Results 31 to 40 of about 21,655,635 (191)

Modelling Qualitative Data from Repeated Surveys

Computation, 2023
This article presents an innovative dynamic model that describes the probability distributions of ordered categorical variables observed over time. For this purpose, we extend the definition of the mixture distribution obtained from the combination of a ...
Marcella Corduas, Domenico Piccolo
doaj   +1 more source

Interlaboratory comparison of the intensity of drinking water odor and taste by two-way ordinal analysis of variation without replication

Journal of Water and Health, 2022
A case study of ordinal data from human organoleptic examination (sensory analysis) of drinking water obtained in an interlaboratory comparison of 49 ecological laboratories is described.
Tamar Gadrich, Ilya Kuselman, Francesca R. Pennecchi, D. Brynn Hibbert, Anastasia A. Semenova, Pui Sze Cheow, Vladimir N. Naidenko   +6 more
doaj   +1 more source

Goodness-of-Fit and Generalized Estimating Equation Methods for Ordinal Responses Based on the Stereotype Model

Stats, 2022
Background: Data with ordinal categories occur in many diverse areas, but methodologies for modeling ordinal data lag severely behind equivalent methodologies for continuous data.
Daniel Fernández, Louise McMillan, Richard Arnold, Martin Spiess, Ivy Liu   +4 more
doaj   +1 more source

Comparison Non-Parametric Machine Learning Algorithms for Prediction of Employee Talent

IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2021
Classification of ordinal data is part of categorical data. Ordinal data consists of features with values based on order or ranking. The use of machine learning methods in Human Resources Management is intended to support decision-making based on ...
I Ketut Adi Wirayasa, Arko Djajadi, H andri Santoso, Eko Indrajit   +3 more
doaj   +1 more source

A Marginal Maximum Likelihood Approach for Extended Quadratic Structural Equation Modeling with Ordinal Data

Structural Equation Modeling: A Multidisciplinary Journal, 2020
The literature on non-linear structural equation modeling is plentiful. Despite this fact, few studies consider interactions between exogenous and endogenous latent variables.
Shaobo Jin, J. Vegelius, F. Yang-Wallentin   +2 more
semanticscholar   +1 more source

An Ordinal Data Clustering Algorithm with Automated Distance Learning

AAAI Conference on Artificial Intelligence, 2020
Clustering ordinal data is a common task in data mining and machine learning fields. As a major type of categorical data, ordinal data is composed of attributes with naturally ordered possible values (also called categories interchangeably in this paper).
Yiqun Zhang, Y. Cheung
semanticscholar   +1 more source

On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio

Risks, 2018
We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM) and mixture-based clustering for an ordered stereotype model (OSM).
Tatjana Miljkovic, and Daniel Fernández   +1 more
doaj   +1 more source

Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation

Frontiers in Digital Health, 2023
This paper presents a new Bayesian method for analyzing Ecological Momentary Assessment (EMA) data and applies this method in a re-analysis of data from a previous EMA study.
Arne Leijon, Petra von Gablenz, Inga Holube, Jalil Taghia, Karolina Smeds   +4 more
doaj   +1 more source

Co-Clustering of Ordinal Data via Latent Continuous Random Variables and Not Missing at Random Entries

Journal of Computational And Graphical Statistics, 2020
This article is about the co-clustering of ordinal data. Such data are very common on e-commerce platforms where customers rank the products/services they bought.
Marco Corneli, C. Bouveyron, Pierre Latouche   +2 more
semanticscholar   +1 more source

How Good are Our Measures? Investigating the Appropriate Use of Factor Analysis for Survey Instruments

Journal of MultiDisciplinary Evaluation, 2015
Background: Evaluation work frequently utilizes factor analysis to establish the dimensionality, reliability, and stability of surveys. However, survey data is typically ordinal, violating the assumptions of most statistical methods, and thus is often ...
Megan Sanders, P. Cristian Gugiu, Patricia Enciso   +2 more
doaj   +1 more source