the relation between single nucleotide polymorphisms (snps) and some diseases has been concerned by many researchers. also the missing snps are quite common in genetic association studies. hence, this article investigates the relation between existing snps in dnmt1 of human chromosome 19 with colorectal cancer. this article aims is to presents an imputation method for missing snps not at random...

A missing value indicates that a particular attribute of an instance learning problem is not recorded. They are very common in many real-life datasets. In spite this, however, most machine methods cannot handle values. Thus, they should be imputed before training. Gaussian Processes (GPs) non-parametric models with accurate uncertainty estimates combined sparse approximations and stochastic var...

Dealing with incomplete information is an important problem in decision making. In this paper, we present a short discussion on this topic and a new estimation method of missing values in an incomplete fuzzy preference relation which is based on the modelling of consistency of preferences via a representable uninorm.

We carry out an ANOVA analysis to compare multiple treatment effects for longitudinal studies with missing values. The treatment effects are modeled semiparametrically via a partially linear regression which is flexible in quantifying the time effects of treatments. The empirical likelihood is employed to formulate nonparametric ANOVA tests for treatment effects with respect to covariates and t...

Motivation: Microarray data is used in a range of application areas in biology, though often it contains considerable numbers of missing values. These missing values can significantly affect subsequent statistical analysis and machine learning algorithms so there is a strong motivation to estimate these values as accurately as possible prior to using these algorithms. While many imputation algo...