Cronbach’s coefficient alpha is a widely used reliability measure in social, behavioral, and education sciences. It is reported in nearly every study that involves measuring a construct through multiple items. With non-tau-equivalent items, McDonald’s omega has been used as a popular alternative to alpha in the literature. Traditional estimation methods for alpha and omega often implicitly assu...

University of Cantabria Avenida de los Castros s/n, 39005, Santander, Spain This paper discusses the most common problems associated with the determination of design wave heights. It analyzes two common methods used In fitting wave data and shows some of the stability or inconsistency prcAlems associated with commonly used distributions. Some methods to obtain confidence intervals, detecting of...

Existing methods of parameter and structure learning of Bayesian Networks (BNs) from a database assume that the database is complete. If there are missing values, they are assumed to be missing at random. This paper incorporates the concepts used in Dempster-Shafer theory of belief functions to learn both the parameters and structure of BNs. Instead of filling the missing values by their estima...

While considering processes of decision making we often encounter the problem of incomplete information. In group decision making (GDM) problems each decision maker is supposed to provide a matrix that describes his/her preferences over the set of given options. However, we have to take into account that an expert can not be able to define his/her preferences about all the options. Usually the ...

Raw Data used in data mining often contain missing information, which inevitably degrades the quality of the derived knowledge. In this paper, a new method of guessing missing attribute values is suggested. This method selects attributes one by one using attribute group mutual information calculated by flattening the already selected attributes. As each new attribute is added, its missing value...

We focus on learning over multivariate and relational timeseries where relations are modeled by a graph. We propose a model that is able to simultaneously fill in missing values and predict future ones. This approach is based on representation learning techniques, where temporal data are represented in a latent vector space so as to capture the dynamicity of the process and also the relations b...

Even in well-controlled situations, missing data always occur in longitudinal data analysis. Missing data may degrade the performance of confidence intervals, reduce statistical power and bias parameter estimate. In this paper, we review and discuss general approaches for handling miss data in longitudinal studies. We first illustrate the mechanism of missing data. Then we focus on the methods ...

Textbook database examples are often wrong and simplistic. Unfortunately Data is never born clean or pure. Errors, missing values, repeated entries, inconsistent instances and unsatisfied business rules are the norm rather than the exception. Data cleaning (also known as data cleansing, record linkage and many other terminologies) is growing as a major application requirement and an interdiscip...

Abstract. Value-added models have been widely used to assess the contributions of individual teachers and schools to students’ academic growth based on longitudinal student achievement outcomes. There is concern, however, that ignoring the presence of missing values, which are common in longitudinal studies, can bias teachers’ value-added scores. In this article, a flexible correlated random ef...

Clinical measurements collected over time are naturally represented as multivariate time series (MTS), which often contain missing data. An autoencoder can learn low dimensional vectorial representations of MTS that preserve important data characteristics, but cannot deal explicitly with missing data. In this work, we propose a new framework that combines an autoencoder with the Time series Clu...