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Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understanding the large-scale response-predictor association network structures via layers of sparse latent factors ranked by importance. Yet sparsity and orthogonality have been two largely incompatible goals. To accommodate both features, in this paper, we suggest the method of sparse orthogonal factor regression (SOFAR) via the sparse singular value decomposition with orthogonality constrained optimization to learn the underlying association networks, with broad applications to both unsupervised and supervised learning tasks, such as biclustering with sparse singular value decomposition, sparse principal component analysis, sparse factor analysis, and spare vector autoregression analysis. Exploiting the framework of convexity-assisted nonconvex optimization, we derive nonasymptotic error bounds for the suggested procedure characterizing the theoretical advantages. The statistical guarantees are powered by an efficient SOFAR algorithm with convergence property. Both computational and theoretical advantages of our procedure are demonstrated with several simulations and real data examples. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to explore the latent relations of the selected morphometric, physiological and biochemical parameters. Thirty-six variables (12 morphometric, 9 physiological and 15 biochemical variables) were measured on 317 male-entities aged 17 - 35 y/o. The obtained data were analysed through the factor analysis of the first and second order. The statistical analyses were performed with the IBM SPSS Statistics software package, version 20. The factorization of the first order enabled extraction of 12 latent factors that explain 74.8% of the total variance, while the factorization of the second order enabled extraction of five latent components that explain 51.39% of the total variance. The final results of this study confirm the main hypothesis that there exist the numbers of latent variables that explain the latent structure of selected biometric measures. The nature of the extracted latent factors/components in both orders of factorization is relatively clear, understandable, and easy to interpret. The higher projections of the manifest biometric variables on the extracted latent factors of the first and second order were accordingly with the nature of the measured variables. The results of this research might be considered as one step more in the holistic approach to the biometric measures. [ABSTRACT FROM AUTHOR]

We construct a structural model of entry into self‐employment to evaluate the impact of policies supporting entrepreneurship. Previous work has recognized that workers may opt for self‐employment due to the nonpecuniary benefits of running a business and not necessarily because they are good at it. Other literature has examined how socio‐emotional skills, such as personality traits, affect selection into self‐employment. We link these two lines of inquiry. The model we estimate captures three factors that affect selection into self‐employment: credit constraints, relative earnings, and preferences. We incorporate personality traits by allowing them to affect sector‐specific earnings as well as preferences. The estimated model reveals that the personality traits that make entrepreneurship profitable are not always the same traits driving people to open a business. This has important consequences for entrepreneurship policies. For example, subsidies for small businesses do not attract talented‐but‐reluctant entrepreneurs, but instead attract individuals with personality traits associated with strong preferences for running a business and low‐quality business ideas. [ABSTRACT FROM AUTHOR]

The study explores the causal relationships between latent factors and various observed external variables along with their ability to explain multimodal behaviours of post-secondary students in Toronto. Multimodality was measured by the number of unique modes used by any individual for their daily travels. As opposed to using a single mode of transportation, use of multiple modes for different trips indicates the degree and the nature of multimodality. For the empirical investigation, the study uses structural equation modelling and ordered probability modelling for a dataset collected through a large-scale travel diary survey among four major universities in Toronto representing over 180 000 post-secondary students in the region. The results of the empirical investigation reveal that latent attitudes are influential factors in determining the multimodal behaviour of post-secondary students in Toronto. The results also found that mobility tool ownership and land use characteristics have a significant influence on those latent attitudes, and are direct determinants of the degree of multimodality. In particular, the results indicate that smart fare payment cards have a considerable effect on latent attitudes for post-secondary students. These findings could have policy implication from the planning perspective and should warrant further investigations. [ABSTRACT FROM AUTHOR]

Freeform buildings differ from conventional buildings in a way that they have irregularly shaped facades. Oftentimes, freeform projects run behind schedule and over the budget. This study aimed at exploring factors that influence time and cost overruns on freeform buildings. The study employed questionnaire surveys to collect data from experts with freeform project experience, and the responses were analyzed using R statistical program. The study used exploratory factor analysis to identify the most critical latent factors to freeform project success. The study found factors related to application of Building Information Modeling (BIM) and fabrication of freeform components as very crucial to timely completion, whereas factors related to personnel involved as essential to completing within budget. Additionally, the study summarized time overrun variables into three latent factors; uniqueness, capabilities and practices of the parties involved, and level of preparation for the project. The cost overrun latent factors were: cost uncertainty, client decisions, extent of preparation, and delay. Understanding the causes of time and cost overruns on freeform buildings identified in this study can help clients make informed decisions regarding undertaking freeform projects as well as help consultants and contractors minimize the risk of encountering such overruns. [ABSTRACT FROM AUTHOR]

Joint species distribution models (JSDMs) account for biotic interactions and missing environmental predictors in correlative species distribution models. Several different JSDMs have been proposed in the literature, but the use of different or conflicting nomenclature and statistical notation potentially obscures similarities and differences among them. Furthermore, new JSDM implementations have been illustrated with different case studies, preventing direct comparisons of computational and statistical performance.We aim to resolve these outstanding issues by (a) highlighting similarities among seven presence–absence JSDMs using a clearly defined, singular notation; and (b) evaluating the computational and statistical performance of each JSDM using six datasets that vary widely in numbers of sites, species, and environmental covariates considered.Our singular notation shows that many of the JSDMs are very similar, and in turn parameter estimates of different JSDMs are moderate to strongly, positively correlated. In contrast, the different JSDMs clearly differ in computational efficiency and memory limitations.Our framework will allow ecologists to make educated decisions about the JSDM that best suits their objective, and enable wider uptake of JSDM methods among the ecological community. [ABSTRACT FROM AUTHOR]

In recent years, the recommender systems have become essential tools for companies to offer their products in a personalized way and to improve the user experience. The primary objective of these systems is to propose products or services to the user, according to specific criteria, such as their interests, their preferences, the place where they work or where they live. The problem arises when the system recommends products from an establishment to users who never visited that establishment. Besides, it is known that the order in which users purchase certain products or services can impact on the recommendation. To deal with these two problems, we propose a process that combines two widely used models: latent factors and matrix factorization. Also, to include temporality in our results, we use the \emph{Sequitur} algorithm. In order to test our proposal, we have used a database with approximately $65$ million banking transactions. The results obtained show the efficiency of our proposal in terms of average consumption ticket increase. [ABSTRACT FROM AUTHOR]

An inherently non-negative latent factor model is proposed to extract non-negative latent factors from non-negative big sparse matrices efficiently and effectively. A single-element-dependent sigmoid function connects output latent factors with decision variables, such that non-negativity constraints on the output latent factors are always fulfilled and thus successfully separated from the training process with respect to the decision variables. Consequently, the proposed model can be easily and fast built with excellent prediction accuracy. Experimental results on an industrial size sparse matrix are given to verify its outstanding performance and suitability for industrial applications.

The problem of predicting students’ performance has been recently tackled by using matrix factorization, a popular method applied for collaborative filtering based recommender systems. This problem consists of predicting the unknown performance or score of a particular student for a task s/he did not complete or did not attend, according to the scores of the tasks s/he did complete and the scores of the colleagues who completed the task in question. The solving method considers matrix factorization and a gradient descent algorithm in order to build a prediction model that minimizes the error in the prediction of test data. However, we identified two key aspects that influence the accuracy of the prediction. On the one hand, the model involves a pair of important parameters: the learning rate and the regularization factor, for which there are no fixed values for any experimental case. On the other hand, the datasets are extracted from virtual classrooms on online campuses and have a number of implicit latent factors. The right figures are difficult to ascertain, as they depend on the nature of the dataset: subject, size, type of learning, academic environment, etc. This paper proposes some approaches to improve the prediction accuracy by optimizing the values of the latent factors, learning rate, and regularization factor. To this end, we apply optimization algorithms that cover a wide search space. The experimental results obtained from real-world datasets improved the prediction accuracy in the context of a thorough search for predefined values. Obtaining optimized values of these parameters allows us to apply them to further predictions for similar datasets.

Many cities are creating policies and programs aimed at expanding bicycling mode shares. Attitudes towards bicycling in combination with weather conditions, however, can strongly influence the decision to ride. In locations with wide annual seasonal variation, attitudes can radically alter demand on bicycle networks across the year. Though researchers have looked at weather impacts on bicycling, the link between attitudinal factors that might impact riding decisions and seasonal variation remains understudied. This paper investigates heterogeneous taste preferences about the inclination to bicycle for riders who ride only in the warm weather and those who ride all year long (including during severe winters). This research relies on survey data from Edmonton, Canada and presents results from a hybrid discrete choice model. After controlling for age, sex, education, income, the supply of bike lanes, and the latent variable “bicycling inclination,” the results indicate that attitudes have a significantly positive impact on the decision to ride across seasons. The findings suggest that public education and season-specific training programs—particularly aimed at adults and women—have the potential to increase bicycling all year around. [ABSTRACT FROM AUTHOR]

Consumer behavior and marketing research have shown that brand has significant influence on product reviews and product purchase decisions. However, there is very little work on incorporating brand related factors into product recommender systems. Meanwhile, the similarity in brand preference between a user and other socially connected users also affects her adoption decisions. To integrate seamlessly the individual and social brand related factors into the recommendation process, we propose a novel model called Social Brand–Item–Topic (SocBIT). As the original SocBIT model does not enforce non-negativity, which poses some difficulty in result interpretation, we also propose a non-negative version, called SocBIT+. Both SocBIT and SocBIT+ return not only user topic interest, but also brand-related user factors, namely user brand preference and user brand-consciousness. The former refers to user preference for each brand, the latter refers to the extent to which a user relies on brand to make her adoption decisions. Our experiments on real-world datasets demonstrate that SocBIT and SocBIT+ significantly improve rating prediction accuracy over state-of-the-art models such as Social Regularization Ma et al. (in: ACM conference on web search and data mining (WSDM), 2011), Recommendation by Social Trust Ensemble Ma et al. (in: ACM conference on research and development in information retrieval (SIGIR), 2009a) and Social Recommendation Ma et al. (in: ACM conference on information and knowledge management (CIKM), 2008), which incorporate only the social factors. Specifically, both SocBIT and SocBIT+ offer an improvement of at least 22% over these state-of-the-art models in rating prediction for various real-world datasets. Last but not least, our models also outperform the mentioned models in adoption prediction, e.g., they provide higher precision-at-N and recall-at-N. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to study the corporate social responsibility (CSR) practices of luxury chain hotels situated in Delhi, National Capital Region (NCR), India. This research involves the primary and secondary data. Primary data were collected through a self-designed questionnaire filled up by employees working with selected hotels which consist offive national and five international hotels situated in India. Secondary data were collected through papers published in various journals. Data collected was analyzed and interpreted by using IBM SPSS 20.0. Interpretation of the data were based on the perception of employees working in the respective hotels. The paper identifies different latentfactors through exploratory factor analysis (EFA) which categorizes CSR into four segments named as social activities, environmental activities, economic activities and philanthropic activities. We also applied confirmatory factor analysis to assess convergent and discriminant validity. [ABSTRACT FROM AUTHOR]

Modern processes of reforming the agroindustrial complex of Ukraine, a large part of which belongs to the agrarian sector, under the globalization of world economic processes in the agricultural market require that domestic agroindustrial enterprises improve and rationally manage innovative activities (IА). The effective solution of problems that arise in this context in the rapidly changing economic and social and political environment requires that analytical methods in the management of the enterprises of the agroindustrial complex (AIC) be used on the basis of the tools of economic and mathematical modelling, taking into account characteristics of agrarian production. The main approaches to modelling systems related to IА management are either descriptive or insufficiently formalized, and suggest virtual experimenting with IA management, avoiding the possibility of assessing the impact and consequences of different management options in the long view, minimizing IA risks when making managerial decisions. The subject matter of the article is the tools of economic and mathematical simulation of the development of innovative activities of the domestic agroindustrial complex. The goal is to develop tools for modelling innovative activities of enterprises of the agroindustrial complex of Ukraine in the form of analytical dependencies of impact factors. The objective is to research the dynamics of innovative activities of enterprises of the agroindustrial complex of Ukraine in terms of its components under the globalization of world economic processes in the market of agricultural products. General scientific methods are used, such as system analysis – to determine the peculiarities of development of IА of agroindustrial production in Ukraine, factor analysis – to formalize the cause-and-effect relationships of the investigated factors of AIC enterprises impact on IA. The following results were obtained: on the basis of the analysis of the dynamics of changes in the major indices of domestic agricultural production, the problem aspects were identified and the components of IA of agricultural production were determined, latent factors with prolonged impact on the level of innovative activities of agricultural enterprises of Ukraine were calculated. Conclusions: the calculated latent factors of the impact of AIC enterprises on IА can be efficiently used in foreign economic activities as recommendations for determining the IА strategy of Ukraine in the world agricultural market.

Collaborative filtering is a popular strategy in recommender systems area. This approach gathers users’ ratings and then predicts what users will rate based on their similarity to other users. However, most of the collaborative filtering methods have faced problems such as sparseness and scalability. This paper presents a non-negative matrix factorisation method to alleviate these problems via decomposing rating matrix into user matrix and item matrix. This method tries to find two non-negative user matrix and item matrix whose product can well estimate the rating matrix. This approach proposes updated rules to learn the latent factors for factorising the rating matrix. The proposed method can estimate all the unknown ratings and its computational complexity is very low. Empirical studies on benchmark datasets show that the proposed method is more tolerant of the sparseness and scalability problems. [ABSTRACT FROM AUTHOR]

Background: De novo inference of clinically relevant gene function relationships from tumor RNA-seq remains a challenging task. Current methods typically either partition patient samples into a few subtypes or rely upon analysis of pairwise gene correlations that will miss some groups in noisy data. Leveraging higher dimensional information can be expected to increase the power to discern targetable pathways, but this is commonly thought to be an intractable computational problem. Methods: In this work we adapt a recently developed machine learning algorithm for sensitive detection of complex gene relationships. The algorithm, CorEx, efficiently optimizes over multivariate mutual information and can be iteratively applied to generate a hierarchy of relatively independent latent factors. The learned latent factors are used to stratify patients for survival analysis with respect to both single factors and combinations. These analyses are performed and interpreted in the context of biological function annotations and protein network interactions that might be utilized to match patients to multiple therapies. Results: Analysis of ovarian tumor RNA-seq samples demonstrates the algorithm's power to infer well over one hundred biologically interpretable gene cohorts, several times more than standard methods such as hierarchical clustering and k-means. The CorEx factor hierarchy is also informative, with related but distinct gene clusters grouped by upper nodes. Some latent factors correlate with patient survival, including one for a pathway connected with the epithelial-mesenchymal transition in breast cancer that is regulated by a microRNA that modulates epigenetics. Further, combinations of factors lead to a synergistic survival advantage in some cases. Conclusions: In contrast to studies that attempt to partition patients into a small number of subtypes (typically 4 or fewer) for treatment purposes, our approach utilizes subgroup information for combinatoric transcriptional phenotyping. Considering only the 66 gene expression groups that are found to both have significant Gene Ontology enrichment and are small enough to indicate specific drug targets implies a computational phenotype for ovarian cancer that allows for 366 possible patient profiles, enabling truly personalized treatment. The findings here demonstrate a new technique that sheds light on the complexity of gene expression dependencies in tumors and could eventually enable the use of patient RNA-seq profiles for selection of personalized and effective cancer treatments. [ABSTRACT FROM AUTHOR]

Urgency of the research. Modern processes of reforming AIC on the basis of the critical state of Ukraine's economy, much of which the agricultural sector, demands from AIC to improve their organizational and administrative activities based on implementation and good governance of innovation process (IP). Target setting. Efficient solving of occurring issues in these rapidly changing conditions in the economic and sociopolitical environment requires the use of analytical techniques in the management of agricultural enterprises, IPbased instruments of economic and mathematical modeling, taking into consideration peculiarities of agricultural production. Actual scientific researches and issues analysis. The main approaches to the design of the systems associated with managing IP, realized with descriptive tools or presented with insufficient formalization, and offer practically an experiment with managing IP, avoiding the possibility to assess the impact and consequences of various options for managing IP in the future prospect of minimizing risks in management IA while making management decisions. Uninvestigated parts of general matters defining. The scientists haven’t yet developed the tools of economic and mathematical modeling for the control and management of full technological cycle, necessary for innovation in production. The research objective. The article studies the patterns of development and management of IP enterprises in Ukraine, as well as mechanisms of their internal and external relationships. The statement of basic materials. The dynamics of ID agricultural enterprises in terms of its components has been analyzed, was researched its ID development tendencies based on the analysis of the dynamics of changes in key indicators of the domestic agricultural production. The factors influence the level ID agrarian enterprises and factor analysis was performed. With the results the system of latent factors models based on the calculated integral indicators of the components of their IA, including the dynamics of innovation in the enterprises, the dynamics of costs and IA funding sources, the dynamics of introducing IA. Conclusions. Calculated latent factors of IA agricultural enterprises, it should be appropriate to use as guidelines for foreign trade in order to determine the IA strategy for Ukraine on the world agricultural market. [ABSTRACT FROM AUTHOR]

Emotion analysis in social media is challenging. While most studies focus on positive and negative sentiments, the differentiation between emotions is more difficult. We investigate the problem as a collection of binary classification tasks on the basis of four opposing emotion pairs provided by Plutchik. We processed the content of messages by three alternative methods: structural and lexical features, latent factors, and natural language processing. The final prediction is suggested by classifiers deriving from the state of the art in machine learning. Results are convincing in the possibility to distinguish the emotions pairs in social media. [ABSTRACT FROM AUTHOR]

Volatility, represented in the form of conditional heteroscedasticity, plays an important role in controlling and forecasting risks in various financial operations including asset pricing, portfolio allocation, and hedging futures. However, modeling and forecasting multi-dimensional conditional heteroscedasticity are technically challenging. As the volatilities of many financial assets are often driven by a few common and latent factors, we propose in this article a dimension-reduction method to model a multivariate volatility process and to estimate a lower-dimensional space, to be called the volatility space, within which the dynamics of the multivariate volatility process is confined. The new method is simple to use, as technically it boils down to an eigenanalysis for a nonnegative definite matrix. Hence, it is applicable to the cases when the number of assets concerned is in the order of thousands (using an ordinary PC/laptop). On the other hand, the model has the capability to cater for complex conditional heteroscedasticity behavior for multi-dimensional processes. Some asymptotic properties for the new method are established. We further illustrate the new method using both simulated and real data examples. [ABSTRACT FROM PUBLISHER]

This paper makes both a methodological contribution as well as an empirical contribution. From a methodological perspective, we propose a new econometric approach for the estimation of joint mixed models that include a multiple discrete choice outcome and a nominal discrete outcome, in addition to the count, binary/ordinal outcomes, and continuous outcomes considered in traditional structural equation models. These outcomes are modeled together by specifying latent underlying unobserved individual lifestyle, personality, and attitudinal factors that impact the many outcomes, and generate the jointness among the outcomes. From an empirical perspective, we analyze residential location choice, household vehicle ownership choice, as well as time-use choices, and investigate the extent of association versus causality in the effects of residential density on activity participation and mobility choices. The sample for the empirical application is drawn from a travel survey conducted in the Puget Sound Region in 2014. The results show that residential density effects on activity participation and motorized auto ownership are both associative as well as causal, emphasizing that accounting for residential self-selection effects are not simply esoteric econometric pursuits, but can have important implications for land-use policy measures that focus on neo-urbanist design. [ABSTRACT FROM AUTHOR]

In recent years, lots of efforts have been devoted to the identification of the factors influencing residential energy consumption. Many factors affect energy consumption at the same time, leading to the lack of precision when identifying which factors are significant. This paper reports the results of performing factor analysis for examining the factors affecting residential energy consumption. Data gathered through interviews and surveys with the residents and of housing units in Tehran (capital of Iran) are used for this purpose. The database applied comprises 56 predictors, for 2087 observations. Thirteen latent factors related to households’ energy consumption were shown by the data. Finally, a regression model was employed in order to recognise the most important factors. The amount of electricity and natural gas consumption was used as the dependent variable in the regression model. The results obtained can help prioritise efforts for modifying parameters in order to reduce the energy consumption in the residential sector. [ABSTRACT FROM PUBLISHER]

An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance sampling and on a linear Gaussian approximating model from which the latent process can be simulated. Given the presence of a latent long-memory process, we require a modification of the importance sampling technique. In particular, the long-memory process needs to be approximated by a finite dynamic linear process. Two possible approximations are discussed and are compared with each other. We show that an autoregression obtained from minimizing mean squared prediction errors leads to an effective and feasible method. In our empirical study, we analyze ten daily log-return series from the S&P 500 stock index by univariate and multivariate long-memory stochastic volatility models. We compare the in-sample and out-of-sample performance of a number of models within the class of long-memory stochastic volatility models. [ABSTRACT FROM PUBLISHER]

Modern species distribution models account for spatial autocorrelation in order to obtain unbiased statistical inference on the effects of covariates, to improve the model's predictive ability through spatial interpolation and to gain insight in the spatial processes shaping the data. Somewhat analogously, hierarchical approaches to community-level data have been developed to gain insights into community-level processes and to improve species-level inference by borrowing information from other species that are either ecologically or phylogenetically related to the focal species., We unify spatial and community-level structures by developing spatially explicit joint species distribution models. The models utilize spatially structured latent factors to model missing covariates as well as species-to-species associations in a statistically and computationally effective manner., We illustrate that the inclusion of the spatial latent factors greatly increases the predictive performance of the modelling approach with a case study of 55 species of butterfly recorded on a 10 km × 10 km grid in Great Britain consisting of 2609 grid cells. [ABSTRACT FROM AUTHOR]

Current recommender systems are very inefficient. There are many metrics that are used to measure the effectiveness of recommender systems. These metrics often include “conversion rate” and “click through rate”. Recently, these rates are in low single digit (less than 10%). In other words, more than 90% of times, the model that the targeting system is based on, produces noise. The belief in this work is that the main problem leading to getting such unsatisfactory outcomes is the modeling problem. Much of the modeling problem could be represented and exemplified in treating users and items as member of clusters (segments). In this work, we consider full personalization of recommendation systems. We aim at personalization of users and items simultaneously. [ABSTRACT FROM AUTHOR]

Background: In the context of high-throughput molecular data analysis it is common that the observations included in a dataset form distinct groups; for example, measured at different times, under different conditions or even in different labs. These groups are generally denoted as batches. Systematic differences between these batches not attributable to the biological signal of interest are denoted as batch effects. If ignored when conducting analyses on the combined data, batch effects can lead to distortions in the results. In this paper we present FAbatch, a general, model-based method for correcting for such batch effects in the case of an analysis involving a binary target variable. It is a combination of two commonly used approaches: location-and-scale adjustment and data cleaning by adjustment for distortions due to latent factors. We compare FAbatch extensively to the most commonly applied competitors on the basis of several performance metrics. FAbatch can also be used in the context of prediction modelling to eliminate batch effects from new test data. This important application is illustrated using real and simulated data. We implemented FAbatch and various other functionalities in the R package bapred available online from CRAN. Results: FAbatch is seen to be competitive in many cases and above average in others. In our analyses, the only cases where it failed to adequately preserve the biological signal were when there were extremely outlying batches and when the batch effects were very weak compared to the biological signal. Conclusions: As seen in this paper batch effect structures found in real datasets are diverse. Current batch effect adjustment methods are often either too simplistic or make restrictive assumptions, which can be violated in real datasets. Due to the generality of its underlying model and its ability to perform well FAbatch represents a reliable tool for batch effect adjustment for most situations found in practice. [ABSTRACT FROM AUTHOR]

Based on a meta-analysis, Redick and Lindsey (2013) found that complex span and n-back tasks show an average correlation of r = .20, and concluded that complex span and n-back tasks cannot be used interchangeably as working memory measures in research applications (p. 1102). Here, we comment on this conclusion from a psychometric perspective. In addition to construct variance, performance on a test contains measurement error, task-specific variance, and paradigm-specific variance. Hence, low correlations among dissimilar indicators do not provide strong evidence for the existence, or absence, of a construct common to both indicators. One way to arrive at such evidence is to fit hierarchical latent factors that model task-specific, paradigm-specific, and construct variance. We report analyses for 101 younger and 103 older adults who worked on 9 different working memory tasks. The data are consistent with a hierarchical model of working memory, according to which both complex span and n-back tasks are valid indicators of working memory. The working memory factor predicts 71% of the variance in a factor of reasoning among younger adults (83% for among older adults). When the working memory factor was restricted to any possible triplet of working memory tasks, the correlation between working memory and reasoning was inversely related to the average magnitude of the correlations among the indicators, indicating that more highly intercorrelated indicators may provide poorer coverage of the construct space. We stress the need to go beyond specific tasks and paradigms when studying higher-order cognitive constructs, such as working memory.

This paper formulates a generalized heterogeneous data model (GHDM) that jointly handles mixed types of dependent variables—including multiple nominal outcomes, multiple ordinal variables, and multiple count variables, as well as multiple continuous variables—by representing the covariance relationships among them through a reduced number of latent factors. Sufficiency conditions for identification of the GHDM parameters are presented. The maximum approximate composite marginal likelihood (MACML) method is proposed to estimate this jointly mixed model system. This estimation method provides computational time advantages since the dimensionality of integration in the likelihood function is independent of the number of latent factors. The study undertakes a simulation experiment within the virtual context of integrating residential location choice and travel behavior to evaluate the ability of the MACML approach to recover parameters. The simulation results show that the MACML approach effectively recovers underlying parameters, and also that ignoring the multi-dimensional nature of the relationship among mixed types of dependent variables can lead not only to inconsistent parameter estimation, but also have important implications for policy analysis. [ABSTRACT FROM AUTHOR]

Hospital readmissions of patients put a high burden not only on the health care system, but also on the patients since complications after discharge generally lead to additional burdens. Estimating the risk of readmission after discharge from inpatient care has been the subject of several publications in recent years. In those publications the authors mostly tried to directly infer the readmission risk (within a certain time frame) from the clinical data recorded in the medical routine such as primary diagnosis, co-morbidities, length of stay, or questionnaires. Instead of using these data directly as inputs for a prediction model, we are exploiting latent embeddings for the nominal parts of the data (e.g., diagnosis and procedure codes). These latent embeddings have been used with great success in the natural language processing domain and can be constructed in a preprocessing step. We show in our experiments, that a prediction model that exploits these latent embeddings can lead to improved readmission predictive models. [ABSTRACT FROM AUTHOR]

We present new results for the likelihood-based analysis of the dynamic factor model. The latent factors are modelled by linear dynamic stochastic processes. The idiosyncratic disturbance series are specified as autoregressive processes with mutually correlated innovations. The new results lead to computationally efficient procedures for the estimation of the factors and for the parameter estimation by maximum likelihood methods. We also present the implications of our results for models with regression effects, for Bayesian analysis, for signal extraction, and for forecasting. An empirical illustration is provided for the analysis of a large panel of macroeconomic time series. [ABSTRACT FROM AUTHOR]

Price formation in the domestic market has not been widely studied in spite of the importance of the Italian wine market in terms of sales. We use a unique dataset to estimate the hedonic price function for Italian wine sold on the Italian market in the period 2005-2011. For each bottle considered, the dataset records several characteristics such as the price by retail channel (on the mass market and in wine shops), label characteristics, chemical analysis, sensory evaluations and experts' opinions. The objective of the analysis is to examine price setting on the mass market and in wine shops and to explore the differences in price formation for red and white wines. Our results have been obtained using an innovative technique that consists of combining hedonic price techniques with dimensionality reduction tools. [ABSTRACT FROM AUTHOR]

Tools for assessing decoding skill in students attending elementary grades are of fundamental importance for guaranteeing an early identification of reading disabled students and reducing both the primary negative effects (on learning) and the secondary negative effects (on the development of the personality) of this disability. This article presents results obtained by administering existing standardized tests of reading and a new screening procedure to about 1,500 students in the elementary grades in Italy. It is found that variables measuring speed and accuracy in all administered reading tests are not Gaussian, and therefore the threshold values used for classifying a student as a normal decoder or as an impaired decoder must be estimated on the basis of the empirical distribution of these variables rather than by using the percentiles of the normal distribution. It is also found that the decoding speed and the decoding accuracy can be measured in either a 1-minute procedure or in much longer standardized tests. The screening procedure and the tests administered are found to be equivalent insofar as they carry the same information. Finally, it is found that speed and accuracy act as complementary effects in the measurement of decoding ability. On the basis of this last finding, the study introduces a new composite indicator aimed at determining the student’s performance, which combines speed and accuracy in the measurement of decoding ability. [ABSTRACT FROM PUBLISHER]

A Multivariate Regression Model Based on the Optimal Partition of Predictors (MRBOP) useful in applications in the presence of strongly correlated predictors is presented. Such classes of predictors are synthesized by latent factors, which are obtained through an appropriate linear combination of the original variables and are forced to be weakly correlated. Specifically, the proposed model assumes that the latent factors are determined by subsets of predictors characterizing only one latent factor. MRBOP is formalized in a least squares framework optimizing a penalized quadratic objective function through an alternating least-squares (ALS) algorithm. The performance of the methodology is evaluated on simulated and real data sets. [ABSTRACT FROM AUTHOR]

This paper attempts to review the main factors of the yield curve in Chilean market during the period 2005-2013. Two different approaches are used to compute the main three factors denoted as the level, slope and curvature of the yield curve. Then, the impact of economic surprises and announcements are analyzed. Our results indicate that local surprises and announcements (both local and external) have similar effects on the estimated factors under both approaches, whereas is evidenced an asymmetric impact in the case of external surprises. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Sexually transmitted diseases (STDs) are a significant public health concern. Numerous internalizing and externalizing psychiatric disorders have been found to be related to STD risk. However, to date, no studies have examined several psychiatric disorders simultaneously to account for STD risk. Given that psychiatric disorders often co-occur and can be explained by a limited number of latent dimensions of psychopathology, it is important to examine whether the relationship between STDs and psychiatric disorders is best explained by broad dimensions of psychopathology. Methods: The current study examined the associations between a range of Axis I and II psychiatric disorders at baseline and rates of STDs at a three-year follow-up in a large, nationally representative sample of adults in the United States (n =34,434). A confirmatory factor analysis (CFA) was conducted to fit three factors, two internalizing and one externalizing. Structural equation modeling (SEM) was used to assess the relationships between and among the factors and STD status and to test for mediation. Results: In bivariate analyses, most Axis I and Axis II disorders were associated with STD diagnosis at Wave 2, whereas the results of the structural model showed that only the externalizing factor was significantly associated with STD diagnosis at Wave 2. Further, the externalizing factor mediated the relationship between one of the internalizing factors and STD diagnosis. Conclusion: Findings suggest the unique contribution of externalizing psychopathology to STD risk and the importance of examining latent dimensions of disorders when understanding this relationship between psychiatric disorders and STDs. [Copyright &y& Elsevier]