Dependent Latent Variables Research Articles

A class of models for large zero-inflated spatial data

AbstractSpatially correlated data with an excess of zeros, usually referred to as zero-inflated spatial data, arise in many disciplines. Examples include count data, for instance, abundance (or lack thereof) of animal species and disease counts, as well as semi-continuous data like observed precipitation. Spatial two-part models are a flexible class of models for such data. Fitting two-part models can be computationally expensive for large data due to high-dimensional dependent latent variables, costly matrix operations, and slow mixing Markov chains. We describe a flexible, computationally efficient approach for modeling large zero-inflated spatial data using the projection-based intrinsic conditional autoregression (PICAR) framework. We study our approach, which we call PICAR-Z, through extensive simulation studies and two environmental data sets. Our results suggest that PICAR-Z provides accurate predictions while remaining computationally efficient. An important goal of our work is to allow researchers who are not experts in computation to easily build computationally efficient extensions to zero-inflated spatial models; this also allows for a more thorough exploration of modeling choices in two-part models than was previously possible. We show that PICAR-Z is easy to implement and extend in popular probabilistic programming languages such as and .

Distress and neuroticism as mediators of the effect of childhood and adulthood adversity on cognitive performance in the UK Biobank study

Childhood adversity and adulthood adversity affect cognition later in life. However, the mechanism through which adversity exerts these effects on cognition remains under-researched. We aimed to investigate if the effect of adversity on cognition was mediated by distress or neuroticism. The UK Biobank is a large, population-based, cohort study designed to investigate risk factors of cognitive health. Here, data were analysed using a cross-sectional design. Structural equation models were fitted to the data with childhood adversity or adulthood adversity as independent variables, distress and neuroticism as mediators and executive function and processing speed as latent dependent variables that were derived from the cognitive scores in the UK Biobank. Complete data were available for 64,051 participants in the childhood adversity model and 63,360 participants in the adulthood adversity model. Childhood adversity did not show a direct effect on processing speed. The effect of childhood adversity on executive function was partially mediated by distress and neuroticism. The effects of adulthood adversity on executive function and processing speed were both partially mediated by distress and neuroticism. In conclusion, distress and neuroticism mediated the deleterious effect of childhood and adulthood adversity on cognition and may provide a mechanism underlying the deleterious consequences of adversity.

Independent and joint associations of aerobic and muscle-strengthening exercise with mental health in adolescents: A cross-sectional analysis before and during COVID-19 using the 2015–2021 National Youth Risk Behavior Survey

PurposeCOVID-19 led to social isolation that may have compromised adolescent mental health. This study examined the independent and joint associations of aerobic physical activity (PA) and muscle-strengthening exercise (MSE) with mental health problems in adolescents. MethodsParticipants were US adolescents who completed the 2015–2021 National Youth Risk Behavior Survey (N = 61,298; 45.7% female). Data were collected between 2015 and 2021 and analyzed in 2023. Outcomes were binary response items asking about feeling sad/hopeless, having difficulty concentrating, remembering, or making decisions, and having a suicidal ideation. Preventive exposure variables were items asking about frequencies of aerobic PA and MSE with responses dichotomized to align with recommendations. Independent and joint associations were examined using robust Poisson regression with covariates selected using double selection lasso. Structural equation models examined the associations treating PA and MSE as continuous predictors and poor mental health as a latent dependent variable. ResultsMeeting either recommendation alone associated with a 4–10% lower prevalence of mental health problems (APR = 0.90–0.96, p < 0.05), and meeting both recommendations associated with a 15%–20% lower prevalence of mental health problems (APR = 0.80–0.85, p < 0.001). Although categorical joint associations were stronger in males (p < 0.05), multiplicative interactions were observed in females using continuous variables for PA and MSE (β = −0.09, p < 0.001). ConclusionMeeting aerobic PA and MSE recommendations associated with lower prevalence of mental health problems. Participation in MSE below recommended levels may be beneficial for females when combined with aerobic PA. Future research should examine these associations by acquiring contextual information and device-based assessments.

Life-course insecurity among young adults: Evidence for variation by employment status?

Previous research suggests that lack of employment security can lead young adults to experience a higher degree of insecurity with regard to their future life. We test the relationship between life-course insecurity, i.e. worrisome feelings with regard to one's own future, and young adults' employment status using a newly developed measurement instrument. Furthermore, we examine whether, in terms of life-course insecurity, specific groups of young people are more affected by insecure employment conditions based on their structural position. Survey data (n=1087) were collected within a Dutch representative panel among those aged 18-35. Structural equation modelling is used to construct latent dependent variables for experienced insecurity in four life domains, namely 'work', 'finances', 'partner and family' and 'leisure and personal development'. Results show that, while controlling for gender, life phase, education level and level of neuroticism, lack of employment is associated with higher insecurity in all four domains of life. Precarious employment based on a flexible contract is associated with higher insecurity regarding 'work', 'finances' and 'partner and family'. Moreover, we find the relationship between lack of employment and life-course insecurity to be stronger for young people in the 26-35 age bracket. However, higher educational attainment does not attenuate the positive relationship between precarious employment and life-course insecurity. The findings of this study inform our theoretical understanding of agency within the life course of young adults by signaling insecure labour market attachment as a potential constraint to formulating plans for the future.

Technology Improvement and Micro, Small, and Medium Enterprises (MSMEs) on Regional Development in Special Region of Yogyakarta

A new perspective on regional economic development is development capital generated from the region. MSMEs and Higher Education as development capital of the Special Region of Yogyakarta (DIY) have different characteristics. Development is recognized as a process in which interrelationships and effects between the variables lead to regional development. Therefore, it is necessary to identify the effect of regional development capital on regional development. This study aims to examine the use of PLS-SEM in designing a model of the relationship between technological innovation, capital, and workforce on regional development. This current study also aims to observe the effect of latent predictor variables (Xi) of technology, innovation, capital, and workforce on LPE, HDI, and poverty rate as the latent dependent variable of regional development (Yi). Regional development capital has a positive effect on LPE. Technology has a positive and significant effect on innovation by 92.6%. Innovation Technology positively affects Economic Growth by 14.44% but is not significant. Though it is not significant, regional capital proxied by MSMEs positively affects economic growth. The workforce has a negative and insignificant effect on the economic growth of -28.3%. LPE and HDI have a negative effect on the poverty rate, although not significant at -6.8% and -13.3%, respectively. LPE and HDI play a role in reducing poverty. However, they perform on different paths. Workforce productivity is still low. Thus, it has not been able to boost economic growth and reduce poverty. These results are expected to be utilized as a basis for developing strategies for technology utilization by MSMEs

Nonnegative matrix tri-factorization based clustering in a heterogeneous information network with star network schema

Heterogeneous Information Networks (HINs) contain multiple types of nodes and edges; therefore, they can preserve the semantic information and structure information. Cluster analysis using an HIN has obvious advantages over a transformation into a homogenous information network, which can promote the clustering results of different types of nodes. In our study, we applied a Nonnegative Matrix Tri-Factorization (NMTF) in a cluster analysis of multiple metapaths in HIN. Unlike the parameter estimation method of the probability distribution in previous studies, NMTF can obtain several dependent latent variables simultaneously, and each latent variable in NMTF is associated with the cluster of the corresponding node in the HIN. The method is suited to co-clustering leveraging multiple metapaths in HIN, because NMTF is employed for multiple nonnegative matrix factorizations simultaneously in our study. Experimental results on the real dataset show that the validity and correctness of our method, and the clustering result are better than that of the existing similar clustering algorithm.

Modelling of threats that affect Cyano-HABs in an eutrophicated reservoir: First phase towards water security and environmental governance in watersheds

Cyano-HABs are proliferating around the world due to anthropogenic nutrient enrichment of freshwater bodies. This study seeks to obtain a holistic vision over the various threats that affect the Cyano-HABs of Umia basin and especially of A Baxe reservoir (Galicia, NW Spain), through the method of Partial least squares path modelling (PLS-PM). The A Baxe reservoirs is a fundamental source of drinking water supply to surrounding dwellings. This study identifies and quantify the variables that increase contaminant concentration and decrease ecological integrity, as well as how this scenario evolved over various hydrologic years. In this regard, the PLS-PM equations will be robust and powerful tools to predict changes in eutrophication and ecological integrity, as response to measures implemented in the basin that can improve water quality. The dependent latent variables are “Eutrophication” (chlorophyl-a, Microcystis sp.) and “Ecological Integrity” (METI Bioindicator). The independent latent variables are “SWP”, which represents surface water parameters (phosphorus, nitrogen and pH) and “Climatic Conditions” (temperature, precipitation). The PLS-PM results revealed that 51.0% of “Eutrophication” is predicted by the independent variables. The connections between latent variables are quantified through path coefficients (β). The “SWP” contributes by increasing “Eutrophication” (β = 0.235), the same occurring with the “Climatic Conditions” (β = −0.672). The variables “Eutrophication” (β = −0.217) and “SWP” (β = −0.483) lower the “Ecological Integrity”. On the other hand, different trophic scenarios, adapted to the temperature increase predicted for the study area, were tested, and it was found that ecological integrity would improve by 46% if the oligotrophic state were reached. Therefore, it is recommended to prevent pollution by means of water control and governance plans, as well as corrective and preventive measures, which guarantee the water security of the river basins. Despite the complex mathematics behind the PLS-PM models, their user-friendly development and application through interactive graphical interfaces make them easily transposable to other eutrophic reservoirs, widening the readership of these studies focused on multiple-geosphere assessment of environmental impacts.

Which factors determine students’ water-saving behaviors? Evidence from China colleges

ABSTRACT In this paper, we attempt to answer the following questions: Which factors determine college students’ water-saving behaviors? The answer can enable colleges to take targeted water management measures to promote water-saving behaviors of students. Based on the theory of planned behavior and the combination of attitude-situation-behavior theory, this paper built a comprehensive theoretical model with sociodemographic characteristics, psychological characteristics as external dependent latent variables, situational factors as moderating variables, and water-saving behaviors as internal dependent latent variables. The research investigated 742 students in China colleges, and used corresponding methods to analyze the different types of the influencing factors of students’ water-saving behaviors. The results show that the sociodemographic characteristics of college students have no significant impact on water-saving behaviors; the psychological characteristics have a significant impact on individual water-saving behaviors; the situational factors have a significant moderating effect on individual water-saving behaviors of students.

Gut microbiome mediates host genomic effects on phenotypes: a case study with fat deposition in pigs

A large number of studies have highlighted the importance of gut microbiome composition in shaping fat deposition in mammals. Several studies have also highlighted how host genome controls the abundance of certain species that make up the gut microbiota. We propose a systematic approach to infer how the host genome can control the gut microbiome, which in turn contributes to the host phenotype determination. We implemented a mediation test that can be applied to measured and latent dependent variables to describe fat deposition in swine (Sus scrofa). In this study, we identify several host genomic features having a microbiome-mediated effects on fat deposition. This demonstrates how the host genome can affect the phenotypic trait by inducing a change in gut microbiome composition that leads to a change in the phenotype. Host genomic variants identified through our analysis are different than the ones detected in a traditional genome-wide association study. In addition, the use of latent dependent variables allows for the discovery of additional host genomic features that do not show a significant effect on the measured variables. Microbiome-mediated host genomic effects can help understand the genetic determination of fat deposition. Since their contribution to the overall genetic variance is usually not included in association studies, they can contribute to filling the missing heritability gap and provide further insights into the host genome – gut microbiome interplay. Further studies should focus on the portability of these effects to other populations as well as their preservation when pro-/pre-/anti-biotics are used (i.e. remediation).

Water security and watershed management assessed through the modelling of hydrology and ecological integrity: A study in the Galicia-Costa (NW Spain)

Water management is a crucial tool for addressing the increasing uncertainties caused by climate change, biodiversity loss and the conditions of socioeconomic limits. The multiple factors affecting water resources need to be successfully managed to achieve optimal governance and thus move towards water security. This study seeks to obtain a holistic vision of the various threats that affect the ecological integrity of the basins that form the hydrological district of Galicia-Costa, through the method of partial least squares path modelling (PLS-PM). The data is analysed overall for the hydrological years from 2009 to 2015. The independent latent variables are “Anthropogenic” (comprising the percentage of water bodies with edges alongside artificial surfaces, the percentage connected to artificial land use patches, the edge density of artificial surfaces and population density) and “Nature” (edge density of forestry land uses, edge length of land water bodies alongside forested areas and the percentage of land occupied by the largest patch of forest). The dependent latent variables are “SWP”, which represents surface water parameters (biological oxygen demand, chlorides, conductivity and dissolved iron) and “Ecological Integrity” (METI Bioindicator). The connections between latent variables are uantified through path coefficients (β). From an overall perspective, the PLS-PM results reveal that 69.0% of “SWP” is predicted by the independent variables (R2 = 0.690), “Anthropogenic” contributes by increasing SWP (β = 0.471), while “Nature” decreases the concentration of SWP (β = −0.523), which indicates the polluting parameters in the water. The variables “Anthropogenic” (β = −0.351) and “SWP” (β = −0.265) lower the quality of “Ecological Integrity”. This variable must be managed through soil conservation measures for the benefit of water security. This study has been able to identify and quantify the variables that increase contaminant concentration and decrease ecological integrity, providing a promising methodology that facilitates protection and correction measures to guarantee water safety.

Interval censored regression with fixed effects

SummaryThis paper considers identification and estimation of a fixed‐effects model with an interval‐censored dependent variable. In each time period, the researcher observes the interval (with known endpoints) in which the dependent variable lies but not the value of the dependent variable itself. Two versions of the model are considered: a parametric model with logistic errors and a semiparametric model with errors having an unspecified distribution. In both cases, the error disturbances can be heteroskedastic over cross‐sectional units as long as they are stationary within a cross‐sectional unit; the semiparametric model also allows for serial correlation of the error disturbances. A conditional‐logit‐type composite likelihood estimator is proposed for the logistic fixed‐effects model, and a composite maximum‐score‐type estimator is proposed for the semiparametric model. In general, the scale of the coefficient parameters is identified by these estimators, meaning that the causal effects of interest are estimated directly in cases where the latent dependent variable is of primary interest (e.g., pure data‐coding situations). Monte Carlo simulations and an empirical application to birthweight outcomes illustrate the performance of the parametric estimator.

Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models

Dynamically rescaled Hamiltonian Monte Carlo is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified parameterization so that the reparameterized target distribution has close to constant scaling properties, and thus is easily sampled using standard (Euclidian metric) Hamiltonian Monte Carlo. Provided that the parameterizations of the conditional distributions specifying the hierarchical model are “constant information parameterizations” (CIPs), the relation between the modified- and original parameterization is bijective, explicitly computed, and admit exploitation of sparsity in the numerical linear algebra involved. CIPs for a large catalogue of statistical models are presented, and from the catalogue, it is clear that many CIPs are currently routinely used in statistical computing. A relation between the proposed methodology and a class of explicitly integrated Riemann manifold Hamiltonian Monte Carlo methods is discussed. The methodology is illustrated on several example models, including a model for inflation rates with multiple levels of nonlinearly dependent latent variables. Supplementary materials for this article are available online.

SEMIPARAMETRIC ESTIMATION OF CENSORED SPATIAL AUTOREGRESSIVE MODELS

This study considers the estimation of spatial autoregressive models with censored dependent variables, where the spatial autocorrelation exists within the uncensored latent dependent variables. The estimator proposed in this paper is semiparametric, in the sense that the error distribution is not parametrically specified and can be heteroskedastic. Under a median restriction, we show that the proposed estimator is consistent and asymptotically normally distributed. As an empirical illustration, we investigate the determinants of the risk of assault and other violent crimes including injury in the Tokyo metropolitan area.

Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef.

Structural equation models involving latent variables are useful tools for formulating hypothesized models defined by theoretical variables and causal links between these variables. The objectives of this study were: (1) to identify latent variables underlying carcass and meat quality traits and (2) to perform whole-genome scans for these latent variables in order to identify genomic regions and individual genes with both direct and indirect effects. A total of 726 steers from an Angus-Brahman multibreed population with records for 22 phenotypes were used. A total of 480 animals were genotyped with the GGP Bovine F-250. The single-step genomic best linear unbiased prediction method was used to estimate the amount of genetic variance explained for each latent variable by chromosome regions of 20 adjacent SNP-windows across the genome. Three types of genetic effects were considered: (1) direct effects on a single latent phenotype; (2) direct effects on two latent phenotypes simultaneously; and (3) indirect effects. The final structural model included carcass quality as an independent latent variable and meat quality as a dependent latent variable. Carcass quality was defined by quality grade, fat over the ribeye and marbling, while the meat quality was described by juiciness, tenderness and connective tissue, all of them measured through a taste panel. From 571 associated genomic regions (643 genes), each one explaining at least 0.05% of the additive variance, 159 regions (179 genes) were associated with carcass quality, 106 regions (114 genes) were associated with both carcass and meat quality, 242 regions (266 genes) were associated with meat quality, and 64 regions (84 genes) were associated with carcass quality, having an indirect effect on meat quality. Three biological mechanisms emerged from these findings: postmortem proteolysis of structural proteins and cellular compartmentalization, cellular proliferation and differentiation of adipocytes, and fat deposition.

The lower bound and the causes of monetary policy inertia: evidence from Sweden

ABSTRACTIn 2009, in the midst of a global recession, Sweden’s Riksbank approached a lower bound on nominal interest rates. This encounter with the lower bound provides a natural experiment for investigating the causes of monetary policy inertia. To exploit this experiment, we estimate Taylor rules with Tobit specifications that permit both interest rate smoothing and persistent shocks (serial correlation) as explanations for inertia. The interest rate smoothing hypothesis leads to a specification in which lagged actual values of the dependent variable appear on the right-hand side of the Taylor rule, while the persistent shocks hypothesis leads to a specification in which lagged values of an unobserved latent dependent variable appear on the right-hand side of the Taylor rule. The divergence of actual and latent dependent variables that occurs at the lower bound provides leverage in distinguishing the two hypotheses. For a conventional Taylor rule, we find evidence of both sources of inertia. For a modified Taylor rule that includes a measure of financial stress, our evidence suggests that interest rate smoothing is the principal source of monetary policy inertia.

New Product Development and Innovation in the Maquiladora Industry: A Causal Model

Companies seek to stand out from their competitors and react to other competitive threats. Making a difference means doing things differently in order to create a product that other companies cannot provide. This can be achieved through an innovation process. This article analyses, by means of a structural equation model, the current situation of Mexican maquiladora companies, which face the constant challenge of product innovation. The model associates three success factors for new product development (product, organization, and production process characteristics as independent latent variables) with benefits gained by customers and companies (dependent latent variables). Results show that, in the Mexican maquiladora sector, organizational characteristics and production processes characteristics explain only 31% of the variability (R2 = 0.31), and it seems necessary to integrate other aspects. The relationship between customer benefits and company benefits explains 58% of the variability, the largest proportion in the model (R2 = 0.58).

Two paths to customer loyalty: the moderating effect of the differentiation level strategy in the performance-satisfaction-value-intentions relationship

PurposeThis paper aims to show how consumers process information differently under two dissimilar types of brand strategies. By analysing data from dishwashing detergent consumers who evaluated two different brands, one with a high differentiated/performance/price brand (HB) strategy and other with a low differentiated/cost/price brand (LB) strategy, it is shown how the structural relationship between some constructs differ under each price brand strategy. With a structural path analysis, the product performance, customer satisfaction, customer perceived value and behavioural intentions relationship are assessed. Additionally, the moderating effect of the brand strategy in this structural relationship is tested.Design/methodology/approachData of a sample ofn= 273 Mexican dishwashing detergent users were analysed. Respondents evaluated two brands of dishwashing detergent. One brand followed a high differentiated/performance/price strategy (Axion) and the other used a low differentiated/cost/price strategy (Roma). Participants had to have been consumers of both brands of detergents. A structural path analysis to show the moderating effect of the brand strategy was implemented. A nested comparison invariance test for structural weights to corroborate differences was used, as well.FindingsResults confirm structural differences between both brand strategies in the constructs relationship. The HB strategy showed that both satisfaction and perceived value have a mediating effect between product performance and brand intentions; on the other hand, the LB strategy showed that only satisfaction has a mediating effect between these two constructs. The explanation amount for dependent latent variables was higher for the HB strategy than for the LB strategy.Originality/valueThis study offers a distinct view of the differentiated/quality brand strategy – low cost/price brand strategy duality. It provides a theoretical-empirical explanation of the cognitive processes that both strategies involve for a customer. As well, this study conceptually relates the generic strategies theory with the use of specific brand strategies.

Identification of Panel Data Models with Endogenous Censoring

This paper analyzes the identification question in censored panel data models, where the censoring can depend on both observable and unobservable variables in arbitrary ways. Under some general conditions, we derive the tightest sets on the parameter of interest. These sets (which can be singletons) represent the limit of what one can learn about the parameter of interest given the model and the data in that every parameter that belongs to these sets is observationally equivalent to the true parameter. We consider two separate sets of assumptions, motivated by the previous literature, each controlling for unobserved heterogeneity with an individual specific (fixed) effect. The first imposes a stationarity assumption on the unobserved disturbance terms, along the lines of Manski (1987), and Honore (1993). The second is a nonstationary model that imposes a conditional independence assumption. For both models, we provide sufficient conditions for these models to point identify the parameters. Since our identified sets are defined through parameters that obey first order dominance, we outline easily implementable approaches to build confidence regions based on recent advances in Linton et.al.(2010) on bootstrapping tests of stochastic dominance. We also extend our results to dynamic versions of the censored panel models in which we consider lagged observed, latent dependent variables and lagged censoring indicator variables as regressors.

Semiparametric Estimation of Censored Spatial Autoregressive Models

This study considers the estimation of spatial autoregressive models with censored dependent variables, where the spatial autocorrelation exists within the uncensored latent dependent variables. The estimator proposed in this paper is semiparametric in the sense that the error distribution is not parametrically specified and can be heteroscedastic. Under a median restriction, we show that the proposed estimator is consistent and asymptotically normally distributed. A set of Monte Carlo simulations is conducted to evaluate the finite sample performance of the estimator. As an empirical illustration, we investigate the determinants of household burglary risk in the Tokyo metropolitan area.

Nonparametric finite translation hidden Markov models and extensions

In this paper, we consider nonparametric finite translation hidden Markov models, or more generally finite translation mixtures with dependent latent variables. We prove that all the parameters of the model are identifiable as soon as the matrix that defines the joint distribution of two consecutive latent variables is non-singular and the translation parameters are distinct. Under this assumption, we provide a consistent estimator of the number of populations, of the translation parameters and of the distribution of two consecutive latent variables, which we prove to be asymptotically normally distributed under mild dependency assumptions. We propose a nonparametric estimator of the unknown translated density. In case the latent variables form a Markov chain, we prove that this estimator is minimax adaptive over regularity classes of densities.