Bayesian information criterion (BIC) is a common model selection criterion for mixtures of factor analyzers (MFA). However, it is found that BIC penalizes each factor analyzer implausibly using the whole sample size. In this paper, we propose a new criterion for MFA called hierarchical BIC (H-BIC). Formally, the main difference from BIC is that H-BIC penalizes each factor analyzer using its own...

INTRODUCTION This cross-sectional study examined variables associated with body image concern (BIC) and whether these associations differed between female and male students in Egypt. During the period 2009-2010, 3271 undergraduate students (1663 females, 1504 males) at Assuit University in Egypt completed a self-administered questionnaire that assessed BIC and other socio-demographic and health...

Latent growth curve modeling (LGM) combined with the latent classes (LGMM) in the SEM context, is the method under investigation in this study. This dynamic way of analyzing longitudinal data takes an increasingly central position in the social sciences, e.g. in psychology. Despite twenty years development of the theory behind the LGM and LGMM, these are novel methods in analyzing data in pract...

Based on the problem of determining the hidden dimensionality (or the number of latent factors) of Factor Analysis (FA) model, this paper provides a theoretic comparison on several classical model selection criteria, including Akaike’s Information Criterion (AIC), Bozdogan’s Consistent Akaike’s Information Criterion (CAIC), Hannan–Quinn information criterion (HQC), Schwarz’s Bayesian Informatio...

Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are latent. There are no theoretically well justified model selection criteria for HLC models in particular and Bayesian networks with latent nodes in general. Nonetheless, empirical studies suggest that the BIC score is a reasonable criterion to use in practice for le...

Model selection is commonly based on some variation of the BIC or minimum message length criteria, such as MML and MDL. In either case the criterion is split into two terms: one for the model (data code length/model complexity) and one for the data given the model (message length/data likelihood). For problems such as change detection, unsupervised segmentation or data clustering it is common p...

In model-based clustering, the density of each cluster is usually assumed to be a certain basic parametric distribution, e.g., the normal distribution. In practice, it is often difficult to decide which parametric distribution is suitable to characterize a cluster, especially for multivariate data. Moreover, the densities of individual clusters may be multi-modal themselves, and therefore canno...

Bicaudal-C (Bic-C) encodes an RNA-binding protein required maternally for patterning the Drosophila embryo. We identified a set of mRNAs that associate with Bic-C in ovarian ribonucleoprotein complexes. These mRNAs are enriched for mRNAs that function in oogenesis and in cytoskeletal regulation, and include Bic-C RNA itself. Bic-C binds specific segments of the Bic-C 5' untranslated region and ...

OBJECTIVE It has been proposed that biocompatible bicarbonate/lactate based (Bic/Lac), physiologic-pH peritoneal dialysis (PD) solutions will be beneficial in long-term PD. However, we do not yet have detailed knowledge concerning the comparative physiology of buffer transport for these new solutions and their impact on underlying peritoneal transport of solutes and ultrafiltration (UF). The pu...

We consider the regression model in the situation when the number of available regressors pn is much bigger than the sample size n and the number of nonzero coefficients p0n is small (the sparse regression). To choose the regression model, we need to identify the nonzero coefficients. However, in this situation the classical model selection criteria for the choice of predictors like, e.g., the ...