Trading between perceived risks and benefits related to biosimilar biological treatment in Crohn’s disease; discrete choice experiment among gastroenterologists

Objective: The objective of the study is to explore preferences of gastroenterologists for biosimilar drugs in Crohn’s Disease and reveal trade-offs between the perceived risks and benefits related to biosimilar drugs. Method: Discrete choice experiment was carried out involving 51 Hungarian gastroenterologists in May, 2014. The following attributes were used to describe hypothetical choice sets: 1) type of the treatment (biosimilar/originator) 2) severity of disease 3) availability of continuous medicine supply 4) frequency of the efficacy check-ups. Multinomial logit model was used to differentiate between three attitude types: 1) always opting for the originator 2) willing to consider biosimilar for biological-naïve patients only 3) willing to consider biosimilar treatment for both types of patients. Conditional logit model was used to estimate the probabilities of choosing a given profile. Results: Men, senior consultants, working in IBD center and treating more patients are more likely to willing to consider biosimilar for biological-naïve patients only. Treatment type (originator/biosimilar) was the most important determinant of choice for patients already treated with biologicals, and the availability of continuous medicine supply in the case biological-naïve patients. The probabilities of choosing the biosimilar with all the benefits offered over the originator under current reimbursement conditions are 89% vs 11% for new patients, and 44% vs 56% for patients already treated with biological. Conclusions: Gastroenterologists were willing to trade between perceived risks and benefits of biosimilars. The continuous medical supply would be one of the major benefits of biosimilars. However, benefits offered in the scenarios do not compensate for the change from the originator to the biosimilar treatment of patients already treated with biologicals.

Essays on financial crises

This dissertation analyzes recent financial crises in developed and developing countries. The research emphasizes the effects of institutional factors on the international banking and currency crises and their output losses. ^ Chapter two examines the roles of regulation, supervision, and countries' institutional environment in determining the probability of banking crises for a panel of fifteen developed countries from 1975 to 1998. The results from a multivariate logit model indicated that countries with greater government involvement, less capital standard requirements, and lower lending limits on a single borrower are associated with a higher probability of banking crises. ^ Chapter three studies whether output loss in banking crisis differs in market-based or bank-based financial systems. Using existing banking crisis data for sixty-nine countries during 1970–1999, we investigate whether the underlying financial system affects the output loss. The results show that output losses are more serious in market-based economies than those in bank-based economies. Longer crisis duration tends to increase the output losses in banking crises. Finally, countries with deposit insurance and strict law enforcement have less output losses. ^ Chapter four uses macroeconomic and institutional measures to explain the extent of exchange rate depreciation and the decline in stock prices for emerging countries affected by the Mexican currency crisis of 1994–95. The results show that countries with more government budget deficits, and worse reserve adequacies tend to experience large exchange rate depreciation. The institutional measures do not explain much the extent of both the exchange rate depreciation and the decline in stock prices. ^

The effects of government disability benefits, accessibility laws and rehabilitation on employment choices by individuals with spinal cord injuries

Greater inclusion of individuals with disabilities into mainstream society is an important goal for society. One of the best ways to include individuals is to actively promote and encourage their participation in the labor force. Of all disabilities, it is feasible to assume that individual with spinal cord injuries can be among the most easily mainstreamed into the labor force. However, less that fifty percent of individuals with spinal cord injuries work. ^ This study focuses on how disability benefit programs, such as Social Security Disability Insurance, and Worker's Compensation, the Americans with Disabilities Act and rehabilitation programs affect employment decisions. The questions were modeled using utility theory with an augmented expenditure function and indifference theory. Statically, Probit, Logit, predicted probability, and linear regressions were used to analyze these questions. Statistical analysis was done on the probability of working, ever attempting to work after injury, and on the number of years after injury that work was first attempted and the number of hours worked per week. The data utilized were from the National Spinal Cord Injury Database and the Spinal Cord Injuries and Labor Database. The Spinal Cord Injuries and Labor Database was created specifically for this study by the author. Receiving disability benefits decreased the probability of working, of ever attempting to work, increased the number of years after injury before the first work attempt was made, and decreased the number of hours worked per week for those individuals working. These results were all statistically significant. The Americans with Disabilities Act decrease the number of years before an individual made a work attempt. The decrease is statistically significant. The amount of rehabilitation had a significant positive effect for male individuals with low paraplegia, and significant negative effect for individuals with high tetraplegia. For women, there were significant negative effects for high tetraplegia and high paraplegia. ^ This study finds that the financial disincentives of receiving benefits are the major determinants of whether an individual with a spinal cord injury returns to the labor force. Policies are recommended that would decrease the disincentive. ^

Investigation of the causes of serious injury and fatal crashes: An analysis using econometric models

The rate of fatal crashes in Florida has remained significantly higher than the national average for the last several years. The 2003 statistics from the National Highway Traffic Safety Administration (NHTSA), the latest available, show a fatality rate in Florida of 1.71 per 100 million vehicle-miles traveled compared to the national average of 1.48 per 100 million vehicle-miles traveled. The objective of this research is to better understand the driver, environmental, and roadway factors that affect the probability of injury severity in Florida. ^ In this research, the ordered logit model was used to develop six injury severity models; single-vehicle and two-vehicle crashes on urban freeways and urban principal arterials and two-vehicle crashes at urban signalized and unsignalized intersections. The data used in this research included all crashes that occurred on the state highway system for the period from 2001 to 2003 in the Southeast Florida region, which includes the Miami-Dade, Broward and Palm Beach Counties.^ The results of the analysis indicate that the age group and gender of the driver at fault were significant factors of injury severity risk across all models. The greatest risk of severe injury was observed for the age groups 55 to 65 and 66 and older. A positive association between injury severity and the race of the driver at fault was also found. Driver at fault of Hispanic origin was associated with a higher risk of severe injury for both freeway models and for the two-vehicle crash model on arterial roads. A higher risk of more severe injury crash involvement was also found when an African-American was the at fault driver on two-vehicle crashes on freeways. In addition, the arterial class was also found to be positively associated with a higher risk of severe crashes. Six-lane divided arterials exhibited the highest injury severity risk of all arterial classes. The lowest severe injury risk was found for one way roads. Alcohol involvement by the driver at fault was also found to be a significant risk of severe injury for the single-vehicle crash model on freeways. ^

Statistical analysis and meta-analysis of microarray data

The microarray technology provides a high-throughput technique to study gene expression. Microarrays can help us diagnose different types of cancers, understand biological processes, assess host responses to drugs and pathogens, find markers for specific diseases, and much more. Microarray experiments generate large amounts of data. Thus, effective data processing and analysis are critical for making reliable inferences from the data. ^ The first part of dissertation addresses the problem of finding an optimal set of genes (biomarkers) to classify a set of samples as diseased or normal. Three statistical gene selection methods (GS, GS-NR, and GS-PCA) were developed to identify a set of genes that best differentiate between samples. A comparative study on different classification tools was performed and the best combinations of gene selection and classifiers for multi-class cancer classification were identified. For most of the benchmarking cancer data sets, the gene selection method proposed in this dissertation, GS, outperformed other gene selection methods. The classifiers based on Random Forests, neural network ensembles, and K-nearest neighbor (KNN) showed consistently god performance. A striking commonality among these classifiers is that they all use a committee-based approach, suggesting that ensemble classification methods are superior. ^ The same biological problem may be studied at different research labs and/or performed using different lab protocols or samples. In such situations, it is important to combine results from these efforts. The second part of the dissertation addresses the problem of pooling the results from different independent experiments to obtain improved results. Four statistical pooling techniques (Fisher inverse chi-square method, Logit method. Stouffer's Z transform method, and Liptak-Stouffer weighted Z-method) were investigated in this dissertation. These pooling techniques were applied to the problem of identifying cell cycle-regulated genes in two different yeast species. As a result, improved sets of cell cycle-regulated genes were identified. The last part of dissertation explores the effectiveness of wavelet data transforms for the task of clustering. Discrete wavelet transforms, with an appropriate choice of wavelet bases, were shown to be effective in producing clusters that were biologically more meaningful. ^

A methodology to estimate time varying user responses to travel time and travel time reliability in a road pricing environment

Road pricing has emerged as an effective means of managing road traffic demand while simultaneously raising additional revenues to transportation agencies. Research on the factors that govern travel decisions has shown that user preferences may be a function of the demographic characteristics of the individuals and the perceived trip attributes. However, it is not clear what are the actual trip attributes considered in the travel decision- making process, how these attributes are perceived by travelers, and how the set of trip attributes change as a function of the time of the day or from day to day. In this study, operational Intelligent Transportation Systems (ITS) archives are mined and the aggregated preferences for a priced system are extracted at a fine time aggregation level for an extended number of days. The resulting information is related to corresponding time-varying trip attributes such as travel time, travel time reliability, charged toll, and other parameters. The time-varying user preferences and trip attributes are linked together by means of a binary choice model (Logit) with a linear utility function on trip attributes. The trip attributes weights in the utility function are then dynamically estimated for each time of day by means of an adaptive, limited-memory discrete Kalman filter (ALMF). The relationship between traveler choices and travel time is assessed using different rules to capture the logic that best represents the traveler perception and the effect of the real-time information on the observed preferences. The impact of travel time reliability on traveler choices is investigated considering its multiple definitions. It can be concluded based on the results that using the ALMF algorithm allows a robust estimation of time-varying weights in the utility function at fine time aggregation levels. The high correlations among the trip attributes severely constrain the simultaneous estimation of their weights in the utility function. Despite the data limitations, it is found that, the ALMF algorithm can provide stable estimates of the choice parameters for some periods of the day. Finally, it is found that the daily variation of the user sensitivities for different periods of the day resembles a well-defined normal distribution.

Variables affecting the success of older applicants for employment in a public organization: An age -cued cognitive model of administrative decision-making

The dissertation takes a multivariate approach to answer the question of how applicant age, after controlling for other variables, affects employment success in a public organization. In addition to applicant age, there are five other categories of variables examined: organization/applicant variables describing the relationship of the applicant to the organization; organization/position variables describing the target position as it relates to the organization; episodic variables such as applicant age relative to the ages of competing applicants; economic variables relating to the salary needs of older applicants; and cognitive variables that may affect the decision maker's evaluation of the applicant. ^ An exploratory phase of research employs archival data from approximately 500 decisions made in the past three years to hire or promote applicants for positions in one public health administration organization. A logit regression model is employed to examine the probability that the variables modify the effect of applicant age on employment success. A confirmatory phase of the dissertation is a controlled experiment in which hiring decision makers from the same public organization perform a simulated hiring decision exercise to evaluate hypothetical applicants of similar qualifications but of different ages. The responses of the decision makers to a series of bipolar adjective scales add support to the cognitive component of the theoretical model of the hiring decision. A final section contains information gathered from interviews with key informants. ^ Applicant age has tended to have a curvilinear relationship with employment success. For some positions, the mean age of the applicants most likely to succeed varies with the values of the five groups of moderating variables. The research contributes not only to the practice of public personnel administration, but is useful in examining larger public policy issues associated with an aging workforce. ^

Treatment attrition among racial /ethnic minority adolescents participating in a brief motivational intervention for substance use problems: The influence of individual, social, cultural, and treatment factors

The present study has the primary aim of examining the predictors of treatment attrition among racial/ethnic minority adolescents with substance use problems. This study explores the potential differential influence of specific individual, social, cultural, and treatment factors on treatment attrition within three racial/ethnic subgroups of adolescents. Participants: A unique feature of the study is the use of a racial/ethnic minority sample (N=453), [U.S.-born Hispanics (n = 262), Foreign-born Hispanics (n = 117), and African-Americans (n = 74)]. Multivariate logit analyses were used to examine the influence of specific factors on treatment attrition among the full sample of adolescents, as well as within each racial/ethnic subgroup. Consistent with expectations, multivariate logit analyses reveal that, the specific factors associated with attrition varied across the three racial/ethnic subgroups. Having parents with problem substance use, being on the waitlist, and being court mandated to treatment emerged as predictors of attrition among the US-born Hispanics, while only Conduct Disorder was significantly associated with greater attrition among foreign-born Hispanics. Finally, among African-Americans, parental crack/cocaine use, parental abstinence from alcohol, and being on the waitlist were predictive of attrition. Multiple factors were associated with treatment attrition among racial/ethnic minority adolescents with specific factors differentially predicting attrition within each racial/ethnic subgroup. African-American youth were more than twice as likely as their Hispanic counterparts to leave treatment prematurely. It is critically important to understand the predictors of attrition among racial/ethnic minority youth in order to better meet the needs of youth most at risk of dropping out. ^

Treatment attrition among racial/ethnic minority adolescents participating in a brief motivational intervention for substance use problems : the influence of individual, social, cultural, and treatment factors

The present study has the primary aim of examining the predictors of treatment attrition among racial/ethnic minority adolescents with substance use problems. This study explores the potential differential influence of specific individual, social, cultural, and treatment factors on treatment attrition within three racial/ethnic subgroups of adolescents. Participants: A unique feature of the study is the use of a racial/ethnic minority sample (N=453), [U.S.-born Hispanics (n = 262), Foreign-born Hispanics (n = 117), and African- Americans (n = 74)]. Multivariate logit analyses were used to examine the influence of specific factors on treatment attrition among the full sample of adolescents, as well as within each racial/ethnic subgroup. Consistent with expectations, multivariate logit analyses reveal that, the specific factors associated with attrition varied across the three racial/ethnic subgroups. Having parents with problem substance use, being on the waitlist, and being court mandated to treatment emerged as predictors of attrition among the US-born Hispanics, while only Conduct Disorder was significantly associated with greater attrition among foreign-born Hispanics. Finally, among African-Americans, parental crack/cocaine use, parental abstinence from alcohol, and being on the waitlist were predictive of attrition. Multiple factors were associated with treatment attrition among racial/ethnic minority adolescents with specific factors differentially predicting attrition within each racial/ethnic subgroup. African-American youth were more than twice as likely as their Hispanic counterparts to leave treatment prematurely. It is critically important to understand the predictors of attrition among racial/ethnic minority youth in order to better meet the needs of youth most at risk of dropping out.

The effects of government disability benefits, accessibility laws and rehabilitation on employment choices by individuals with spinal cord injuries

Greater inclusion of individuals with disabilities into mainstream society is an important goal for society. One of the best ways to include individuals is to actively promote and encourage their participation in the labor force. Of all disabilities, it is feasible to assume that individual with spinal cord injuries can be among the most easily mainstreamed into the labor force. However, less that fifty percent of individuals with spinal cord injuries work. This study focuses on how disability benefit programs, such as Social Security Disability Insurance, and Worker's Compensation, the Americans with Disabilities Act and rehabilitation programs affect employment decisions. The questions were modeled using utility theory with an augmented expenditure function and indifference theory. Statically, Probit, Logit, predicted probability, and linear regressions were used to analyze these questions. Statistical analysis was done on the probability of working, ever attempting to work after injury, and on the number of years after injury that work was first attempted and the number of hours worked per week. The data utilized were from the National Spinal Cord Injury Database and the Spinal Cord Injuries and Labor Database. The Spinal Cord Injuries and Labor Database was created specifically for this study by the author. Receiving disability benefits decreased the probability of working, of ever attempting to work, increased the number of years after injury before the first work attempt was made, and decreased the number of hours worked per week for those individuals working. These results were all statistically significant. The Americans with Disabilities Act decrease the number of years before an individual made a work attempt. The decrease is statistically significant. The amount of rehabilitation had a significant positive effect for male individuals with low paraplegia, and significant negative effect for individuals with high tetraplegia. For women, there were significant negative effects for high tetraplegia and high paraplegia. This study finds that the financial disincentives of receiving benefits are the major determinants of whether an individual with a spinal cord injury returns to the labor force. Policies are recommended that would decrease the disincentive.

Understanding Immigrants' Travel Behavior in Florida: Neighborhood Effects and Behavioral Assimilation

The goal of this study was to develop Multinomial Logit models for the mode choice behavior of immigrants, with key focuses on neighborhood effects and behavioral assimilation. The first aspect shows the relationship between social network ties and immigrants’ chosen mode of transportation, while the second aspect explores the gradual changes toward alternative mode usage with regard to immigrants’ migrating period in the United States (US). Mode choice models were developed for work, shopping, social, recreational, and other trip purposes to evaluate the impacts of various land use patterns, neighborhood typology, socioeconomic-demographic and immigrant related attributes on individuals’ travel behavior. Estimated coefficients of mode choice determinants were compared between each alternative mode (i.e., high-occupancy vehicle, public transit, and non-motorized transport) with single-occupant vehicles. The model results revealed the significant influence of neighborhood and land use variables on the usage of alternative modes among immigrants. Incorporating these indicators into the demand forecasting process will provide a better understanding of the diverse travel patterns for the unique composition of population groups in Florida.

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Revisiting a destination: A tourist’s choice model : A case study of Sweden

This paper documents the design and results of a study on tourists’ decision-making about destinations in Sweden. For this purpose, secondary data, available from surveys were used to identify which type of individual has the highest probability to revisit a destination and what are influencing factors to do so. A binary logit model is applied. The results show that very important influencing factors are the length of stay as well as the origin of the individual. These results could be useful for a marketing organization as well as for policy, to develop strategies to attract the most profitable tourism segment. Therefore, it can also be a great support for sustainable tourism development, where the main focus does not has priority on increasing number of tourists.

The effect of alcohol consumption on household income in Ireland

This paper presents a study of the effects of alcohol consumption on household income in Ireland using the Slán National Health and Lifestyle Survey 2007 dataset, accounting for endogeneity and selection bias. Drinkers are categorised into one of four categories based on the recommended weekly drinking levels by the Irish Health Promotion Unit; those who never drank, non-drinkers, moderate and heavy drinkers. A multinomial logit OLS Two Step Estimate is used to explain individual's choice of drinking status and to correct for selection bias which would result in the selection into a particular category of drinking being endogenous. Endogeneity which may arise through the simultaneity of drinking status and income either due to the reverse causation between the two variables, income affecting alcohol consumption or alcohol consumption affecting income, or due to unobserved heterogeneity, is addressed. This paper finds that the household income of drinkers is higher than that of non-drinkers and of those who never drank. There is very little difference between the household income of moderate and heavy drinkers, with heavy drinkers earning slightly more. Weekly household income for those who never drank is €454.20, non-drinkers is €506.26, compared with €683.36 per week for moderate drinkers and €694.18 for heavy drinkers.