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A method for ordinal outcomes: The ordered stereotype model. [PDF]
Int J Methods Psychiatr Res, 2019 AbstractObjective: The collection and use of ordinal variables are common in many psychological and psychiatric studies. Although the models for continuous variables have similarities to those for ordinal variables, there are advantages when a model developed for modeling ordinal data is used such as avoiding “floor” and “ceiling” effects and avoiding ...
Fernandez D, Liu I, Costilla R.
europepmc +16 more sources
A goodness‐of‐fit test for the ordered stereotype model [PDF]
Statistics in Medicine, 2016 This paper presents a new goodness‐of‐fit test for an ordered stereotype model used for an ordinal response variable. The proposed test is based on the well‐known Hosmer–Lemeshow test and its version for the proportional odds regression model. The latter test statistic is calculated from a grouping scheme assuming that the levels of the ordinal ...
Fernández Martínez, Daniel, Liu, Ivy
exaly +10 more sources
Mixture-based clustering for the ordered stereotype model [PDF]
Computational Statistics & Data Analysis, 2016 Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques such as distance-based algorithms or matrix decomposition and eigenvalues. Recently a group of likelihood-based finite mixture models for a data matrix with binary or count data, using basic Bernoulli or Poisson building blocks ...
Daniel Fernández +2 more
exaly +11 more sources
Generalized estimating equations to estimate the ordered stereotype logit model for panel data [PDF]
Statistics in Medicine, 2020 By modeling the effects of predictor variables as a multiplicative function of regression parameters being invariant over categories, and category‐specific scalar effects, the ordered stereotype logit model is a flexible regression model for ordinal response variables.
Martin Spiess +3 more
exaly +10 more sources
High-dimensional genomic feature selection with the ordered stereotype logit model. [PDF]
Brief Bioinform, 2022 AbstractFor many high-dimensional genomic and epigenomic datasets, the outcome of interest is ordinal. While these ordinal outcomes are often thought of as the observed cutpoints of some latent continuous variable, some ordinal outcomes are truly discrete and are comprised of the subjective combination of several factors.
Seffernick AE +6 more
europepmc +8 more sources
Elastic Net Constrained Stereotype Logit Model for Ordered Categorical Data [PDF]
Biometrics & Biostatistics International Journal, 2015 Gene expression studies are of growing importance in the field of medicine. In fact, sub-types within the same disease have been shown to have differing gene expression profiles. Often, researchers are interested in differentiating a disease by a categorical classification indicative of disease progression.
André Aa, Williams, Kellie J, Archer
semanticscholar +5 more sources
Model-based goodness-of-fit tests for the ordered stereotype model
Statistical Methods in Medical Research, 2019 This paper presents two new model-based goodness-of-fit tests for the ordered stereotype model applied to an ordinal response variable. The proposed tests are based on the Lipsitz test, which partitions the subjects into G groups following the popular Hosmer–Lemeshow test for binary data.
Fernández Martínez, Daniel +4 more
semanticscholar +9 more sources
Goodness-of-Fit and Generalized Estimating Equation Methods for Ordinal Responses Based on the Stereotype Model [PDF]
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 +4 more
doaj +5 more sources
Partial Ordered Stereotype Model: Development of a New Model for Ordinal Data [PDF]
Ordinal variables are categorical variables whose categories have a natural ordering (e.g., Likert scale). Modelling ordinal responses requires specific methods that properly respect the discrete and natural ordering without including arbitrary assumptions, such as equally spaced categories.
Laia Egea Cortés
semanticscholar +4 more sources
Mixture-based Clustering for the Ordered Stereotype Model [PDF]
, 2021 <p>Many of the methods which deal with the reduction of dimensionality in matrices of data are based on mathematical techniques. In general, it is not possible to use statistical inferences or select the appropriateness of a model via information criteria with these techniques because there is no underlying probability model. Furthermore, the use
Fernández MartÃnez, Daniel
openaire +2 more sources