https://elib.bsu.by:443/handle/123456789/93226 Mon, 20 Apr 2026 09:17:05 GMT 2026-04-20T09:17:05Z https://elib.bsu.by:443/handle/123456789/93478 Заглавие документа: A Distance-Distance Plot for Diagnosing Multivariate Outliers Авторы: Willems, G. Аннотация: We propose a diagnostic method that can be used whenever multiple outliers are identified by robust estimates for multivariate location and scatter. The main purpose is visualization of the multivariate data to help determine whether the detected outliers (a) form a separate cluster or (b) are isolated or randomly scattered (such as heavy tails compared with Gaussian). We make use of Mahalanobis distances in order to check for separation and to reveal additional aspects of the data structure. The method is especially useful if multivariate structure is not seen in bivariate scatterplots. Mon, 01 Jan 2007 00:00:00 GMT https://elib.bsu.by:443/handle/123456789/93478 2007-01-01T00:00:00Z https://elib.bsu.by:443/handle/123456789/93477 Заглавие документа: Fuzzy Data and Statistical Modeling Авторы: Viertl, R. Аннотация: Data are frequently not precise numbers but more or less non-precise, also called fuzzy. Before analyzing such data the mathematical description of fuzzy data is necessary. This is possible using fuzzy models. Based on this, descriptive data analysis as well as statistical modeling have to be adapted. Basic methods for this are described in this contribution. Mon, 01 Jan 2007 00:00:00 GMT https://elib.bsu.by:443/handle/123456789/93477 2007-01-01T00:00:00Z https://elib.bsu.by:443/handle/123456789/93476 Заглавие документа: Nadaraya-Watson Density Estimation for Interval Censored Data Авторы: Stoimenova, E. Аннотация: This paper is concerned with the nonparametric estimation of a density function when the data are incomplete due to interval censoring. The Nadaraya- Watson kernel density estimator is modified to allow description of such interval data. Mon, 01 Jan 2007 00:00:00 GMT https://elib.bsu.by:443/handle/123456789/93476 2007-01-01T00:00:00Z https://elib.bsu.by:443/handle/123456789/93475 Заглавие документа: On Statistical Classification of Scientific Texts Авторы: Rudzkis, R.; Balys, V. Аннотация: The research considers the problem of classification of scientific texts. Models and methods based on stochastic distribution of scientific terms are discussed. The preliminar results of experimental study over real-world data are reported. Mon, 01 Jan 2007 00:00:00 GMT https://elib.bsu.by:443/handle/123456789/93475 2007-01-01T00:00:00Z