Relevant bibliographies by topics / Binary dependent variable

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Binary dependent variable'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Binary dependent variable"

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Fahmy, Rifqi Nur. "Determinan Keputusan Melakukan Migrasi Ulang-Alik." Efficient: Indonesian Journal of Development Economics 1, no. 3 (2018): 242–51. http://dx.doi.org/10.15294/efficient.v1i3.27869.

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The aim of this research is to analyze the influence of dependent variable of family dependent, education level, age, marital status, and distance partially to workforce’s decision to migrate from Surakarta to Karanganyar Regency. This research used binary logistic regression analysis method. The sample in this research is 100 respondents. The result of binary logistic regression model analysis in this research shows that from five independent variables, there are two variables that have significant effect on workforce’s decision to do the commuter migration that is dependent variable of famil
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Kao, Chihwa, and John F. Schnell. "Errors in variables in panel data with a binary dependent variable." Economics Letters 24, no. 1 (1987): 45–49. http://dx.doi.org/10.1016/0165-1765(87)90179-0.

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Chesher, Andrew, and Adam M. Rosen. "What Do Instrumental Variable Models Deliver with Discrete Dependent Variables?" American Economic Review 103, no. 3 (2013): 557–62. http://dx.doi.org/10.1257/aer.103.3.557.

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We compare nonparametric instrumental variables (IV) models with linear models and 2SLS methods when dependent variables are discrete. A 2SLS method can deliver a consistent estimator of a Local Average Treatment Effect but is not informative about other treatment effect parameters. The IV models set identify a range of interesting structural and treatment effect parameters. We give set identification results for a counterfactual probability and an Average Treatment Effect in a IV binary threshold crossing model. We illustrate using data on female employment and family size (employed by Joshua
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Dukalang, Hendra H. "PERBANDINGAN REGRESI LOGISTIK BINER DAN PROBIT BINER DALAM PEMODELAN TINGKAT PARTISIPASI ANGKATAN KERJA." Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi 7, no. 2 (2019): 62–70. http://dx.doi.org/10.34312/euler.v7i2.10355.

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Regression is a data analysis method used to model the relationship between one response variable and one or more predictor variables. In regression modelling, data is often used. In general, the regression model that is often used is simple or multiple regression in modelling where the response variable is quantitative data. The fundamental difference from regression models using quantitative data is the main objective is to estimate the average value of the dependent variable using certain values of the independent variable. Whereas in a regression model with a qualitative dependent variable
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Horowitz, Joel L., and N. E. Savin. "Binary Response Models: Logits, Probits and Semiparametrics." Journal of Economic Perspectives 15, no. 4 (2001): 43–56. http://dx.doi.org/10.1257/jep.15.4.43.

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A binary-response model is a mean-regression model in which the dependent variable takes only the values zero and one. This paper describes and illustrates the estimation of logit and probit binary-response models. The linear probability model is also discussed. Reasons for not using this model in applied research are explained and illustrated with data. Semiparametric and nonparametric models are also described. In contrast to logit and probit models, semi- and nonparametric models avoid the restrictive and unrealistic assumption that the analyst knows the functional form of the relation betw
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Araveeporn, Autcha. "The Higher-Order of Adaptive Lasso and Elastic Net Methods for Classification on High Dimensional Data." Mathematics 9, no. 10 (2021): 1091. http://dx.doi.org/10.3390/math9101091.

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The lasso and elastic net methods are the popular technique for parameter estimation and variable selection. Moreover, the adaptive lasso and elastic net methods use the adaptive weights on the penalty function based on the lasso and elastic net estimates. The adaptive weight is related to the power order of the estimator. Normally, these methods focus to estimate parameters in terms of linear regression models that are based on the dependent variable and independent variable as a continuous scale. In this paper, we compare the lasso and elastic net methods and the higher-order of the adaptive
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Kling, Gerhard, Charles Harvey, and Mairi Maclean. "Establishing Causal Order in Longitudinal Studies Combining Binary and Continuous Dependent Variables." Organizational Research Methods 20, no. 4 (2015): 770–99. http://dx.doi.org/10.1177/1094428115618760.

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Longitudinal studies with a mix of binary outcomes and continuous variables are common in organizational research. Selecting the dependent variable is often difficult due to conflicting theories and contradictory empirical studies. In addition, organizational researchers are confronted with methodological challenges posed by latent variables relating to observed binary outcomes and within-subject correlation. We draw on Dueker’s qualitative vector autoregression (QVAR) and Lunn, Osorio, and Whittaker’s multivariate probit model to develop a solution to these problems in the form of a qualitati
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Araveeporn, Autcha. "Comparison of Logistic Regression and Discriminant Analysis for Classification of Multicollinearity Data." WSEAS TRANSACTIONS ON MATHEMATICS 22 (February 16, 2023): 120–31. http://dx.doi.org/10.37394/23206.2023.22.15.

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The objective of this study is to concentrate on the classification method of the logistic regression and the discriminant analysis by using the simulation dataset and the liver patients as the actual data. These datasets are used the binary dependent variable depending on the correlated independent variables or called multicollinearity data. The standard classification method is logistic regression, which uses the logit function’s probability to conduct the dichotomous dependent variable. The iteration process can be solved to estimate logit function parameters and explain the relationship be
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Compton, Ryan. "A Data-Driven Approach to the Fragile Families Challenge: Prediction through Principal-Components Analysis and Random Forests." Socius: Sociological Research for a Dynamic World 5 (January 2019): 237802311881872. http://dx.doi.org/10.1177/2378023118818720.

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Sociological research typically involves exploring theoretical relationships, but the emergence of “big data” enables alternative approaches. This work shows the promise of data-driven machine-learning techniques involving feature engineering and predictive model optimization to address a sociological data challenge. The author’s group develops improved generalizable models to identify at-risk families. Principal-components analysis and decision tree modeling are used to predict six main dependent variables in the Fragile Families Challenge, successfully modeling one binary variable but no con
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Thomas, Jonathan M. "On testing the logistic assumption in binary dependent variable models." Empirical Economics 18, no. 2 (1993): 381–92. http://dx.doi.org/10.1007/bf01205409.

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Dissertations / Theses on the topic "Binary dependent variable"

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Gu, Yuanyuan Economics Australian School of Business UNSW. "Misclassification of the dependent variable in binary choice models." Awarded by:University of New South Wales. Economics, 2006. http://handle.unsw.edu.au/1959.4/26218.

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Survey data are often subject to a number of measurement errors. The measurement error associated with a multinomial variable is called a misclassification error. In this dissertation we study such errors when the outcome is binary. It is known that ignoring such misclassification errors may affect the parameter estimates, see for example Hausman, Abrevaya and Scott-Morton (1998). However, previous studies showed that robust estimation of the parameters is achievable if we take misclassification into account. There are many attempts to do so in the literature and the major problem in implement
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Berrett, Candace. "Bayesian Probit Regression Models for Spatially-Dependent Categorical Data." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1285076512.

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CENTORRINO, SAMUELE. "Essays in Nonparamentric Estimation with Instrumental Variables." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/109031.

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This thesis deals with the broad problem of causality and endogeneity in econometrics when the function of interest is estimated nonparametrically. It explores this problem in two separate frameworks. In the cross sectional, iid setting, it considers the estimation of a nonlinear additively separable model, in which the regression function depends on an endogenous explanatory variable. Endogeneity is, in this case, broadly defined. It can relate to reverse causality (the dependent variable can also affects the independent regressor) or to simultaneity (the error term contains information that
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Ayis, Salma Ahmed. "Modelling unobserved heterogeneity : theoretical and practical aspects." Thesis, University of Southampton, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261592.

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Cortés, Tejada Fernando Javier. "Jointly modelling of cluster dependent pro les of fractional and binary variables from a Bayesian point of view." Master's thesis, Pontificia Universidad Católica del Perú, 2020. http://hdl.handle.net/20.500.12404/17386.

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En la presente tesis se proponen modelos de clasificación basados en regresiones beta inflacionadas cero-uno con efectos mixtos para modelar perfiles longitudinales de variables fraccionarias mixtas y variables binarias de forma conjunta con formación de clústeres. Las distintas parametrizaciones de los modelos propuestos permiten modelar distintos efectos, como modelar directamente la media marginal a través de covariables e interpretar fácilmente su efecto sobre ella o modelar la media condicional y las probabilidades de inflación de forma separada. Además, se forman clústeres de grupos de i
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Alves, Thaís Guimarães. "Ensaios sobre as crises financeiras internacionais: economias avançadas, emergentes e em desenvolvimento." Universidade Federal de Uberlândia, 2012. https://repositorio.ufu.br/handle/123456789/13446.

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The general goal of the three essays is to analyze on theoretical and empirical grounds the international financial crises for advanced, emerging and developing countries. One can say that each Essay has its own specificities. The First Essay develops an analysis of the impacts of the 2008 financial crisis on economic growth for a number of advanced, emerging and developing countries using OLS cross-section models. The second Essay concerns in estimating the probability of occurrence of different types of international financial crises in the period 1970-2009 for selected Latin America (Argent
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Liang, Zhongwen. "Limited Dependent Variable Correlated Random Coefficient Panel Data Models." Thesis, 2012. http://hdl.handle.net/1969.1/ETD-TAMU-2012-08-11682.

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In this dissertation, I consider linear, binary response correlated random coefficient (CRC) panel data models and a truncated CRC panel data model which are frequently used in economic analysis. I focus on the nonparametric identification and estimation of panel data models under unobserved heterogeneity which is captured by random coefficients and when these random coefficients are correlated with regressors. For the analysis of linear CRC models, I give the identification conditions for the average slopes of a linear CRC model with a general nonparametric correlation between regressors and
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LeMire, Steven D. "An investigation of type I error rate control for independant variable subset tests with a binary dependent variable using ordinary least squares, logistic regression analysis, and nonparametric regression." 2005. http://catalog.hathitrust.org/api/volumes/oclc/69659854.html.

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Books on the topic "Binary dependent variable"

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Thomas, Jonathan. On testing the logistic assumption in Binary Dependent Variable Models. University of Cambridge, Department of Applied Economics, 1991.

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Franzese, Robert J., and Jude C. Hays. Empirical Models of Spatial Inter‐Dependence. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0025.

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This article discusses the role of ‘spatial interdependence’ between units of analysis by using a symmetric weighting matrix for the units of observation whose elements reflect the relative connectivity between unit i and unit j. It starts by addressing spatial interdependence in political science. There are two workhorse regression models in empirical spatial analysis: spatial lag and spatial error models. The article then addresses OLS estimation and specification testing under the null hypothesis of no spatial dependence. It turns to the topic of assessing spatial lag models, and a discussi
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Berkel, Hanna, and Finn Tarp. Informality and firm performance in Myanmar. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/930-3.

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Using a novel panel survey of enterprises in Myanmar, we compare the performance of manufacturing firms by three different informality definitions. The first is binary, based on whether firms pay taxes. The second captures five categories of registration with the authorities, and the third definition relates to three groupings of the informality status of a firm’s workers. Depending on the informality concept used, formalization has positive, insignificant, and negative performance outcomes. However, our analysis shows that independent of the informality definition, differences between formali
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Book chapters on the topic "Binary dependent variable"

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Giordano, Francesco, Marcella Niglio, and Marialuisa Restaino. "Screening Covariates in Presence of Unbalanced Binary Dependent Variable." In Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78965-7_38.

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Ives, Anthony R., and Theodore Garland. "Phylogenetic Regression for Binary Dependent Variables." In Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43550-2_9.

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Dunn, John C., and Michael L. Kalish. "Independent Observations with Binary Dependent Variables." In State-Trace Analysis. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73129-2_6.

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Honoré, Bo E., Luojia Hu, Ekaterini Kyriazidou, and Martin Weidner. "Simultaneity in binary outcome models with an application to employment for couples." In Advanced Studies in Theoretical and Applied Econometrics. Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-48385-1_26.

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AbstractTwo of Peter Schmidt’s many contributions to econometrics have been to introduce a simultaneous logit model for bivariate binary outcomes and to study estimation of dynamic linear fixed effects panel data models using short panels. In this paper, we study a dynamic panel data version of the bivariate model introduced in Schmidt and Strauss (Econometrica 43:745–755, 1975) that allows for lagged dependent variables and fixed effects as in Ahn and Schmidt (J Econom 68:5–27, 1995). We combine a conditional likelihood approach with a method of moments approach to obtain an estimation strate
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Abdullateef, Aliyu Olayemi. "Qualitative Response Regression Modeling." In Advances in Marketing, Customer Relationship Management, and E-Services. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-6371-8.ch011.

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In most regression models, readers have implicitly assumed that the dependent variable (regressand) Y is quantitative. On the contrary, explanatory variables could take the form of qualitative (or dummy), quantitative, or a triangulation thereof. This chapter discusses the observed fundamental differences between quantitative and qualitative models through a clear definition of their individual objectives. This chapter also considers many models in which the regressand is a qualitative variable, popularly called categorical variables, indicator variables, dummy variables, or qualitative variab
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Anderson, Raymond A. "Stats & Maths & Unicorns." In Credit Intelligence & Modelling. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0011.

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This chapter covers basic statistical concepts. Most statistics relate to hypothesis testing, and others to variable selection and model fitting. The name is because an exact match between a theoretical and empirical distribution is as rare as a unicorn. (1) Dispersion—measures of random variations—variance and its inflation factor, covariance and correlations {Pearson’s product-moment, Spearman’s rank order}, and the Mahalanobis distance. (2) Goodness-of-fit—do observations match expectations? This applies to both continuous dependent variables {R-squared and adjusted R2} and categorical {Pea
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Hawkins, Dawn. "More on the generalized linear model: logistic and loglinear models." In Biomeasurement. Oxford University Press, 2019. http://dx.doi.org/10.1093/hesc/9780198807483.003.0014.

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This chapter discusses sub-frameworks of the generalized linear model (GLZM) which can be used for nominal-level data with two categories (binary logistic model) and scale data in the form of counts (loglinear model). It examines how they can be used to assess the relative effects of multiple explanatory (independent) variables on a single response (dependent) variable. The binary logistic model uses the binomial distribution while the loglinear model uses the Poisson distribution and is useful if the response variable is count data. For explanatory variables, it is the same for the binary log
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Tran, Thanh V., and Keith T. Chan. "Comparing a Binary Dependent Variable Across Cultural Groups Using Applied Logistic Regression." In Applied Cross-Cultural Data Analysis for Social Work. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190888510.003.0005.

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This chapter reviews the basic ideas of logistic regression involving a binary dependent regressed on independent variables, along with assumptions for analysis and interpretations of results. It provides strategies and practical guides for data analysis using Stata and explains the basic assumptions of logistic regression and its applications for cross-cultural data analysis. The chapter also provides examples of logistic regression models for cross-cultural comparison, and outlines the techniques for testing the equivalence of effects across groups. The text includes examples of charts and g
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Nagel, Stefan. "Supervised Learning." In Machine Learning in Asset Pricing. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691218700.003.0002.

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This chapter provides a brief overview of supervised learning methods as the literature on this topic is vast and rapidly evolving. It focuses on the basic elements of these techniques that seem particularly useful for asset pricing and looks at the material on some of those methods in more detail by reviewing asset pricing applications. Supervised learning methods can be grouped into two categories: classification and regression methods. The chapter explains that classification methods are used in settings where the dependent variable y is categorial, while regression methods deal with contin
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Szekér Szabolcs and Vathy-Fogarassy Ágnes. "The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching." In Studies in Health Technology and Informatics. IOS Press, 2018. https://doi.org/10.3233/978-1-61499-858-7-1.

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Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent va
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Conference papers on the topic "Binary dependent variable"

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Arici, Mehmet, Wachira Daosud, Jozef Vargan, and Miroslav Fikar. "Multi-Model Predictive Control of a Distillation Column." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.180258.

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Successful implementation of optimization-driven control techniques, such as model predictive control (MPC), is highly dependent on an accurate and detailed model of the process. As complexity in the system increases, linear approximation used in MPC may result in poor performance since a critical operating point is valid in only a small neighborhood of operation. To address this problem, this paper proposes a collaborative approach that combines linear and data-based models to predict state variables individually. The outputs of these models, along with constraints, are then incorporated into
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Carvalho, Marta de, Maria Cecília Trindade, Wladimir Freitas, and Andrei Sposito. "CAN EPWORTH SLEEPINESS SCALE BE A PREDICTOR OF COGNITIVE DEFICT IN A COGNITIVE SCREENING TEST IN A COHORT OF ELDERLY FREE OF CLINICALLY MANIFEST VASCULAR BRAIN DISEASE." In XIII Meeting of Researchers on Alzheimer's Disease and Related Disorders. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1980-5764.rpda069.

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Background: The Obstructive Sleep Apnea Syndrome (OSAS) is highly prevalent among the elderly and relevant due to its cognitive impact. Objective: To evaluate an association between cognitive impairment (CI) and the presence of OSAS as assessed by the Mini Mental (MM) scale and the Ephorth Sleepiness Scale (ESS) in a population of octogens free from overt cerebral vascular disease (CVD). Methods: 137 individuals were selected. The study was approved by the ethics committee. Categorical variables were evaluated as percentages, continuous variables with normal distribution as mean ± SD and non-p
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Johnson, Peter E., Kenneth M. Bryden, and Daniel A. Ashlock. "Inverse Solution of a Heat Conduction Problem Using Evolutionary Data Segregation Techniques." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41283.

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Engineering problems are typically solved by direct solution. For the direct solution of engineering problems the boundary conditions and physical properties of the domain are given, and the dependent variable is calculated throughout the domain. In contrast to this, for inverse engineering problems the dependent variable is known at select locations in the domain, and the material properties and/or the boundary conditions need to be determined. This paper will present a novel technique for the inverse solution of a heat transfer engineering design problem in which the temperature profile and
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Tjostheim, Ingvar, Chris Wales, and John A Waterworth. "Sexting, age and digital vulnerabilities." In 12th International Conference on Human Interaction and Emerging Technologies (IHIET 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005513.

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Despite popular misconception it is not only young people that are sharing nude pictures and videos. There are a number of studies targeted towards the younger generation about their sexting, but few on older age-groups. In general, younger people take more risk than older people, and some seem to care less about possible negative consequences. For this study we commissioned a market research company to collect data from a national population, with a representative sample from 16 to 69 years old, in total 1071 citizens. We used binary logistic regression for the analysis of responses, a method
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Chernenko, Ilia. "Regional Determinants of Lifelong Learning in Russia: the Impact of Infrastructure Quality, Crime Level and Cultural Development on the Human Capital Strategies of the Employed." In Human Capital, Institutions, Economic Growth. Kutaisi University, 2023. http://dx.doi.org/10.52244/c.2023.11.6.

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Lifelong learning (LLL) is a crucial strategy for the development of human capital, particularly in the face of challenges such as an aging population, a significant decrease in birth rates, and the outflow of qualified personnel, which pose a threat to the national labor market. The objective of this article is to examine the factors that influence LLL and assess the specific impact of regional variables that reflect infrastructure quality, access to social services, cultural development, and crime rates. This study utilizes data from a Rosstat survey on the well-being of the population, whic
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Zhang, Duan Z., and Rick M. Rauenzahn. "Effects of Long and Short Relaxation Times of Particle Interactions in Dense and Slow Granular Flows." In ASME/JSME 2003 4th Joint Fluids Summer Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/fedsm2003-45748.

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The rheological properties and the duration of particle interactions in a dense granular media are closely related to the formation of particle interaction networks. The behavior of particle interaction networks depends not only on the particle volume fractions but also on friction between particles. For examples, for frictionless particles, a particle interaction network may not form at particle volume fraction greater than 0.62, the random dense packing volume fraction for monodisperse spheres. Without network formation, particle interactions are short in time and mostly binary. Under this c
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Xu, Minghan, Saad Akhtar, and Agus P. Sasmito. "A Heterogenous Nucleation Model for Supercooled Water and Sucrose Solution Droplets Under Ultra-Cold Environments." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-68974.

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Abstract With growing food scarcity and high demands for vaccine storage, advancing spray freeze-drying technology has never been more important for prolonging shelf life of biological and pharmaceutical materials. Particularly, the estimation of nucleation behaviour for both pure substances and binary mixtures has become vital to the optimal thermal design and implementation of spay freeze-drying technology. Notwithstanding that past nucleation frameworks could predict nucleation rate and temperature of droplet solidification, few of them considered extreme surrounding conditions, such as ver
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Cao, Tianxiang, Xin Song, and Jun Wang. "A Comparison of the Effectiveness of Techniques for Predicting Binary Dependent Variables." In 2022 21st International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2022. http://dx.doi.org/10.1109/iscit55906.2022.9931323.

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Bofill, Miquel, Jordi Coll, Josep Suy, and Mateu Villaret. "Compact MDDs for Pseudo-Boolean Constraints with At-Most-One Relations in Resource-Constrained Scheduling Problems." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/78.

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Pseudo-Boolean (PB) constraints are usually encoded into Boolean clauses using compact Binary Decision Diagram (BDD) representations. Although these constraints appear in many problems, they are particularly useful for representing resource constraints in scheduling problems. Sometimes, the Boolean variables in the PB constraints have implicit at-most-one relations. In this work we introduce a way to take advantage of these implicit relations to obtain a compact Multi-Decision Diagram (MDD) representation for those PB constraints. We provide empirical evidence of the usefulness of this techniq
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Tavartkiladze, Marine. "Effective Employment Policy as a Component of Strengthening Economic Development (Case of Georgia)." In 8th FEB International Scientific Conference. University of Maribor Press, 2024. http://dx.doi.org/10.18690/um.epf.5.2024.43.

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Poverty is one of the greatest challenges for Georgia, which is directly related to the issues of effective management of active or passive employment policy. The purpose of the research is to identify the challenges of employment policy and future opportunities in Georgia. The paper presents the results of qualitative and quantitative research with target groups (socially vulnerable persons, unemployed, large families, pensioners, employees etc.) And then the economic and statistical analysis of the collected data is conducted. Logistic and multinomial models are used for analysis. For binary
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Reports on the topic "Binary dependent variable"

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Wang, Chih-Hao, and Na Chen. Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling? Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2011.

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The transportation studies literature recognizes the relationship between accessibility and active travel. However, there is limited research on the specific impact of walking and cycling accessibility to multi-use paths on active travel behavior. Combined with the culture of automobile dependency in the US, this knowledge gap has been making it difficult for policy-makers to encourage walking and cycling mode choices, highlighting the need to promote a walking and cycling culture in cities. In this case, a clustering effect (“you bike, I bike”) can be used as leverage to initiate such a trend
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