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172
The Maximum Approximate Composite Marginal Likelihood (MACML) Estimation of the Multinomial Probitbased Unordered Response Choice Models
 TRANSPORTATION RESEARCH PART B
, 2011
"... The likelihood functions of multinomial probit (MNP)based choice models entail the evaluation of analyticallyintractable integrals. As a result, such models are usually estimated using maximum simulated likelihood (MSL) techniques. Unfortunately, for many practical situations, the computational co ..."
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Cited by 21 (15 self)
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The likelihood functions of multinomial probit (MNP)based choice models entail the evaluation of analyticallyintractable integrals. As a result, such models are usually estimated using maximum simulated likelihood (MSL) techniques. Unfortunately, for many practical situations, the computational cost to ensure good asymptotic MSL estimator properties can be prohibitive and practically infeasible as the number of dimensions of integration rises. In this paper, we introduce a maximum approximate composite marginal likelihood (MACML) estimation approach for MNP models that can be applied using simple optimization software for likelihood estimation. It also represents a conceptually and pedagogically simpler procedure relative to simulation techniques, and has the advantage of substantial computational time efficiency relative to the MSL approach. The paper provides a “blueprint” for the MACML estimation for a wide variety of MNP models.
Ambient air pollution and daily emergency department visits for ischemic stroke in
 Int J Occup Med Environ Health 2008
"... Abstract Objectives: To investigate the potential correlation between ambient air pollution exposure and emergency department (ED) visits for depression. Materials and Methods: A hierarchical clusters design was used to study 27 047 ED visits for depression in six cities in Canada. The data used in ..."
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Cited by 17 (3 self)
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Abstract Objectives: To investigate the potential correlation between ambient air pollution exposure and emergency department (ED) visits for depression. Materials and Methods: A hierarchical clusters design was used to study 27 047 ED visits for depression in six cities in Canada. The data used in the analysis contain the dates of visits, daily numbers of diagnosed visits, and daily mean concentrations of air pollutants as well as the meteorological factors. The generalized linear mixed models technique was applied to data analysis. Poisson models were fitted to the clustered counts of ED visits with a single air pollutant, temperature and relative humidity. Results: Statistically significant positive correlations were observed between the number of ED visits for depression and the air concentrations of carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ) and particulate matter (PM 10 ). The percentage increase in daily ED visits was 15.5% (95% CI: 8.023.5) for CO per 0.8 ppm and 20.0% (95% CI: 13.327.2) for NO 2 per 20.1 ppb, for same day exposure in the warm weather period (AprilSeptember). For PM 10 , the largest increase, 7.2% (95% CI: 3.011.6) per 19.4 ug/m 3 , was observed for the cold weather period (OctoberMarch). Conclusions: The results support the hypothesis that ED visits for depressive disorder correlate with ambient air pollution, and that a large majority of this pollution results from combustion of fossil fuels (e.g. in motor vehicles).
Actuarial statistics with generalized linear mixed models
 Insurance: Mathematics and Economics, 2006a
"... Over the last decade the use of generalized linear models (GLMs) in actuarial statistics received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh & Nelder (1989). Traditional GLMs however model a sample of independent random variables. Since actu ..."
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Cited by 14 (3 self)
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Over the last decade the use of generalized linear models (GLMs) in actuarial statistics received a lot of attention, starting from the actuarial illustrations in the standard text by McCullagh & Nelder (1989). Traditional GLMs however model a sample of independent random variables. Since actuaries very often have repeated measurements or longitudinal data (i.e. repeated measurements over time) at their disposal, this article considers statistical techniques to model such data within the framework of GLMs. Use is made of generalized linear mixed models (GLMMs) which model a transformation of the mean as a linear function of both fixed and random effects. The likelihood and Bayesian approaches to GLMMs are explained. The models are illustrated by considering classical credibility models and more general regression models for nonlife ratemaking in the context of GLMMs. Details on computation and implementation (in SAS and WinBugs) are provided.
Type I and Type II error under randomeffects misspecification in generalized linear mixed models
 Downloaded by [University of California, Los Angeles (UCLA)] at 12:08 05 April 2012
, 2007
"... Generalized linear mixed models (GLMM) have become a frequently used tool for the analysis of nonGaussian longitudinal data. Estimation is based on maximum likelihood theory which assumes that the underlying probability model is correctly specified. Recent research is showing that the results obtai ..."
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Cited by 13 (1 self)
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Generalized linear mixed models (GLMM) have become a frequently used tool for the analysis of nonGaussian longitudinal data. Estimation is based on maximum likelihood theory which assumes that the underlying probability model is correctly specified. Recent research is showing that the results obtained from these models are not always robust against departures from the assumptions on which these models are based. In the present work we have used simulations with a logistic randomintercept model to study the impact of misspecifying the randomeffects distribution on the type I and II errors of the tests for the mean structure in GLMM. We found that the misspecification can either increase or decrease the power of the tests, depending on the shape of the underlying randomeffects distribution, and it can considerably inflate the type I error rate. Additionally, we have found a theoretical result which states that, whenever a subset of fixedeffects parameters, not included in the randomeffects structure, equals zero, the corresponding maximum likelihood estimator will consistently estimate zero. This implies that under certain conditions a significant effect could be considered as a reliable result, even if the randomeffects distribution is misspecified.
A flexible spatially dependent discrete choice model: formulation and application to teenagers’ weekday recreational activity participation. Transportation Research Part B
"... This study proposes a simple and practical Composite Marginal Likelihood (CML) inference approach to estimate orderedresponse discrete choice models with flexible copulabased spatial dependence structures across observational units. The approach is applicable to data sets of any size, provides sta ..."
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Cited by 10 (6 self)
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This study proposes a simple and practical Composite Marginal Likelihood (CML) inference approach to estimate orderedresponse discrete choice models with flexible copulabased spatial dependence structures across observational units. The approach is applicable to data sets of any size, provides standard error estimates for all parameters, and does not require any simulation machinery. The combined copulaCML approach proposed here should be appealing for general multivariate modeling contexts because it is simple and flexible, and is easy to implement The ability of the CML approach to recover the parameters of a spatially ordered process is evaluated using a simulation study, which clearly points to the effectiveness of the approach. In addition, the combined copulaCML approach is applied to study the daily episode frequency of teenagers ’ physically active and physically inactive recreational activity participation, a subject of considerable interest in the transportation, sociology, and adolescence development fields. The data for the analysis are drawn from the 2000 San Francisco Bay Area Survey. The results highlight the
A latent variable representation of count data models to accommodate spatial and temporal dependence: Application to predicting crash frequency at intersections
 Transportation Research Part B
, 2012
"... This paper proposes a reformulation of count models as a special case of generalized orderedresponse models in which a single latent continuous variable is partitioned into mutually exclusive intervals. Using this equivalent latent variablebased generalized ordered response framework for count dat ..."
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Cited by 10 (6 self)
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This paper proposes a reformulation of count models as a special case of generalized orderedresponse models in which a single latent continuous variable is partitioned into mutually exclusive intervals. Using this equivalent latent variablebased generalized ordered response framework for count data models, we are then able to gainfully and efficiently introduce temporal and spatial dependencies through the latent continuous variables. Our formulation also allows handling excess zeros in correlated count data, a phenomenon that is commonly found in practice. A composite marginal likelihood inference approach is used to estimate model parameters. The modeling framework is applied to predict crash frequency at urban intersections in Arlington, Texas. The sample is drawn from the Texas Department of Transportation (TxDOT) crash incident files between 2003 and 2009, resulting in 1,190 intersectionyear observations. The results reveal the presence of intersectionspecific timeinvariant unobserved components influencing crash propensity and a spatial lag structure to characterize spatial dependence. Roadway configuration, approach roadway functional types, traffic control type, total daily entering traffic volumes and the split of volumes between approaches are all important
To CamelCase or Under score
"... Naming conventions are generally adopted in an effort to improve program comprehension. Two of the most popular conventions are alternatives for composing multiword identifiers: the use of underscores and the use of camel casing. While most programmers have a personal opinion as to which style is b ..."
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Cited by 8 (0 self)
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Naming conventions are generally adopted in an effort to improve program comprehension. Two of the most popular conventions are alternatives for composing multiword identifiers: the use of underscores and the use of camel casing. While most programmers have a personal opinion as to which style is better, empirical study forms a more appropriate basis for choosing between them. The central hypothesis considered herein is that identifier style affects the speed and accuracy of manipulating programs. An empirical study of 135 programmers and nonprogrammers was conducted to better understand the impact of identifier style on code readability. The experiment builds on past work of others who study how readers of natural language perform such tasks. Results indicate that camel casing leads to higher accuracy among all subjects regardless of training, and those trained in camel casing are able to recognize identifiers in the camel case style faster than identifiers in the underscore style. 1
Estimation of models in the Rasch family for polytomous items and multiple latent variables
 Journal of Statistical Software
, 2007
"... The Rasch family of models considered in this paper includes models for polytomous items and multiple correlated latent traits, as well as for dichotomous items and a single latent variable. An R package is described that computes estimates of parameters and robust standard errors of a class of log ..."
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Cited by 7 (1 self)
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The Rasch family of models considered in this paper includes models for polytomous items and multiple correlated latent traits, as well as for dichotomous items and a single latent variable. An R package is described that computes estimates of parameters and robust standard errors of a class of loglinearbylinear association (LLLA) models, which are derived from a Rasch family of models. The LLLA models are special cases of loglinear models with bivariate interactions. Maximum likelihood estimation of LLLA models in this form is limited to relatively small problems; however, pseudolikelihood estimation overcomes this limitation. Maximizing the pseudolikelihood function is achieved by maximizing the likelihood of a single conditional multinomial logistic regression model. The parameter estimates are asymptotically normal and consistent. Based on our simulation studies, the pseudolikelihood and maximum likelihood estimates of the parameters of LLLA models are nearly identical and the loss of efficiency is negligible. Recovery of parameters of Rasch models fit to simulated data is excellent.
Human Salmonellosis: estimation of doseillness from outbreak data
 Risk Analysis
, 2008
"... The quantification of the relationship between the amount of microbial organisms ingested and a specific outcome such as infection, illness or mortality is a key aspect of quantitative risk assessment. A main problem in determining such doseresponse models is the availability of appropriate data. ..."
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Cited by 6 (1 self)
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The quantification of the relationship between the amount of microbial organisms ingested and a specific outcome such as infection, illness or mortality is a key aspect of quantitative risk assessment. A main problem in determining such doseresponse models is the availability of appropriate data. Human feeding trials have been criticized because only young healthy volunteers are selected to participate and low doses, as often occurring in real life, are typically not considered. Epidemiological outbreak data are considered to be more valuable, but are more subject to data uncertainty. In this paper, we model the doseillness relationship based on data of 20 Salmonella outbreaks, as discussed by the World Health Organization. In particular, we model the doseillness relationship using Generalized Linear Mixed Models and fractional polynomials of dose. The fractional polynomial models are modified to satisfy the properties of different types of doseillness models as proposed by Teunis et al [1]. Within these models, differences in host susceptibility (susceptible versus normal population) are modeled as fixed effects whereas differences in serovar type and food matrix are modeled as random effects. In addition, two bootstrap procedures are presented. A first procedure accounts for stochastic variability whereas a second procedure accounts for both stochastic variability and data uncertainty. The analyses indicate that the susceptible population has a higher probability of illness at low dose levels when the combination pathogenfood matrix is extremely virulent and at high dose levels when the combination is less virulent. Furthermore, the analyses suggest that immunity exists in the normal population but not in the susceptible population.
Foot strike and injury rates in endurance runners: A retrospective study
 Medicine & Science in Sports & Exercise
, 2011
"... study tests if runners who habitually forefoot strike have different rates of injury than runners who habitually rearfoot strike. Methods: We measured the strike characteristics of middle and longdistance runners from a collegiate crosscountry team and quantified their history of injury, includin ..."
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Cited by 5 (0 self)
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study tests if runners who habitually forefoot strike have different rates of injury than runners who habitually rearfoot strike. Methods: We measured the strike characteristics of middle and longdistance runners from a collegiate crosscountry team and quantified their history of injury, including the incidence and rate of specific injuries, the severity of each injury, and the rate of mild, moderate, and severe injuries per mile run. Results: Of the 52 runners studied, 36 (69%) primarily used a rearfoot strike and 16 (31%) primarily used a forefoot strike. Approximately 74 % of runners experienced a moderate or severe injury each year, but those who habitually rearfoot strike had approximately twice the rate of repetitive stress injuries than individuals who habitually forefoot strike. Traumatic injury rates were not significantly different between the two groups. A generalized linear model showed that strike type, sex, race distance, and average miles per week each correlate significantly (P G 0.01) with repetitive injury rates. Conclusions: Competitive crosscountry runners on a college team incur high injury rates, but runners who habitually rearfoot strike have significantly higher rates of repetitive stress injury than those who mostly forefoot strike. This study does not test the causal bases for this general difference. One hypothesis, which requires further research, is that the absence of a marked impact peak in the ground reaction force during a forefoot strike compared with a rearfoot strike may contribute to lower rates of injuries in habitual forefoot strikers. Key Words: RUNNING FORM,