David S. Bunch

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Dynamic, disaggregate choice models which use longitudinal data axe known to have clear advantages over cross-sectional models, but they also have their own unique estimation problems. The correlation among unobserved error components ("heterogeneity") that is likely to exist in such data sets can be the source of apparent state dependence, but true state(More)
We compare multinomial logit and mixed logit models for data on California households’ revealed and stated preferences for automobiles. The stated preference data elicited households’ preferences among gas, electric, methanol, and CNG vehicles with various attributes. The mixed logit models provide a much better fit to these data, and forecasting exercises(More)
Random utility models often involve terms which represent alternative-specific errors, and the main attractive feature of the multinomial probit (MNP) model is that it allows a rather general covariance structure for these errors. However, since observed choices only reveal information regarding utility differences, and since scale cannot be determined, not(More)
We present FORTRAN 77 subroutines that solve statistical parameter estimation problems for general nonlinear models, e.g., nonlinear least-squares, maximum likelihood, maximum quasi-likelihood, generalized nonlinear least-squares, and some robust fitting problems. The accompanying test examples include members of the generalized linear model family,(More)
The authors compare a variety of different designs for choice experiments that satisfy the properties of the MNL model. These include both previously published and new design approaches: the latter are based on “shifting” design codes to create choice alternatives that maximize the number of attribute comparisons. An analysis of relative statistical(More)
In this paper we revisit various important issues relating to practical estimation of the multinomial probit model, using an empirical analysis of car ownership as a test case. To provide context, a brief literature review of empirical probit studies is included. Estimates are obtained for a full range of model specifications, including models with random(More)
The standard implementation of enzyme-linked immunosorbent assay (ELISA) for single analytes can lead to false conclusions if cross reacting compounds are present in the sample. This paper discusses the extension of the usual four-parameter logistic model for ELISA to the case of multiple cross-reacting analytes. The use of the extended model in(More)
This paper shows how to obtain accuracy and efficiency in an ELISA analysis by allocating the wells on a 96-well microplate between calibration and determination of unknowns, and by choosing the known concentrations for calibration. The method also can determine how much is lost in precision by using a convenient but non-optimal protocol.