Results 191 to 200 of about 58,795 (233)
Some of the next articles are maybe not open access.
Understanding and interpreting generalized ordered logit models
Journal of Mathematical Sociology, 2016ABSTRACTWhen outcome variables are ordinal rather than continuous, the ordered logit model, aka the proportional odds model (ologit/po), is a popular analytical method. However, generalized ordered logit/partial proportional odds models (gologit/ppo) are often a superior alternative.
Richard Williams
exaly +2 more sources
2014
The ordered logit model is a regression model for an ordinal response variable. The model is based on the cumulative probabilities of the response variable: in particular, the logit of each cumulative probability is assumed to be a linear function of the covariates with regression coefficients constant across response categories.
Grilli, Leonardo, Rampichini, Carla
openaire +2 more sources
The ordered logit model is a regression model for an ordinal response variable. The model is based on the cumulative probabilities of the response variable: in particular, the logit of each cumulative probability is assumed to be a linear function of the covariates with regression coefficients constant across response categories.
Grilli, Leonardo, Rampichini, Carla
openaire +2 more sources
Simple inference in multinomial and ordered logit
Econometric Reviews, 1998This paper provides some simple methods of interpreting the coefficients in multinomial logit and ordered logit models. These methods are summarized in Propositions concerning the magnitudes, signs, and patterns of partial derivatives of the outcome probabilities with respect to the exogenousvariables.
David L. Crawford +2 more
openaire +1 more source
Perceptual Mapping Using Ordered Logit Analysis
Marketing Science, 1990This study is to present a new method for constructing a perceptual map based on logit analysis. This is an extension of the explosion logit model of an individual choice to a problem of perceptual mapping giving rise to advantages over existing methods in various aspects. Firstly, input data format is flexible and user-friendly.
openaire +1 more source
Efficiency Gains in Rank‐ordered Multinomial Logit Models
Oxford Bulletin of Economics and Statistics, 2017AbstractThis paper considers estimation of discrete choice models when agents report their ranking of the alternatives (or some of them) rather than just the utility maximizing alternative. We investigate the parametric conditional rank‐ordered Logit model. We show that conditions for identification do not change even if we observe ranking.
Arie Beresteanu, Federico Zincenko
openaire +1 more source
Logit and Probit: Ordered and Multinomial Models
Journal of the American Statistical Association, 2003(2003). Logit and Probit: Ordered and Multinomial Models. Journal of the American Statistical Association: Vol. 98, No. 463, pp. 775-776.
openaire +1 more source
A note on the use of rank‐ordered logit models for ordered response categories
British Journal of Mathematical and Statistical Psychology, 2022Abstract Models for rankings have been shown to produce more efficient estimators than comparable models for first/top choices. The discussions and applications of these models typically only consider unordered alternatives. But these models can be usefully adapted to the case where a respondent ranks a set of ordered alternatives ...
openaire +3 more sources
Die Analyse von Wählerpräferenzen mit Rank Ordered Logit
2007„Wenn am nachsten Sonntag Bundestagswahl ware, welche Partei wurden Sie wahlen?“ - Die so genannte Wahlabsicht ist eine der am haufigsten erhobenen und am meisten studierten Variablen in der empirischen Wahlforschung. Zur statistischen Analyse wird dabei zunehmend auf Verfahren wie das Multinomial Logit1 Modell zuruckgegriffen, das in der Wahlforschung
Herrmann, Michael +3 more
openaire +2 more sources
A mixed grouped response ordered logit count model framework
Analytic Methods in Accident Research, 2018The study proposes and estimates a new econometric framework for analysing crash count events labeled as the Mixed Grouped Response Ordered Logit Count model. The proposed framework relates the crash count propensity to the observed counts directly while also accommodating for heteroscedasticity and unobserved heterogeneity.
Yasmin, Shamsunnahar, Eluru, Naveen
openaire +2 more sources