Malgorzata Bogdan

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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(More)
MOTIVATION Pairwise local sequence alignment is commonly used to search data bases for sequences related to some query sequence. Alignments are obtained using a scoring matrix that takes into account the different frequencies of occurrence of the various types of amino acid substitutions. Software like BLAST provides the user with a set of scoring matrices(More)
One of the most popular criteria for model selection is the Bayesian Information Criterion (BIC). It is based on an asymptotic approximation using Bayes rule when the sample size tends to infinity and the dimension of the model is fixed. Although it works well in classical applications, it performs less satisfactorily for high dimensional problems, i.e.(More)
We consider the situation when a large data base needs to be searched to identify a few important predictors of a given quantitative response variable. There is a lot of evidence that in this case classical model selection criteria, like Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC), have a strong tendency to overestimate the(More)
We consider the problem of locating multiple interacting quantitative trait loci (QTL) influencing traits measured in counts. In many applications the distribution of the count variable has a spike at zero. Zero-inflated generalized Poisson regression (ZIGPR) allows for an additional probability mass at zero and hence an improvement in the detection of(More)
<lb>In regression settings where explanatory variables have very low correlations and where there<lb>are relatively few effects each of large magnitude, it is commonly believed that the Lasso shall be<lb>able to find the important variables with few errors—if any. In contrast, this paper shows that<lb>this is not the case even when the design variables are(More)
With the rise of both the number and the complexity of traits of interest, control of the false discovery rate (FDR) in genetic association studies has become an increasingly appealing and accepted target for multiple comparison adjustment. While a number of robust FDR-controlling strategies exist, the nature of this error rate is intimately tied to the(More)