We show that the distributions of random coefficients in various discrete choice models are nonparametrically identified. Our identification results apply to static discrete choice models including binary logit, multinomial logit, nested logit, and probit models as well as to dynamic programming discrete choice models. In these models the only key condition we need to verify for identification ...

We develop an extension of the familiar linear mixed logit model to allow for the direct estimation of parametric non-linear functions defined over structural parameters. Classic applications include the estimation of coefficients of utility functions to characterize risk attitudes and discounting functions to characterize impatience. There are several unexpected benefits of this extension, apa...

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...

The objective of this study is twofold: 1) to investigate an impact of software quality attributes of a learning management system (LMS) on students’ satisfaction in both the personal computer and the smartphone settings; 2) to examine whether LMS quality factors have different impacts in the smartphone context and the PC context. We explore five quality attributes of LMS (capability, usability...

Point process models are reviewed and discussed for assessing the effects of environmental risk factors on asthma attacks. It is pointed out that the logit model and proportional intensity model are useful for analyzing the data based on the diaries recorded consecutively during several months or during a few years. Some covariates that seems to influence upon asthmatics are explored using thes...

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 presents a financial distress prediction model that combines the approaches of neural network learning and logit analysis. This combination can retain the advantages and avoid the disadvantages of the two kinds of approaches in solving such a problem. The radial basis function network (RBFN) is adopted to construct the prediction model. The architecture of RBFN allows the grouping o...