The spatial model has been in use in political science for close to 30 years, and in that period it has achieved a place of prime importance as our paradigm of the process of candidatechoice used by voters. For much of this time political scientists have estimated models of candidatechoice using binary logit or probit, even in cases where there were more than two choices facing voters. Recently...

This paper investigates the application of a nested logit model to urban transit networks where every set of competitive transit lines is described by a particular graph structure called hyperpath. It shows that the nested logit model transcends the inherent limitations of the simple logit model while retaining the algorithmic advantages similar to that obtained with the ordinary logit model fo...

Land-use change models are used to explore the dynamics and drivers of land-use/landcover change and to inform policies affecting such change. A broad array of applications and modeling methods are available and each type has certain advantages and disadvantages depending on the objective of the research. This work presents an approach combining cellular automata (CA) model and supported vector...

logit models are one of the most important discrete choice models and they play an important role in describing decision makers’ choices among alternatives. in this paper the multi-nominal logit models has been used in mode choice modeling of isfahan. despite the availability of different mathematical computer programs there are not so many programs available for estimating discrete choice mode...

This paper compares, for a microfinance institution, the performance of two individual classification models: Logistic Regression (Logit) and Multi-Layer Perceptron Neural Network (MLP), to evaluate the credit risk problem and discriminate good creditors from bad ones. Credit scoring systems are currently in common use by numerous financial institutions worldwide. However, credit scoring using ...

We evaluate three link functions (square root, logit, and copula) and Matérn kernel in the kernel-based estimation of reflectance spectra of the Munsell Matte collection in the 400-700 nm region. We estimate reflectance spectra from RGB camera responses in case of real and simulated responses and show that a combination of link function and a kernel regression model with a Matérn kernel decreas...

A challenge for the efficient operation of power systems and wind farms is the occurrence of wind power ramps, which are sudden large changes in the power output from a wind farm. This paper considers the probabilistic forecasting of a ramp event, defined as exceedance beyond a specified threshold. We directly model the exceedance probability using autoregressive logit models fitted to the chan...