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...

This paper investigates via a simulation study the effects of nonlinearities on several commonly used order selection criteria. The most important finding of this study is that SIC, FPE, HQC and BIC perform considerably well in estimating the true autoregressive order, even in the presence of STAR or TAR nonlinearity. Thus we conclude that these criteria may be safely applied to determine the t...

"On-Chip" IDDQ testing by the incorporation of BuiltIn Current (BIC) sensors has some advantages over "offchip" techniques. However, the integration of sensors poses analog design problems which are hard to be solved by a digital designer. The automatic incorporation of the sensors using parameterized BIC cells could be a promising alternative. The work reported here identifies partitioning cri...

In landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike's information c...

The evolution of drug resistance in HIV is characterized by the accumulation of resistance-associated mutations in the HIV genome. Mutagenetic trees, a family of restricted Bayesian tree models, have been applied to infer the order and rate of occurrence of these mutations. Understanding and predicting this evolutionary process is an important prerequisite for the rational design of antiretrovi...

We propose a new method in two variations for the identification of most relevant covariates in linear models with homoscedastic errors. In contrast to AIC, BIC and other information criteria, our method is based on an interpretable scaled quantity. This quantity measures a maximal relative error one makes by selecting covariates from a given set of all available covariates. The proposed model ...

The problem of locating multiple interacting quantitative trait loci (QTL) can be addressed as a multiple regression problem, with marker genotypes being the regressor variables. An important and difficult part in fitting such a regression model is the estimation of the QTL number and respective interactions. Among the many model selection criteria that can be used to estimate the number of reg...

With uncorrelated Gaussian factors extended to mutually independent factors beyond Gaussian, the conventional factor analysis is extended to what is recently called independent factor analysis. Typically, it is called binary factor analysis (BFA) when the factors are binary and called non-Gaussian factor analysis (NFA) when the factors are from real non-Gaussian distributions. A crucial issue i...

For the problem of variable selection in generalized linear models, we develop various adaptive Bayesian criteria. Using a hierarchical mixture setup for model uncertainty, combined with an integrated Laplace approximation, we derive Empirical Bayes and Fully Bayes criteria that can be computed easily and quickly. The performance of these criteria is assessed via simulation and compared to othe...

In this paper, we propose a novel information criteria-based approach to select the dimensionality of word2vec Skip-gram (SG). From perspective probability theory, SG is considered as an implicit distribution estimation under assumption that there exists true contextual among words. Therefore, apply criteria with aim selecting best so corresponding model can be close possible distribution. We e...