Construction of experimental designs for mixed logit models allowing for correlation across choice observations

In each stated choice (SC) survey, there is an underlying experimental design from which the hypothetical choice situations are determined. These designs are constructed by the analyst, with several different ways of constructing these designs having been proposed in the past. Recently, there has been a move from so-called orthogonal designs to more efficient designs. Efficient designs optimize the design such that the data will lead to more reliable parameter estimates for the model under consideration. The main focus has been on the multinomial logit model, however this model is unable to take the dependency between choice situations into account, while in a stated choice survey usually multiple choice situations are presented to a single respondent. In this paper, we extend the literature by focusing on the panel mixed logit (ML) model with random parameters, which can take the above mentioned dependency into account. In deriving the analytical asymptotic variance-covariance matrix for the panel ML model, used to determine the efficiency of a design, we show that it is far more complex than the crosssectional ML model (assuming independent choice observations). Case studies illustrate that it matters for which model the design is optimized, and that it seems that a panel ML model SC experiment needs less respondents than a cross-sectional ML experiment for the same level of reliability of the parameter estimates

Confidence intervals of willingness-­‐to-­‐pay for random coefficient logit models

Random coefficient logit (RCL) models containing random parameters are increasingly used for modelling travel choices. Willingness-to-pay (WTP) measures, such as the value of travel time savings (VTTS) are, in the case of such RCL models, ratios of random parameters. In this paper we apply the Delta method to compute the confidence intervals of such WTP measures, taking into account the variancecovariance matrix of the estimates of the distributional parameters. Compared to simulation methods such as proposed by Krinsky and Robb, the Delta method is able to avoid some of the simulations by deriving partly analytical expressions for the standard errors. Examples of such computations are shown for different combinations of random distributions

Incorporating model uncertainty into the generation of efficient stated choice experiments: A model averaging approach

Stated choice (SC) studies typically rely on the use of an underlying experimental design to construct the hypothetical choice situations shown to respondents. These designs are constructed by the analyst, with several different ways of constructing these designs having been proposed in the past. Recently, there has been a move from so-called orthogonal designs to more efficient designs. Efficient designs optimize the design such that the data will lead to more reliable parameter estimates for the model under consideration. The literature dealing with the generation of efficient designs has examined and largely solved the issue of a requirement for a prior knowledge of the parameter estimates that will be obtained post data collection. Nevertheless, problems related to the fact that the efficiency of a SC experiment is related to the variance-covariance matrix of the model to be estimated and that different econometric models will have different variance-covariance matrix, thus resulting in different levels of efficiency for the same design, has yet to be addressed. In this paper, we propose the use of a model averaging process over different econometric models to solve this problem. Via the use of a case study, we show that designs generated using the model averaging process prove robust to different model estimation as well as provide decent levels of protection against biased parameter estimates relative to designs generated specifically for a given model type

Endomyocardial fibrosis at autopsy in Cape Town

The pathology of 3 cases of endomyocardial fibrosis (EMF) encountered at autopsy in Cape Town is described. The first case of EMF in a non-White permanent resident of South Africa is documented. The macroscopic features allow distinction between EMF and the usual form of idiopathic cardiomyopathy seen in Cape Town. There is a small potential reservoir of patients in Cape Town with typical EMF, but the presence of a coexistent (valvular) lesion may lead to such hearts being ignored in studies of idiopathic cardiomyopathy.S. Afr. Med. J. 48, 1363 (1974)

Detecting dominancy and accounting for scale differences when using stated choice data to estimate logit models

Stated choice surveys have been used for several decades to estimate preferences of agents using choice models, and are widely applied in the transportation domain. Typically orthogonal or efficient experimental designs underlie such surveys. These experimental designs may suffer from choice tasks containing a dominant alternative, which we show is problematic because it affects scale and therefore may bias parameter estimates. We propose a new measure based on minimum regret to calculate dominancy and automatically detect such choice tasks in an experimental design. This measure is then used to define a new experimental design type that ensures tradeoffs within the design. Finally, we propose a new regret-scaled multinomial logit model that takes the level of dominancy within a choice task into account. Results using simulated and empirical data show that the presence of dominant alternatives can bias model estimates, but that making scale a function of a smooth approximation of normalised minimum regret can properly account for scale differences without the need to remove choice tasks with dominant alternatives from the dataset

Efficient stated choice experiments for estimating nested logit models

The allocation of combinations of attribute levels to choice situations in stated choice (SC) experiments can have a significant influence upon the resulting study outputs once data is collected. Recently, a small but growing stream of research has looked at using what have become known as efficient SC experimental designs to allocate the attribute levels to choice situations in a manner designed to produce better model outcomes. This research stream has shown that the use of efficient SC designs can lead to improvements in the reliability of parameter estimates derived from discrete choice models estimated on SC data for a given sample size. Unlike orthogonal designs, however, efficient SC experiments are generated in such a manner that their efficiency is related to the econometric model that is most likely to be estimated once the choice data is collected. To date, most of the research on efficient SC designs has assumed an MNL model format. In this paper, we generate efficient SC experiments for nested logit models and compare and contrast these with designs specifically generated assuming an MNL model form. We find that the overall efficiency of the design is maximized only when the model assumed in generating the design is the model that is fitted during estimation

A comparison of algorithms for generating efficient choice experiments

Stated choice (SC) studies typically rely on the use of an underlying experimental design to construct the hypothetical choice situations shown to respondents. These designs are constructed by the analyst, with several different ways of constructing these designs having been proposed in the past. Recently, there has been a move from so-called orthogonal designs to more efficient designs. Efficient designs optimize the design such that the data will lead to more reliable parameter estimates for the model under consideration. The literature dealing with the generation of efficient designs has examined and largely solved the issue of a requirement for a prior knowledge of the parameter estimates that will be obtained post data collection. However, unlike orthogonal designs, the efficient design methodology requires the evaluation of a number of designs, and hence is computationally expensive to undertake. As such, the literature has suggested and implemented a number of algorithms to locate efficient designs for SC experiments. In this paper, we compare and contrast the performance of these algorithms as well as introduce two new algorithms

Paroxysmal Kinesigenic Dyskinesia: First Molecularly Confirmed Case from Africa

Background: Paroxysmal kinesigenic dyskinesia (PKD) is a movement disorder, with an excellent response to carbamazepine treatment. It has been described in various populations, but not yet in an African population. Case report: In a patient who reported to clinic with side effects of carbamazepine, PRRT2 gene screening was performed based on a clinical history compatible with PKD. A common PRRT2 mutation was identified in this patient, hereby the first genetically confirmed PRRT2-associated PKD in Africa. Discussion: Reporting genetic confirmation of an unusual movement disorder from an equally unusual location shows the wide geographical distribution of PRRT2-associated disease. It also illustrates recognizability of this treatable disorder where the easiest accessible diagnostic tool is neurological history and examination

Macrostate Data Clustering

We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system. A "minimum uncertainty criterion" determines the linear transformation from eigenvectors to cluster-defining window functions. Eigenspectrum gap and cluster certainty conditions identify the proper number of clusters. The physically motivated fuzzy representation and associated uncertainty analysis distinguishes macrostate clustering from spectral partitioning methods. Macrostate data clustering solves a variety of test cases that challenge other methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral graph theory, dynamic eigenvectors, machine learning, macrostates, classificatio

Relativistic versus Nonrelativistic Optical Potentials in A(e,e'p)B Reactions

We investigate the role of relativistic and nonrelativistic optical potentials used in the analysis of ($e,e'p$) data. We find that the relativistic calculations produce smaller ($e,e'p$) cross sections even in the case in which both relativistic and nonrelativistic optical potentials fit equally well the elastic proton--nucleus scattering data. Compared to the nonrelativistic impulse approximation, this effect is due to a depletion in the nuclear interior of the relativistic nucleon current, which should be taken into account in the nonrelativistic treatment by a proper redefinition of the effective current operator.Comment: Added one new figure, the formalism section has been enlarged and the list of references updated. Added one appendix. This version will appear in Phys. Rev. C. Revtex 3.0, 6 figures (not included). Full postscript version of the file and figures available at http://www.nikhefk.nikhef.nl/projects/Theory/preprints