Publisher: School of Statistics, Renmin University China, Journal: Journal Data Science, Title: Evaluation Missing Value Estimation for Microarray Data, Authors: Danh V. Nguyen, Naisyin Wang, Raymond J. Carroll

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Realworld data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a tech...

* This research was sponsored in part by the Office of Naval Research under grant no. N00014-03-1-0033. ‡ 0-7803-7952—7/03/$17.00 © 2003 IEEE Abstract – Existing methods of parameter and structure learning of probabilistic inference networks assume that the database is complete. If there are missing values, these values are assumed to be missing at random. This paper incorporates the concepts u...

In this paper, we will use Cochran-Mantel-Haenszel (CMH) statistics to analyse four data sets which have appeared in the literature. It is well known that tests based on the CMH statistics are equivalent to certain standard rank tests but here we show how CMH statistics also apply in less standard situations. In particular, examples are given for randomized block designs both with and without m...

Representation systems have been widely used to capture different forms of incomplete data in various settings. However, existing representation systems are not expressive enough to handle the more complex scenarios of missing data that can occur in practice: these could vary from missing attribute values, missing a known number of tuples, or even missing an unknown number of tuples. In this wo...

Missing values can occur frequently in many real world situations. Such is the case of multi-way data applications, where objects are usually represented by arrays of 2 or more dimensions e.g. biomedical signals that can be represented as time-frequency matrices. This lack of attributes tends to influence the analysis of the data. In classification tasks for example, the performance of classifi...

A composite loss framework is proposed for low-rank modeling of data consisting of interesting and common values, such as excess zeros or missing values. The methodology is motivated by the generalized low-rank framework and the hurdle method which is commonly used to analyze zero-inflated counts. The model is demonstrated on a manufacturing data set and applied to the problem of missing value ...