Topic
Ordinal regression
About: Ordinal regression is a research topic. Over the lifetime, 1879 publications have been published within this topic receiving 65431 citations.
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TL;DR: In this article, the authors describe forest land use changes during the last decades in Greece as well as analyze the major regional and economic development implications, focusing on the analysis of possible driving forces with economic and social origin.
27 citations
14 Jul 2006
TL;DR: The proposed method is able to handle non-linear ordering on the class and attribute values of classified objects and lies on the boundary between ordinal classification trees, classification trees with monotonicity constraints and multi-relational classification trees.
Abstract: Classification methods commonly assume unordered class values. In many practical applications – for example grading – there is a natural ordering between class values. Furthermore, some attribute values of classified objects can be ordered, too. The standard approach in this case is to convert the ordered values into a numeric quantity and apply a regression learner to the transformed data. This approach can be used just in case of linear ordering. The proposed method for such a classification lies on the boundary between ordinal classification trees, classification trees with monotonicity constraints and multi-relational classification trees. The advantage of the proposed method is that it is able to handle non-linear ordering on the class and attribute values. For the better understanding, we use a toy example from the semantic web environment – prediction of rules for the user's evaluation of hotels.
27 citations
28 Nov 2011
TL;DR: This paper presents a novel algorithm called manifold ordinal regression (MOR) for image ranking, capable of uncovering the intrinsically nonlinear structure held by the image data sets, and provides faithful rating to the new coming images.
Abstract: In this paper, we present a novel algorithm called manifold ordinal regression (MOR) for image ranking. By modeling the manifold information in the objective function, MOR is capable of uncovering the intrinsically nonlinear structure held by the image data sets. By optimizing the ranking information of the training data sets, the proposed algorithm provides faithful rating to the new coming images. To offer more general solution for the real-word tasks, we further provide the semi-supervised manifold ordinal regression (SS-MOR). Experiments on various data sets validate the effectiveness of the proposed algorithms.
27 citations
TL;DR: The inferential implications of OLSLR-based inference on OCR are investigated using simulated data to explore realized Type I error rate and realized statistical power under a variety of scenarios.
Abstract: Ordered categorical variables are frequently encountered as response variables in many disciplines. Agricultural examples include quality assessments of soil or food products, and evaluation of lesion severity, such as teat ends status in dairy cattle. Ordered categorical responses (OCR) are characterized by multiple levels recorded on a ranked scale, whereby levels appraise order but may not be informative of relative magnitude or proportionality between levels. A number of statistically sound methods are available in the standard toolbox to deal with OCR, such as constrained cumulative logit and probit models; however, these are commonly underutilized in practice. Instead, ordinary least squares linear regression (OLSLR) is often employed to infer upon OCR, despite violation of basic model assumptions. In this study, we investigate the inferential implications of OLSLR-based inference on OCR using simulated data to explore realized Type I error rate and realized statistical power under a variety of scenarios. The design of the simulation study was motivated by a data application, thus considering increasing number of levels and various frequency distributions of the OCR. We then illustrate inferential performance of OLSLR relative to a probit regression model fitted to an OCR using a survey dataset of veterinarian antimicrobial use in cattle feedlots. This article has supplementary material online.
27 citations
TL;DR: Boyle et al. as discussed by the authors proposed a multivariate analysis of Ordinal Variables for path analysis and found that it is possible to reduce error interpretation for the squared generalized multiple, partial, and multiple partial correlation coefficients and their special cases.
Abstract: Boyle, Richard P. 1970. \"Path Analysis and Ordinal Data.\" American Journal of Sociology 75 (January): 461-80. Coleman, James S. 1964. Introduction to Mathematical Sociology. New York: Free Press. Hawkes, Roland J. 1971. \"The Multivariate Analysis of Ordinal Measures.\" American Journal of Sociology 76 (March): 908-26. Land, Kenneth C. 1969. \"Principles of Path Analysis.\" Pp. 3-37 in Sociology Methodology, 1969, edited by Edgar F. Borgatta. San Francisco: Jossey-Bass. Ploch, Donald R. 1974. \"Ordinal Measures of Association and the General Linear Model.\" Pp. 343-98 in Measurement in the Social Sciences, edited by H. M. Blalock, Jr. Chicago: Aldine. Reynolds, H. T. 1971. Making Causal Inferences with Ordinal Data. Chapel Hill, N.C.: Institute for Reaserch in Social Science. Smith, Robert B. 1974. \"Continuities in Ordinal Path Analysis.\" Social Forces 53 (December): 200-229. . 1977a. \"Proportional Reduction in Error Interpretations for Daniel's r2 and Its Special Cases.\" Social Forces 55 (June): 1067-75. . 1977b. \"Proportional Reduction in Error Interpretations for the Squared Generalized Multiple, Partial, and Multiple-Partial Correlation Coefficients and Their Special Cases.\" Social Forces 56 (December): 688-702. -. Forthcoming. \"The Use of Spearman's Pb in Causal Models of Ordinal Data.\" Somers, Robert H. 1962. \"A New Asymmetric Measure of Association for Ordinal Variables.\" American Sociological Review 27 (December): 799-811.
27 citations