NET EXPORTS OF THE EUROPEAN POST-TRANSITION COUNTRIES: THE DYNAMIC FACTOR APPROACH

The paper brings the common and unobserved factor of net exports of the six European post-transition countries, namely Czech Republic, Slovakia, Slovenia, Hungary, Latvia and Romania. The data sample consists of the quarterly net exports time series for the period 1996q1 – 2017q2. The results out of the dynamic factor analysis (DFA) revealed that one hidden random walk explains 53.17% of temporal variation in net exports of the observed sample countries. The relationship between unobserved hidden factor and net exports of Czech Republic, Slovakia, Slovenia and Hungary is found to be positive while the Romanian net export was negatively affected. However, the unobserved factor identified in this paper cannot explain the net export in case of Latvia.

Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed

When dealing with large scale gene expression studies, observations are commonly contaminated by sources of unwanted variation such as platforms or batches. Not taking this unwanted variation into account when analyzing the data can lead to spurious associations and to missing important signals. When the analysis is unsupervised, e.g. when the goal is to cluster the samples or to build a corrected version of the dataset—as opposed to the study of an observed factor of interest—taking unwanted variation into account can become a difficult task. The factors driving unwanted variation may be correlated with the unobserved factor of interest, so that correcting for the former can remove the latter if not done carefully. We show how negative control genes and replicate samples can be used to estimate unwanted variation in gene expression, and discuss how this information can be used to correct the expression data. The proposed methods are then evaluated on synthetic data and three gene expression datasets. They generally manage to remove unwanted variation without losing the signal of interest and compare favorably to state-of-the-art corrections. All proposed methods are implemented in the bioconductor package RUVnormalize.

A Robust Factor Analysis Model for Dichotomous Data

Factor analysis is widely used in psychology, sociology and economics, as an analytically tractable method of reducing the dimensionality of the data in multivariate statistical analysis. The classical factor analysis model in which the unobserved factor scores and errors are assumed to follow the normal distributions is often criticized because of its lack of robustness. This paper introduces a new robust factor analysis model for dichotomous data by using robust distributions such as multivariatet-distribution. After comparing the fitting results of the normal factor analysis model and the robust factor analysis model for dichotomous data, it can been seen that the robust factor analysis model can get more accurate analysis results in some cases, which indicates this model expands the application range and practical value of the factor analysis model.