Search results for "Generalised Tobit model"
showing 3 items of 3 documents
A flexible approach for modelling a proportion response variable: Loss given default
2016
Loss given default (LGD) is a proportion of a credit exposure that is lost if the obligor defaults on a loan. Response variable LGD contains values between 0 and 1 including both 0 and 1, where 0 means that the balance is fully recovered while 1 means total loss of exposure at default. This article addresses two alternative semi parametric approaches for modelling loss given default, which is measured on the interval [0,1]. The class of models are very flexible and can accommodate skewness and bimodal characteristics of LGD data. The dependence of the predictors of each of the parameters (of the proposed model distribution for LGD) on explanatory variables can be additive P-splines, regress…
Modelling a proportion response variable using generalised additive models for location scale and shape
2015
In this paper two alternative approaches are proposed to model a response variable Y measured on the interval from zero to one, including both zero and one. The first proposed model employs a flexible four parameter distribution for 0 < Y < 1, for example a logit skew exponential power distribution, inflated by including point probabilities at 0 and 1. The second proposed model is a generalised Tobit model, obtained from a flexible four parameter distribution on (-infinity;+infinity), for example the skew exponential power distribution, by censoring below 0 and above 1. The proposed models are applied to a real data set and compared with current popular models.
Centile estimation for a proportion response variable.
2015
This paper introduces two general models for computing centiles when the response variable Y can take values between 0 and 1, inclusive of 0 or 1. The models developed are more flexible alternatives to the beta inflated distribution. The first proposed model employs a flexible four parameter logit skew Student t (logitSST) distribution to model the response variable Y on the unit interval (0, 1), excluding 0 and 1. This model is then extended to the inflated logitSST distribution for Y on the unit interval, including 1. The second model developed in this paper is a generalised Tobit model for Y on the unit interval, including 1. Applying these two models to (1-Y) rather than Y enables model…