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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Dissertation•
06 Jul 2010
TL;DR: Acknowledgements and dedications are given in this paper, with a Table of Table of Contents and Table of acknowledgements for each of the authors and their contributions to this work.
Abstract: ................................................................................................................................... iii Acknowledgements ................................................................................................................. vii Dedication ................................................................................................................................ ix Table of
11 citations
Posted Content•
TL;DR: Gologit2 as mentioned in this paper is a generalized ordered logit model for ordinal dependent variables that can estimate models that are less restrictive than the proportional odds/parallel lines models estimated by ologit but more parsimonious and interpretable than those estimated by a non-ordinal method, such as multinomial logistic regression.
Abstract: gologit2 estimates generalized ordered logit models for ordinal dependent variables. A major strength of gologit2 is that it can also estimate three special cases of the generalized model: the proportional odds/parallel lines model, the partial proportional odds model, and the logistic regression model. Hence, gologit2 can estimate models that are less restrictive than the proportional odds /parallel lines models estimated by ologit (whose assumptions are often violated) but more parsimonious and interpretable than those estimated by a non-ordinal method, such as multinomial logistic regression (i.e. mlogit). The svy: prefix, as well as factor variables and post-estimation commands such as margins, are supported. Other key strengths of gologit2 include options for linear constraints, alternative model parameterizations, automated model fitting, alternative link functions (logit, probit, complementary log-log, log-log & cauchit), and the computation of estimated probabilities via the predict command. gologit2 works under Stata 11.2 or higher. Those with older versions of Stata should use gologit29 instead. gologit2 is inspired by Vincent Fu's gologit program and is backward compatible with both it and gologit29 but offers several additional powerful options.
10 citations
TL;DR: In this paper, models and estimention procedures for linear regression models in discrete distributions when the regression contains both fixed and random effects are given for discrete variables with typically a small number of possible outcomes such as occurs in ordinal regression.
Abstract: Models and estimention procedures are given for linear regression models in discrete distributions when the regression contains both fixed and random effects. The methods are developed for discrete variables with typically a small number of possible outcomes such as occurs in ordinal regression. The method is applied to a problem arising in the comparison of microbiological test methods.
10 citations
01 Jun 2017
TL;DR: In this article, a novel modeling approach for long-term electricity demand forecasting is introduced via the application of ordinal regression analysis, which has achieved a minimum Mean Absolute Percentage Error (MAPE) of 2.14%.
Abstract: Electricity demand forecasting constitutes a critical process in the operation and planning procedures of power networks that highly affects the decisions of utility providers and energy policy makers. Accurate forecasting is vital in reducing costs, related to excess electricity storage and infrastructures, and achieving enhanced power security and stability. A novel modeling approach for long-term electricity demand forecasting is introduced via the application of ordinal regression analysis. Annual forecasts of the total net electricity demand in the Greek interconnected power system are provided for the years 2016–2025. The Gross Domestic Product (GDP) has been identified as the greatest influential parameter on the evolution of electricity demand. Furthermore, the forecasting model has achieved a minimum Mean Absolute Percentage Error (MAPE) of 2.14%. The extracted forecasts indicate a constant increase of the total net electricity demand in Greece as a result of the expected economic growth during the upcoming years.
10 citations
TL;DR: In this article, a Bayesian nonparametric framework for modeling ordinal regression relationships, which evolve in discrete time, is developed for estimating dynamically evolving relationships between age, length, and maturity, the latter recorded on an ordinal scale.
Abstract: We develop a Bayesian nonparametric framework for modeling ordinal regression relationships, which evolve in discrete time. The motivating application involves a key problem in fisheries research on estimating dynamically evolving relationships between age, length, and maturity, the latter recorded on an ordinal scale. The methodology builds from nonparametric mixture modeling for the joint stochastic mechanism of covariates and latent continuous responses. This approach yields highly flexible inference for ordinal regression functions while at the same time avoiding the computational challenges of parametric models that arise from estimation of cut-off points relating the latent continuous and ordinal responses. A novel-dependent Dirichlet process prior for time-dependent mixing distributions extends the model to the dynamic setting. The methodology is used for a detailed study of relationships between maturity, age, and length for Chilipepper rockfish, using data collected over 15 years along th...
10 citations