Incremental Sparse Bayesian Ordinal Regression [PDF]
, 2018 Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning. An important class of approaches to OR models the problem as a linear combination of basis functions that ...
de Rijke, Maarten, Li, Chang
core +3 more sources
Analyzing and visualizing repeated-measures needs assessment data using the ranked discrepancy model
Advancements in Agricultural DevelopmentThe Ranked Discrepancy Model was introduced in 2021 as an alternative for analyzing Borich-style competency-based needs assessment data which avoided the pitfalls associated with the original methods for analysis.
Lendel Narine, Amy Harder
doaj +1 more source
Ordinal Synchronization: Using ordinal patterns to capture interdependencies between time series
, 2018 We introduce Ordinal Synchronization ($OS$) as a new measure to quantify synchronization between dynamical systems. $OS$ is calculated from the extraction of the ordinal patterns related to two time series, their transformation into $D$-dimensional ...
Buldú, Javier M. +4 more
core +1 more source
An ordinal approach to the measurement of inequality in asset ownership: methodology and an application to Mexican data. [PDF]
, 2018 Asset indices based on durable goods ownership and housing characteristics are widely used to proxy wealth when income or expenditure data are not available. In this paper, we propose an ordinal approach to using data on assets when estimating the wealth
Silber, Jacques
core
When is it Better to Compare than to Score? [PDF]
, 2014 When eliciting judgements from humans for an unknown quantity, one often has the choice of making direct-scoring (cardinal) or comparative (ordinal) measurements.
Balakrishnan, Sivaraman +5 more
core
Regression with Ordered Predictors via Ordinal Smoothing Splines
Frontiers in Applied Mathematics and Statistics, 2017 Many applied studies collect one or more ordered categorical predictors, which do not fit neatly within classic regression frameworks. In most cases, ordinal predictors are treated as either nominal (unordered) variables or metric (continuous) variables ...
Nathaniel E. Helwig, Nathaniel E. Helwig
doaj +1 more source
Characterization of the five-tier model of creative mathematical thinking in primary school learners
AlifmatikaCreative mathematical thinking skills among elementary students remain relatively low, largely due to predominantly procedural instruction, highlighting the need to examine the characteristics of their innovative thinking stages in solving data ...
Ervita Tri Susilowati +2 more
doaj +1 more source
Variational Bayes Estimation of Discrete-Margined Copula Models with Application to Time Series
, 2018 We propose a new variational Bayes estimator for high-dimensional copulas with discrete, or a combination of discrete and continuous, margins. The method is based on a variational approximation to a tractable augmented posterior, and is faster than ...
Loaiza-Maya, Ruben +1 more
core +1 more source
Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds
, 2018 Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal effects as comparisons between the potential outcomes ...
Dasgupta, Tirthankar +2 more
core +1 more source
Multiple Imputation For Missing Ordinal Data [PDF]
, 2005 Simulations were used to compare complete case analysis of ordinal data with including multivariate normal imputations. MVN methods of imputation were not as good as using only complete cases.
Chen, Ling +3 more
core +2 more sources