scispace - formally typeset
Search or ask a question
Topic

Ordinal regression

About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: The prediction performance of scoring systems with respect to an ordinal outcome scale is investigated, based on grouped continuous logistic models as well as on an extension of the stereotype logistic regression model.
Abstract: Scoring systems are used in nearly all fields of medicine for evaluation of the state of a disease. The prediction performance of scoring systems with respect to an ordinal outcome scale is investigated, based on grouped continuous logistic models as well as on an extension of the stereotype logistic regression model. The latter is a canonical approach, which allows assessment of properties of outcome categories such as partial and total ordering, distinguishability and allocatability. The approach is applied to a data set of patients with injuries of the head.

8 citations

Proceedings ArticleDOI
14 Nov 2013
TL;DR: This paper presents a new method for rating prediction in e-commerce, which uses ordinal regression based on linear discriminant analysis (LDA) with multi-modal features, and shows higher performance of the rating prediction can be realized than that when using single kind of features.
Abstract: This paper presents a new method for rating prediction in e-commerce, which uses ordinal regression based on linear discriminant analysis (LDA) with multi-modal features. In order to realize accurate recommendation in e-commerce, the proposed method estimates each user's rating for target items. Note that we define the rating as “the degree of preference for each item by a user.” For estimating the target user's preference of each item from the past ratings of other items, the proposed method performs training from pairs of “ratings of items” and their feature vectors using ordinal regression based on LDA. Furthermore, in this approach, new features are obtained by applying canonical correlation analysis (CCA) to textual and visual features extracted from review's texts and images on the Web, respectively. Therefore, higher performance of the rating prediction can be realized by our method than that when using single kind of features. Experimental results obtained by applying the proposed method to an actual movie data set, which has been provided by SNAP, show the effectiveness of the proposed method.

8 citations

Journal ArticleDOI
TL;DR: In this article, the fundamental problem of ordinal multivariate analysis remains unsolved, and we do not know what an ordinal multiivariate relation is, which is the fundamental issue of our work.
Abstract: phenomena is not to say that every research effort is engaged in "hypothesis testing." Indeed, whole studies and programs of research can be exploratory and directed toward discovery rather than confirmation. But the essence of a rational empirical discipline is to make assertions about phenomena and support or refute these with systematic empirical evidence. So, sooner or later the question of evidence for or against hypotheses comes up. And even in exploratory research, one develops and provisionally accepts or rejects countless small hypotheses on the basis of evidence. But all this does not affect the essential issue: the fact is, the fundamental problem of ordinal multivariate analysis remains unsolved, and we do not know what an ordinal multivariate relation is.

8 citations

01 Jan 2009
TL;DR: In this paper, the use of categorical variables in regression modeling is discussed and some pitfalls in using numerically scaled ordinal variables are considered, as well as some pitfalls of using categorical features in regression models.
Abstract: The use of categorical variables in regression modeling is discussed. Some pitfalls in the use of numerically scaled ordinal variables are considered.

8 citations

Journal ArticleDOI
TL;DR: Except in the simplest situations, both methods outperform basic transformation approaches commonly used in practice and are applied to an HIV biomarker study.
Abstract: Continuous response variables are often transformed to meet modeling assumptions, but the choice of the transformation can be challenging. Two transformation models have recently been proposed: semiparametric cumulative probability models (CPMs) and parametric most likely transformation models (MLTs). Both approaches model the cumulative distribution function and require specifying a link function, which implicitly assumes that the responses follow a known distribution after some monotonic transformation. However, the two approaches estimate the transformation differently. With CPMs, an ordinal regression model is fit, which essentially treats each continuous response as a unique category and therefore nonparametrically estimates the transformation; CPMs are semiparametric linear transformation models. In contrast, with MLTs, the transformation is parameterized using flexible basis functions. Conditional expectations and quantiles are readily derived from both methods on the response variable's original scale. We compare the two methods with extensive simulations. We find that both methods generally have good performance with moderate and large sample sizes. MLTs slightly outperformed CPMs in small sample sizes under correct models. CPMs tended to be somewhat more robust to model misspecification and outcome rounding. Except in the simplest situations, both methods outperform basic transformation approaches commonly used in practice. We apply both methods to an HIV biomarker study.

8 citations


Network Information
Related Topics (5)
Regression analysis
31K papers, 1.7M citations
84% related
Linear regression
21.3K papers, 1.2M citations
79% related
Inference
36.8K papers, 1.3M citations
78% related
Empirical research
51.3K papers, 1.9M citations
78% related
Social media
76K papers, 1.1M citations
77% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023102
2022191
202188
202093
201979
201873