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Determining causes of deaths (CODs) occurred outside of civil registration and vital statistics systems is challenging. A technique called verbal autopsy (VA) is widely adopted to gather information on deaths in practice. A VA consists of interviewing relatives of a deceased person about symptoms of the deceased in the period leading to the death, often resulting in multivariate binary responses. While statistical methods have been devised for estimating the cause-specific mortality fractions (CSMFs) for a study population, continued expansion of VA to new populations (or "domains") necessitates approaches that recognize between-domain differences while capitalizing on potential similarities. In this article, we propose such a domain-adaptive method that integrates external between-domain similarity information encoded by a prespecified rooted weighted tree. Given a cause, we use latent class models to characterize the conditional distributions of the responses that may vary by domain. We specify a logistic stick-breaking Gaussian diffusion process prior along the tree for class mixing weights with node-specific spike-and-slab priors to pool information between the domains in a data-driven way. The posterior inference is conducted via a scalable variational Bayes algorithm. Simulation studies show that the domain adaptation enabled by the proposed method improves CSMF estimation and individual COD assignment. We also illustrate and evaluate the method using a validation dataset. The article concludes with a discussion of limitations and future directions. [ABSTRACT FROM AUTHOR]

Student leadership prepares students for responsibilities, such as taking on specific tasks and assuming leadership roles in their future personal and professional lives. Developing students' leadership profiles is among the important goals of educational systems aiming for future generations to take responsibility and advance their countries. With this perspective in mind, the PISA assessment includes items to measure students' leadership behaviors. This study aims to extract student leadership profiles from the leadership-related items in the PISA 2022 application, using data from Cambodia, Peru, Paraguay, and Guatemala, which have different governance systems and cultural characteristics. The second purpose of the research is to determine the distribution of the identified leadership profiles in these countries and explain them in the context of governance and cultural characteristics. Latent class analysis was used to determine student leadership profiles. Accordingly, two-class and three-class latent models were found to be the most suitable models to explain student profiles. While the distinction between student profiles is more pronounced in the two-class model, the three-class model provides more detailed information about student profiles. In this respect, two-class and three-class latent models are reported comparatively. In the two-class latent model, students are labeled as the "Shy or Lack of Self-Confidence Group" and the "Active Leader or Influential Group". In the three-class latent model, students are labeled as the "Moderate or Passive Leader Group", the "Strong Leader or Influential Group", and the "Avoidant or Leadership-Uncomfortable Group". In both models, it is one of the striking findings that Cambodian students are in the low leadership profile, and Peruvian students are in the high leadership profile. [ABSTRACT FROM AUTHOR]

The paper extends the empirical likelihood (EL) approach of Liu et al. to a new and very flexible family of latent class models for capture-recapture data also allowing for serial dependence on previous capture history, conditionally on latent type and covariates. The EL approach allows to estimate the overall population size directly rather than by adding estimates conditional to covariate configurations. A Fisher-scoring algorithm for maximum likelihood estimation is proposed and a more efficient alternative to the traditional EL approach for estimating the non-parametric component is introduced; this allows us to show that the mapping between the non-parametric distribution of the covariates and the probabilities of being never captured is one-to-one and strictly increasing. Asymptotic results are outlined, and a procedure for constructing profile likelihood confidence intervals for the population size is presented. Two examples based on real data are used to illustrate the proposed approach and a simulation study indicates that, when estimating the overall undercount, the method proposed here is substantially more efficient than the one based on conditional maximum likelihood estimation, especially when the sample size is not sufficiently large. [ABSTRACT FROM AUTHOR]

Carbon labeling of forest fruit products is an important approach to guide consumers toward low-carbon consumption and achieve agricultural carbon neutrality. This study employs a choice experiment method, taking Huyou Pomelo as an example, based on survey data from 1,108 consumers in five cities: Hangzhou, Shanghai, Nanjing, Hefei, and Beijing. This study analyzes consumers' preferences and willingness to pay for carbon-labeled Huyou Pomelo and explores their heterogeneous preferences by utilizing the Random Parameters Logit(RPL) model and the Latent Class Analysis(LCA) model. The results show that: (1) Despite the relatively low public awareness of carbon labeling, customers still have a strong willingness to purchase carbon-labeled products. (2) Consumers have an average willingness to pay 7.772 yuan/kg for carbon-labeled attributes, and a complementary relationship exists between carbon and traceability labeling. (3) Consumer age and marital status significantly influence the preferences of consumers to purchase carbon-labeled Huyou. However, no significant differences exist in purchasing intent based on gender, education level, income level, or household size. Based on the above conclusions, this study provides recommendations for Huyou producers in consumer market segmentation and for the government to improve carbon labeling-related systems. [ABSTRACT FROM AUTHOR]

Existing models for correlating global mortality rates with underlying country-specific factors overlook the variations in the effects of these factors on mortality across different countries. These may arise from social, cultural, and political complexities which are usually not measurable and are therefore referred to as unobserved heterogeneity in the statistical literature. Unobserved heterogeneity leads to biased parameter estimates in the models, erroneous inferences about the effects of factors contributing to mortalities, and ultimately inefficient policies. In this paper, latent class modelling is proposed for capturing such unobserved heterogeneity on the cases of traffic mortality and COVID-19 mortality. The ‘pyramid’ model of safety management is used as a common framework for model formulation. The proposed latent class model is an extension of the Negative Binomial (NB) model used in risk epidemiology. The model is tested with data from 105 countries, retrieved from international databases, including socioeconomic, infrastructure, exposure, transport, and COVID-19 variables. The results suggest that there exist two (different) latent country classes in both causes of mortality. The probability of a country belonging to a certain latent class is a much more efficient metric of country membership than previous deterministic groupings (e.g. income or geographic). Variables such as the elderly population, the GDP per capita or the level of motorization, have different effects in different country classes; these effects are not identifiable by conventional statistical modelling. The impact of ignoring unobserved heterogeneity in country mortality modelling is shown by comparing the results with those of conventional NB models.

We construct Bayesian and frequentist finite-sample goodness-of-fit tests for three different variants of the stochastic blockmodel for network data. Since all of the stochastic blockmodel variants are log-linear in form when block assignments are known, the tests for the latent block model versions combine a block membership estimator with the algebraic statistics machinery for testing goodness-of-fit in log-linear models. We describe Markov bases and marginal polytopes of the variants of the stochastic blockmodel and discuss how both facilitate the development of goodness-of-fit tests and understanding of model behaviour. The general testing methodology developed here extends to any finite mixture of log-linear models on discrete data, and as such is the first application of the algebraic statistics machinery for latent-variable models. [ABSTRACT FROM AUTHOR]

Student leadership prepares students for responsibilities, such as taking on specific tasks and assuming leadership roles in their future personal and professional lives. Developing students’ leadership profiles is among the important goals of educational systems aiming for future generations to take responsibility and advance their countries. With this perspective in mind, the PISA assessment includes items to measure students’ leadership behaviors. This study aims to extract student leadership profiles from the leadership-related items in the PISA 2022 application, using data from Cambodia, Peru, Paraguay, and Guatemala, which have different governance systems and cultural characteristics. The second purpose of the research is to determine the distribution of the identified leadership profiles in these countries and explain them in the context of governance and cultural characteristics. Latent class analysis was used to determine student leadership profiles. Accordingly, two-class and three-class latent models were found to be the most suitable models to explain student profiles. While the distinction between student profiles is more pronounced in the two-class model, the three-class model provides more detailed information about student profiles. In this respect, two-class and three-class latent models are reported comparatively. In the two-class latent model, students are labeled as the “Shy or Lack of Self-Confidence Group” and the “Active Leader or Influential Group”. In the three-class latent model, students are labeled as the “Moderate or Passive Leader Group”, the “Strong Leader or Influential Group”, and the “Avoidant or Leadership-Uncomfortable Group”. In both models, it is one of the striking findings that Cambodian students are in the low leadership profile, and Peruvian students are in the high leadership profile.

Due to the impact of the COVID‐19 pandemic, people has changed the way they work, learn and socialize. As result, it is important to identify the pre‐existing digital gaps to implement the European Union digitalization strategy. This study aims to identify typologies of internet use in the 28 European Union (EU) countries (at the time of the survey), based on the characteristics of the users and their internet usage patterns. A two‐level latent class analysis was applied. At the first level, individuals within each country were grouped according to their characteristics of internet use; and, simultaneously, at the second level, countries were grouped based on the similar structure of individual segments. Using data from Eurobarometer, results show that internet use in the EU digital market is not homogeneous. The European Commission should take these pre‐existing gaps into account in the EU digitalization strategy. [ABSTRACT FROM AUTHOR]

Background: People with diabetes tend to use many medications to treat diabetes and comorbidities. Nevertheless, the evolution of polypharmacy in newly diagnosed males and females has been little studied. Objective: The objective of this paper was to identify and describe medication trajectories in incident diabetes cases according to sex. Methods: Data were obtained from the Quebec Integrated Chronic Disease Surveillance System. We built a population-based cohort of community-dwelling individuals aged >65 years diagnosed with diabetes in 2014 who were alive and covered with the public drug plan until March 31, 2019. Latent class models were used to identify medication trajectory groups in males and females separately. Results: Of the 10,363 included individuals, 51.4% were males. Females were older and more likely to have more medication claims than males. Four trajectory groups were identified for males and five for females. Most trajectories showed sustained and stable number of medications over time. For each sex, only one of the trajectory groups included a mean annual number of medications lesser than five. Slight increasing trends of medication use were detected in the trajectories composed of very high users, which included older, more comorbid individuals frequently exposed to potentially inappropriate medications. Conclusions: Most males and females with incident diabetes had a high burden of medication following the year of diagnosis and were classified in a group of sustained medication use over time. The largest increase in medication was among those who had higher level of polypharmacy of questionable quality at baseline, raising concerns about the innocuity of such medication trajectories.

ABSTRACT: Pregnancy diagnosis using pregnancy-associated glycoprotein (PAG) ELISA technology in blood or milk samples is validated from 28 d after insemination in dairy cows. The objective of this study was to estimate the sensitivity (Se) and specificity (Sp) of a commercial milk PAG-based ELISA in Holstein dairy cows between 23 and 27 d after insemination. Milk samples (n = 268) from 257 Holstein dairy cows 23 to 27 d after AI were submitted for PAG ELISA testing. Pregnancy status was confirmed by either a second milk PAG ELISA test conducted between 28 and 50 d after insemination (n = 200) or transrectal ultrasonography performed between 28 and 59 d after insemination (n = 68). A Bayesian latent class model was used to compare the paired results from the test at 23 to 27 d after AI test to the reference test. The latent class model typically used for comparing 2 or more imperfect tests was extended to include the possibility of pregnancy loss between the 23 to 27 d test and the reference test. Informative priors for the probability of pregnancy loss, and for the Se and Sp of the PAG and ultrasonography reference tests were obtained from the scientific literature. Estimated median Se and Sp of the PAG ELISA test conducted between 23 and 27 d after AI were 0.98 (95% credible interval 0.93 to 1.0) and 0.98 (0.89 to 1.0), respectively, when using a standardized corrected optical density threshold of 0.15. Although the accuracy of the test under investigation was excellent, more data will be needed to confirm the optimal diagnostic cut point for PAG in milk for early pregnancy diagnosis in this time window. The optimal timing of pregnancy diagnosis will depend on herd-specific logistics and the action to be taken to re-inseminate nonpregnant cows.

Researchers use finite mixture models to analyze linked survey and administrative data on labor earnings, while also accounting for various types of measurement error in each data source. Different combinations of error-ridden and error-free observations characterize latent classes. Latent class probabilities depend on the probabilities of the different types of error. We introduce a suite of commands to fit finite mixture models to linked survey-administrative data: there is a general model and seven simpler variants. We also provide postestimation commands for assessment of reliability, marginal effects, data simulation, and prediction of hybrid variables that combine information from both data sources about the outcome of interest. Our commands can also be used to study measurement errors in other variables besides labor earnings. [ABSTRACT FROM AUTHOR]

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree‐structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data‐driven leaf groups with distinct vectors of class probabilities, and (3) individual‐level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions. [ABSTRACT FROM AUTHOR]

Sample surveys may suffer from nonignorable unit nonresponse. This happens when the decision of whether or not to participate in the survey is correlated with variables of interest; in such a case, nonresponse produces biased estimates for parameters related to those variables, even after adjustments that account for auxiliary information. This paper presents a method to deal with nonignorable unit nonresponse that uses generalised calibration and latent variable modelling. Generalised calibration enables to model unit nonresponse using a set of auxiliary variables (instrumental or model variables), that can be different from those used in the calibration constraints (calibration variables). We propose to use latent variables to estimate the probability to participate in the survey and to construct a reweighting system incorporating such latent variables. The proposed methodology is illustrated, its properties discussed and tested on two simulation studies. Finally, it is applied to adjust estimates of the finite population mean wealth from the Italian Survey of Household Income and Wealth. [ABSTRACT FROM AUTHOR]

Latent class models with covariates are widely used for psychological, social, and educational research. Yet the fundamental identifiability issue of these models has not been fully addressed. Among the previous research on the identifiability of latent class models with covariates, Huang and Bandeen-Roche (Psychometrika 69:5–32, 2004) studied the local identifiability conditions. However, motivated by recent advances in the identifiability of the restricted latent class models, particularly cognitive diagnosis models (CDMs), we show in this work that the conditions in Huang and Bandeen-Roche (Psychometrika 69:5–32, 2004) are only necessary but not sufficient to determine the local identifiability of the model parameters. To address the open identifiability issue for latent class models with covariates, this work establishes conditions to ensure the global identifiability of the model parameters in both strict and generic sense. Moreover, our results extend to the polytomous-response CDMs with covariates, which generalizes the existing identifiability results for CDMs. [ABSTRACT FROM AUTHOR]

The subject of this study is consumer behaviour in sourcing meals and the manifestation of different behavioural patterns before and during the COVID-19 pandemic. The natural experiment, which COVID-19 represents, provides fertile ground for studying consumer behaviour and identifying important factors influencing consumer attitudes in sourcing meals and accessing food. To achieve its aim, this study draws from theories of social psychology and economics as a background for understanding the factors and processes affecting attitudes. Using survey data and qualitative and quantitative analysis the study established that the attitudes of sourcing meals remained quite stable since the COVID-19 pandemic began. Contemporary consumer experiences are primarily associated with health and safety concerns and are conditional on financial affordability. Nevertheless, advertisements and marketing campaigns remain an important factor during COVID-19. Social media platforms have grown in importance as a channel through which consumers can be reached for their food access behaviours. [ABSTRACT FROM AUTHOR]

Classifications are a basic tool for research, which allow summarizing the diversity of objects in a number of categories that fits the cognitive abilities of the human mind. Their relevance for higher education is emphasized by the differentiation of institutional profiles. Yet, unlike in the US, there is currently no classification of European Higher Education Institutions (HEIs). This paper fills this gap by developing a classification of European HEIs, which focuses on differences in activity profiles and subject scope. To this aim, it uses data from an enriched version of the European Tertiary Education Register on a sample of more than 2000 HEIs in a large number of European countries. The classification comprises six classes that occupy distinct positions in a configuration space defined by two dimensions, i.e. research vs. educational orientation and subject specialization. Ex-post analysis shows that classes are identifiable and can be attributed meaningful labels; the class of research universities comprises most European HEIs competing in international rankings, while a class of generalist HEIs with lower research orientation that cuts across the traditional distinction between universities and Universities of Applied Sciences can be distinguished. Furthermore, three classes of specialist HEIs can be identified. The classification provides a meaningful representation of European higher education that is more fine-grained than the distinction between university and non-university sectors while remaining parsimonious. We, finally show how national categories map to the classification, displaying its potential to compare differences in national institutional settings across Europe. [ABSTRACT FROM AUTHOR]

Pregnancy diagnosis using pregnancy-associated glycoprotein (PAG) ELISA technology in blood or milk samples is validated from 28 d after insemination in dairy cows. The objective of this study was to estimate the sensitivity (Se) and specificity (Sp) of a commercial milk PAG-based ELISA in Holstein dairy cows between 23 and 27 d after insemination. Milk samples (n = 268) from 257 Holstein dairy cows 23 to 27 d after AI were submitted for PAG ELISA testing. Pregnancy status was confirmed by either a second milk PAG ELISA test conducted between 28 and 50 d after insemination (n = 200) or transrectal ultrasonography performed between 28 and 59 d after insemination (n = 68). A Bayesian latent class model was used to compare the paired results from the test at 23 to 27 d after AI test to the reference test. The latent class model typically used for comparing 2 or more imperfect tests was extended to include the possibility of pregnancy loss between the 23 to 27 d test and the reference test. Informative priors for the probability of pregnancy loss, and for the Se and Sp of the PAG and ultrasonography reference tests were obtained from the scientific literature. Estimated median Se and Sp of the PAG ELISA test conducted between 23 and 27 d after AI were 0.98 (95% credible interval 0.93 to 1.0) and 0.98 (0.89 to 1.0), respectively, when using a standardized corrected optical density threshold of 0.15. Although the accuracy of the test under investigation was excellent, more data will be needed to confirm the optimal diagnostic cut point for PAG in milk for early pregnancy diagnosis in this time window. The optimal timing of pregnancy diagnosis will depend on herd-specific logistics and the action to be taken to re-inseminate nonpregnant cows. [ABSTRACT FROM AUTHOR]

Sociologists have long argued that a racial and ethnic category can be made under certain circumstances and remade under anothers. The panethnic category of Asian American was in fact devised to mobilize different immigrant-origin groups in the 1960s. Today’s Asian Americans have diverse opinions on numerous issues. Given this divergence, it is possible that the category will be subject to remaking or unmaking. Using survey questions from the 2016 pre-election National Asian American Survey, this article analyzes where respondents’ policy opinions converge and diverge. Using latent class models, it shows that though many Asian Americans support government interventions in health care, education, climate change, and racial justice, some diverge sharply in regard to Muslim immigration. Logistic regression models show that different experiences of immigration and differences in national origins undergird such divergence. I discuss the implications of these fault lines for the future of the Asian American category.

This paper focuses on the study of Spanish ceramic tile exporters' port choice criteria, an industry that is characterized by a low unit value index. Thus, transport costs highly influence the competitiveness of their products in international markets, representing a relatively high percentage of the value of the shipment. It is therefore critical for this type of industry to have access to efficient door-to-door maritime chains. Determinants of port choice are analysed through a latent class model allowing to link class membership to specific characteristics of the shippers and their shipments such as the size of the company, the value of the shipment, the destination market and the frequency of shipments. The results obtained show differences in the valuation of the attributes frequency, delays and closing time depending on the type of destination market. While companies exporting to deep-sea markets value specially the attributes frequency and delays, those exporting to the regional corridor are the only ones where the closing time is statistically significant. Policies designed show that when there is a strong competition between two ports to attract cargo from an overlapped hinterland, the ultimate effectiveness of the measures would depend on its competitor's strategic movement and the measures implemented there. • The paper focuses on the study of port choice criteria from shippers' perspective • An application to exporters belonging to Spanish ceramic tile industry • We use latent class model to better capture heterogeneity in shippers' preferences • Results show differences in attributes depending on the type of destination market. • The specialization of the port in each type of traffic is a relative advantage [ABSTRACT FROM AUTHOR]

This paper presents a model‐based method for clustering multivariate binary observations that incorporates constraints consistent with the scientific context. The approach is motivated by the precision medicine problem of identifying autoimmune disease patient subsets or classes who may require different treatments. We start with a family of restricted latent class models or RLCMs. However, in the motivating example and many others like it, the unknown number of classes and the definition of classes using binary states are among the targets of inference. We use a Bayesian approach to RLCMs in order to use informative prior assumptions on the number and definitions of latent classes to be consistent with scientific knowledge so that the posterior distribution tends to concentrate on smaller numbers of clusters and sparser binary patterns. The paper derives a posterior sampling algorithm based on Markov chain Monte Carlo with split‐merge updates to efficiently explore the space of clustering allocations. Through simulations under the assumed model and realistic deviations from it, we demonstrate greater interpretability of results and superior finite‐sample clustering performance for our method compared to common alternatives. The methods are illustrated with an analysis of protein data to detect clusters representing autoantibody classes among scleroderma patients. [ABSTRACT FROM AUTHOR]

This paper developed a two-stage modeling framework for analyzing residential and work location choices with probabilistic choice sets. In the first stage, a household (or a worker) was assumed to select a neighborhood (such as central business district, urban, suburban etc.) to live (or work). In the second stage, the household (or worker) was assumed to choose a specific zone conditional on the selected neighborhood. The neighborhood choice model component takes the form of Manski model with latent choice sets. The model was used to analyze residential and work location decisions in Nashville, Tennessee. The model results indicate significant heterogeneity in the consideration probability of different neighborhood alternatives both in the residential and work location choices. The latent class neighborhood models were found to outperform standard MNL models that assume all decision-makers consider the universal choice set in their decision-making. [ABSTRACT FROM AUTHOR]

Building on past research, we implement a hierarchical latent class model to analyze political participation from a comparative perspective. Our methodology allows simultaneously: (i) estimating citizens' propensity to engage in conventional and unconventional modes of participation; (ii) classifying individuals into underlying "types" capturing within- and cross-country variations in participation; and (iii) assessing how this classification varies with micro- and macro-level factors. We apply our model to Latin American survey data. We show that our method outperforms alternative approaches used to study participation and derive typologies of political engagement. Substantively, we find that the distribution of participatory types is similar throughout the continent, and that it correlates strongly with respondents' socio-demographic characteristics and crime victimization. [ABSTRACT FROM AUTHOR]

Background Research examining the role of obesity in cardiovascular disease (CVD) often fails to adequately consider heterogeneity in obesity severity, distribution, and duration. Methods and Results We here use multivariate latent class mixed models in the biracial Atherosclerosis Risk in Communities study (N=14 514; mean age=54 years; 55% female) to associate obesity subclasses (derived from body mass index, waist circumference, self‐reported weight at age 25, tricep skinfold, and calf circumference across up to four triennial visits) with total mortality, incident CVD, and CVD risk factors. We identified four obesity subclasses, summarized by their body mass index and waist circumference slope as decline (4.1%), stable/slow decline (67.8%), moderate increase (24.6%), and rapid increase (3.6%) subclasses. Compared with participants in the stable/slow decline subclass, the decline subclass was associated with elevated mortality (hazard ratio [HR] 1.45, 95% CI 1.31, 1.60, P

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Sequence analysis is being more and more widely used for the analysis of social sequences and other multivariate categorical time series data. However, it is often complex to describe, visualize, and compare large sequence data, especially when there are multiple parallel sequences per subject. Hidden (latent) Markov models (HMMs) are able to detect underlying latent structures and they can be used in various longitudinal settings: to account for measurement error, to detect unobservable states, or to compress information across several types of observations. Extending to mixture hidden Markov models (MHMMs) allows clustering data into homogeneous subsets, with or without external covariates. The seqHMM package in R is designed for the efficient modeling of sequences and other categorical time series data containing one or multiple subjects with one or multiple interdependent sequences using HMMs and MHMMs. Also other restricted variants of the MHMM can be fitted, e.g., latent class models, Markov models, mixture Markov models, or even ordinary multinomial regression models with suitable parameterization of the HMM. Good graphical presentations of data and models are useful during the whole analysis process from the first glimpse at the data to model fitting and presentation of results. The package provides easy options for plotting parallel sequence data, and proposes visualizing HMMs as directed graphs.

This paper investigates household financial portfolios in Chile. We use latent class models to identify groups of households according to their financial behavior. The model reveals nine distinct behavioral groups. The two largest groups account for 40% of the population and represent mostly households lacking access to banking sector services. Overall, we find strong evidence of households mixing assets and debt, which contradicts the classical assumptions of the life-cycle theory. We demonstrate that a significant share of indebted households has credit in the informal sector even though they were able to save on regular basis and thus should seek credit in the formal market. Education debt seems to be equally present among different socio-economic groups. [ABSTRACT FROM AUTHOR]

We estimate a travel cost model for the George Washington & Jefferson National Forests using an On-Site Latent Class Poisson Model. We show that the constraints of ad-hoc truncation and homogenous preferences significantly impact consumer surplus estimates derived from the on-site travel cost model. By relaxing the constraints, we show that more than one class of visitors with unique preferences exists in the population. The resulting demand functions, price responsive behaviors, and consumer surplus estimates reflect differences across these classes of visitors. With heterogeneous preferences, a group of 'local residents' exists with a probability of 8% and, on average take 113 visits. [ABSTRACT FROM AUTHOR]

Background: Shortly after the introduction of the first licensed vaccine against dengue fever (Dengvaxia), a serious outcome was attributed to the vaccine: vaccinated individuals without a previous dengue infection were at increased risk of developing severe dengue if subsequently infected by a heterologous serotype. In response, the World Health Organization recommended vaccination in regions where the seroprevalence of dengue is at least 50% and, ideally, greater than 70%. Hence, accurate estimates of regional seroprevalence are crucial for both population vaccination strategies and test-then-vaccinate decisions at the individual level. Currently, estimates of seroprevalence are based on surveys, using screening tests for previous dengue exposure. These estimates must consider the sensitivity and specificity of the tests, which depend on identification of those who have been exposed, ostensibly through a test, regarded as the gold standard. There is, however, no easily accessible gold standard test for dengue. Methods: We propose an approach to estimate the seroprevalence of dengue that does not require a gold standard test by modeling: (i) the uncertainty in the sensitivity and specificity, and (ii) the uncertainty in the “true” disease prevalence. Results: Through simulations, we demonstrate the effect of these extra sources of uncertainty on post-test estimates of dengue seroprevalence. Our simulations show, for example, that in a population of 1 million it is possible to overestimate or underestimate the number who are truly seropositive by as much as 76,000. Conclusions: Current estimates can substantially overestimate or underestimate the true probability of previous exposure when these extra sources of variability are not accounted for.

The purpose of this study is to explore the Russian and Chinese emerging markets for imported wines, by assessing Beijing and Moscow consumer demand for different attributes. This study employs the Discrete Choice Experiments technique to evaluate consumer preferences and willingness-to-pay for the selected wine attributes (medals, alcohol level, landscape certification, country of origin, grape variety and price). Results from Latent Class models provide evidence of preference heterogeneity and suggest the existence of two distinct consumer segments in China, and three segments in Russia. In both samples, the wine medals and country of origin are the top selected characteristics. Conspicuous consumption tendencies were found in some segments, with price having a positive impact on utility, probably signalling quality. [ABSTRACT FROM AUTHOR]

The latent attribute space in cognitive diagnosis models (CDMs) is often assumed to be unstructured or saturated. In recent years, the number of latent attributes in real tests has often been found to be large, and polytomous latent attributes have been advocated. Therefore, it is preferable to adopt substantive theories to connect seemingly unrelated latent attributes, to replace the unstructured or saturated latent structural models (LSMs) with structured or parsimonious ones, with simplified parameter estimation. In the present study, we developed a partial mastery, higher-order LSM for polytomous attributes, which was built upon the framework of adjacent-category logit models to account for a higher-order latent structure of multiple polytomous attributes. The proposed model can be incorporated into many existing CDMs. We conducted simulations to evaluate the psychometric properties of the proposed model and obtained good parameter recovery. We then provided an empirical example to demonstrate the applications and the advantages of the proposed model. [ABSTRACT FROM AUTHOR]

We adopt an empirical approach to analyse, measure and decompose inequality of opportunity (IOp) in health, based on a latent class model. This addresses some of the limitations that affect earlier work in this literature concerning the definition of types, such as partial observability, the ad hoc selection of circumstances, the curse of dimensionality and unobserved type-specific heterogeneity that may lead to biased estimates of IOp. We apply our latent class approach to measure IOp in allostatic load, a composite measure of biomarker data. Using data from Understanding Society: The UK Household Longitudinal Study (UKHLS), we find that a latent class model with three latent types best fits the data, with the corresponding types characterised in terms of differences in their observed circumstances. Decomposition analysis shows that about two thirds of the total inequalities in allostatic load can be attributed to the direct and indirect contribution of circumstances and that the direct contribution of effort is small. Further analysis conditional on age-sex groups reveals that the relative (percentage) contribution of circumstances to the total inequalities remains mostly unaffected and the direct contribution of effort remains small. [ABSTRACT FROM AUTHOR]

Summary: Given the increasing prevalence of adult obesity, furthering understanding of the determinants of measures such as the body mass index (BMI) remains high on the policy agenda. We contribute to existing literature on modelling the BMI by proposing an extension to latent class modelling, which serves to unveil a more detailed picture of the determinants of BMI. Interest here lies in latent class analysis with a regression model and predictor variables explaining class membership, a regression model and predictor variables explaining the outcome variable within BMI classes and instances where the BMI classes are naturally ordered and labelled by expected values within class. A simple and generic way of parameterizing both the class probabilities and the statistical representation of behaviours within each class is proposed, that simultaneously preserves the ranking according to class‐specific expected values and yields a parsimonious representation of the class probabilities. Based on a wide range of metrics, the newly proposed approach is found to dominate the prevailing approach and, moreover, results are often quite different across the two. [ABSTRACT FROM AUTHOR]

Salmonella enterica subsp. enterica serovar Dublin (S. Dublin) is a cause of disease in cattle of all ages, although the most commonly clinically affected group is calves aged 2 weeks to 3 months. A special feature of S. Dublin is the tendency to produce persistent infections without clinical manifestation in some infected cattle - also called carriers. [ABSTRACT FROM AUTHOR]

Identifiability of statistical models is a fundamental regularity condition that is required for valid statistical inference. Investigation of model identifiability is mathematically challenging for complex models such as latent class models. Jones et al. used Goodman's technique to investigate the identifiability of latent class models with applications to diagnostic tests in the absence of a gold standard test. The tool they used was based on examining the singularity of the Jacobian or the Fisher information matrix, in order to obtain insights into local identifiability (ie, there exists a neighborhood of a parameter such that no other parameter in the neighborhood leads to the same probability distribution as the parameter). In this paper, we investigate a stronger condition: global identifiability (ie, no two parameters in the parameter space give rise to the same probability distribution), by introducing a powerful mathematical tool from computational algebra: the Gröbner basis. With several existing well‐known examples, we argue that the Gröbner basis method is easy to implement and powerful to study global identifiability of latent class models, and is an attractive alternative to the information matrix analysis by Rothenberg and the Jacobian analysis by Goodman and Jones et al. [ABSTRACT FROM AUTHOR]

A Gaussian mixture model with a structured covariance matrix was used to analyse image data recorded by a digital sensor under darkness to model the effects of temperature and duration of exposure on the expected value and on the variance of the sensor dark current, separately for ordinary and possibly defective pixels. The model accounts for two components of variance within each latent type: random noise in each image and lack of uniformity within the sensor; both components are allowed to depend on experimental conditions. The results seem to indicate that the dependence of the expected value of dark current on duration of exposure and temperature cannot be represented by a simple parametric model. The latent class model detects the presence of at least two types of hot pixels. If we order the latent classes in decreasing order of the class weights, the corresponding expected values and variances increase. The covariance structure that emerges from our analysis has an important implication: the sign and the relative size of pixels deviations from uniformity are invariant to experimental conditions. [ABSTRACT FROM AUTHOR]

Investigating on transit users' opinions represents a useful strategy to understand the level of quality of a service, and consequently pursuing the most convenient interventions for satisfying users and improving the service. On the other hand, an important issue for increasing the use of transit systems is linked to the necessity to attract new users, in addition to customize the current users. For this reason, it becomes fundamental to know also the perceptions of people who do not use transit systems with the aim to discover which are the reasons of their travel choices and particularly which are the most relevant transit service aspects for them. In this work, a Stated Preferences (SP) survey was conducted in order to investigate on both transit current and potential users' preferences. This kind of survey allows potential users opinions to be captured due to its peculiarity of the possibility to propose to interviewees hypothetical services and not only real services, which could not be judged by people who do not use the service. The collected data were adopted to calibrate discrete choice models such as Random Parameters mixed Logit models and Latent Class models. These models allow heterogeneity of users' opinions to be analysed. Calibrating this kind of models using data collected from both users and potential users permitted to capture the differences among users in their preferences about transit service quality, and the differences among potential users in their desired service quality. Model results showed some interesting findings concerning the differences of categories of users and potential users, and especially the diversity of preferences between people who know well the service and people who could become users even if they have not a real perception of it. We also calculated the Willingness-To-Pay (WTP), which allowed to discover that the WTP values obtained for the potential users are very much higher than the values obtained for the users. The proposed models can be also useful for the practitioners and transit operators to identify the best combination of the quality levels to assign to the various service aspects. [ABSTRACT FROM AUTHOR]

Background and Objectives: The existence of different patterns of functional impairment in older adults has scarcely been addressed. This research investigates major patterns of functional impairment based on Pfeffer's Functional Activity Questionnaire (FAQ). Research Design and Methods: The participants come from NEDICES (Neurological Disorders in Central Spain), a prospective population-based cohort study. A total of 3837 individuals without dementia who completed the Spanish version of the FAQ was selected. Latent Class Analysis (LCA) was carried out to examine potential cluster subgroups based on FAQ responses. Results: The FAQ showed good internal consistency (Cronbach's alpha: 0.86) and moderate correlation (r = −.40) with cognitive performance on the Mini-Mental State Examination (MMSE-37). The response patterns revealed the presence of three latent classes: absence of functional alteration (Class 1), established functional alteration (Class 2), and minimal functional alteration (Class 3). Moreover, the probability of resolving Items 2 ('shopping alone for...'), 3 ('heating water...'), 4 ('preparing a balanced meal'), and 9 ('travelling out of neighbourhood...') was close to 0% for Class 2 membership, while those with the lowest probability of resolution for Class 3 were Items 2 and 9. Items 3 and 4 were the best to discriminate between different grades of functional alterations (Class 2 vs. Class 3). Discussion and Implications: Our findings indicate that the combination of overall FAQ score and item response pattern may help to classify individuals with different subtypes of functional impairment. The Spanish version of the FAQ is a useful tool for detection of functional impairment in older adults. [ABSTRACT FROM AUTHOR]

The objective of this study is to characterize Latent Classes emerging from the analysis of the level of digital competences, use and consumption of applications and/or services through the Internet. For this purpose, the results of the survey Basic Digital Competences (Competencias Básicas Digitales-COBADI®) applied to university students, with more than 60 categorical variables, were considered. A total of 4762 undergraduate and graduate students from five Spanish universities participated in this survey: Complutense University of Madrid (UCM), Pablo de Olavide University (UPO), Almeria University (UAL), National University of Distance Education (UNED) and Rey Juan Carlos University (URJC). The application of the questionnaire was done through the Internet, from the Institute for Research in Social Sciences and Education of University of Atacama—Chile. The methodology used is mixed, because the questions of the questionnaire provide qualitative information that can be interpreted and elaborated from the results. It is also quantitative because basic statistical techniques are used for the exploratory analysis of the data, and later Latent Class Analysis (LCA), to complement the description of the data set and the variables considered in the study, thus allowing us to group the classes of variables that do not appear explicitly in the set of observed variables, but which nevertheless affect them. The results of the study show that regardless of the gender and age range of the participants, there are four clearly differentiated groups or classes in the use and consumption of ICTs in different ways for their activities, both personal and academic, which allows for identifying different developments of digital competences. This study allows establishing a baseline in order to be able to elaborate later, in the development of the digital competences currently needed, which should be developed by university students.

ELECTUS is a multi-centre research project with the aim to stabilize the relationship between universities and companies. Through this project, it is possible to acquire information about procedures for the entrepreneurs' choice in the recruitment process of the new graduates. Using a CAWI survey, Lombardy companies with at least 15 employees were asked to manifest their preferences choosing among hypothetical profiles of new graduates with different competencies each other. In this study a Conjoint Analysis is carried out to evaluate which characteristics of a graduate's profile the employers prefer for a potential candidate in the job position of Administrative Clerk. More specifically, in order to detect if there exist subgroups of employers having homogeneous preferences about such characteristics, a Latent Class Metric Conjoint Analysis is employed. [ABSTRACT FROM AUTHOR]

Travel surveys that elicit responses to questions regarding daily activity and travel choices form the basis for most of the transportation planning and policy analysis. The response variables collected in these surveys are prone to errors leading to mismeasurement or misclassification. Standard modeling methods that ignore these errors while modeling travel choices can lead to biased parameter estimates. In this study, methods available in the econometrics literature were used to quantify and assess the impact of misclassification errors in auto ownership choice data. The results uncovered significant misclassification rates ranging from 1 to 40% for different auto ownership alternatives. Also, the results from latent class models provide evidence for variation in misclassification probabilities across different population segments. Models that ignore misclassification were not only found to have lower statistical fit but also significantly different elasticity effects for choice alternatives with high misclassification probabilities. The methods developed in this study can be extended to analyze misclassification in several response variables (e.g., mode choice, activity purpose, trip/tour frequency, and mileage) that constitute the core of advanced travel demand models including tour and activity-based models. [ABSTRACT FROM AUTHOR]

• Choices are influenced by subjective latent traits and latent class heterogeneity. • Route choice behavior of frequent vs occasional drivers has been analysed. • An LC-ICLV hybrid modeling framework has been proposed and applied. • Data has been collected through a revealed preference web-based survey. Previous route choice studies have mostly focused on the effect of observable factors, such as route attributes and socio-economic characteristics, on individuals' decisions. However, route choice decisions might not be exclusively dependent on these observable variables, but also on latent variables, which cannot be directly observed and measured. Also, the latent behavioral heterogeneity among the population has mostly been ignored by assuming that all the individuals in the sample population have similar attitudes, perceptions, and lifestyles. In this paper, we present a comprehensive framework to explicitly incorporate latent behavioral constructs as well as segment heterogeneity based on a probabilistic segmentation of the population. We apply the proposed framework to compare the route choice behavior of frequent versus occasional drivers. An Integrated Choice and Latent Variable model is used to bring in the role of the underlying behavioral constructs, while a Latent Class model accounts for taste heterogeneity across the two segments of our sample population. An Extended Path-Size Logit model is adopted as the choice model component and a Metropolis-Hastings based algorithm is used to generate route alternatives. Data is collected through a web-based survey designed to collect behavioral data on drivers' route choices, using psychometric indicators and behavioral questions on respondents' perceptions and attitudes. Results confirm that the inclusion of latent variables and latent heterogeneity across population segments significantly improve the explanatory power of the choice model, and illustrate how the route choice behavior of frequent car users is different from that of occasional ones. [ABSTRACT FROM AUTHOR]

Introduction. Estimating costs of medical care attributable to treatments over time is difficult due to costs that cannot be explained solely by observed risk factors. Unobserved risk factors cannot be accounted for using standard econometric techniques, potentially leading to imprecise prediction. The goal of this work is to describe methodology to account for latent variables in the prediction of longitudinal costs. Methods. Latent class growth mixture models (LCGMMs) predict class membership using observed risk factors and class-specific distributions of costs over time. Our motivating example models cost of care for children with cystic fibrosis from birth to age 17. We compare a generalized linear mixed model (GLMM) with LCGMMs. Both models use the same covariates and distribution to predict average costs by combinations of observed risk factors. We adopt a Bayesian estimation approach to both models and compare results using the deviance information criterion (DIC). Results. The 3-class LCGMM model has a lower DIC than the GLMM. The LCGMM latent classes include a low-cost group where costs increase slowly over time, a medium-cost group with initial higher costs than the low-cost group and with more rapidly increasing costs at older ages, and a high-cost group with a U-shaped trajectory. The risk profile-specific mixtures of classes are used to predict costs over time. The LCGMM model shows more delineation of costs by age by risk profile and with less uncertainty than the GLMM model. Conclusions. The LCGMM approach creates flexible prediction models when using longitudinal cost data. The Bayesian estimation approach to LCGMM presented fits well into cost-effectiveness modeling where the estimated trajectories and class membership can be used for prediction. [ABSTRACT FROM AUTHOR]