed of backpropagation training method. The algorithm was obtained by employing the conjugate gradient method to solve the nonlinear part in the weights of the hidden layers and the Kalman filter to solve the li...

SUBSET, written in the matrix language Gauss, is a program that identifies optimal subsets of means or proportions based on independent groups. All possible configurations of ordered subsets of groups are identified and the best model is selected using either the AIC or BIC information criterion. For means, both homogeneous and heterogeneous variance cases are considered. SUBSET offers an alter...

Learning visual context is a critical step of dynamic scene modelling. This paper addresses the problem of choosing the most suitable probabilistic model selection criterion for learning visual context of a dynamic scene. A Completed Likelihood Akaike’s Information Criterion (CL-AIC) is formulated to estimate the optimal model order (complexity) for a given visual scene. CL-AIC is designed to o...

Dependent Variable: Y Method: Least Squares Date: 05/23/00 Time: 05:55 Sample: 1 33 Included observations: 33 Variable Coefficient Std. Error t-Statistic Prob. C 102192.4 12799.83 7.983891 0.0000 N -9074.674 2052.674 -4.420904 0.0001 P 0.354668 0.072681 4.879810 0.0000 I 1.287923 0.543294 2.370584 0.0246 R-squared 0.618154 Mean dependent var 125634.6 Adjusted R-squared 0.578653 S.D. dependent v...

While optimal designs are commonly used in the design of experiments, the optimality of those designs frequently depends on the form of an assumed model. Several useful criteria have been proposed to reduce such dependence, and efficient designs have been then constructed based on the criteria, often algorithmically. In the model robust design paradigm, a space of possible models is specified a...