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Relevant bibliographies by topics / Binary dependent variables

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Academic literature on the topic 'Binary dependent variables'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Binary dependent variables"

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Norman, Geoffrey R., and David L. Streiner. "Binary dependent variables: logistic regression." Community Oncology 7, no. 8 (2010): 367–68. http://dx.doi.org/10.1016/s1548-5315(11)70579-4.

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Tennekoon, Vidhura, and Robert Rosenman. "Systematically misclassified binary dependent variables." Communications in Statistics - Theory and Methods 45, no. 9 (2014): 2538–55. http://dx.doi.org/10.1080/03610926.2014.887105.

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Ives, Anthony R., and Theodore Garland. "Phylogenetic Logistic Regression for Binary Dependent Variables." Systematic Biology 59, no. 1 (2009): 9–26. http://dx.doi.org/10.1093/sysbio/syp074.

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Frölich, Markus. "Non-parametric regression for binary dependent variables." Econometrics Journal 9, no. 3 (2006): 511–40. http://dx.doi.org/10.1111/j.1368-423x.2006.00196.x.

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Haynes, Mary E., Roy T. Sabo, and N. Rao Chaganty. "Simulating dependent binary variables through multinomial sampling." Journal of Statistical Computation and Simulation 86, no. 3 (2015): 510–23. http://dx.doi.org/10.1080/00949655.2015.1020313.

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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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Dow, Malcolm M. "Network Autocorrelation Regression With Binary and Ordinal Dependent Variables." Cross-Cultural Research 42, no. 4 (2008): 394–419. http://dx.doi.org/10.1177/1069397108320411.

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Gessner, Guy, Naresh K. Malhotra, Wagner A. Kamakura, and Mark E. Zmijewski. "Estimating models with binary dependent variables: Some theoretical and empirical observations." Journal of Business Research 16, no. 1 (1988): 49–65. http://dx.doi.org/10.1016/0148-2963(88)90080-x.

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Deng, Minfeng. "Binary Pattern Recognition in the Presence of Correlated Multiple Dependent Variables." Natural Resources Research 19, no. 4 (2010): 269–78. http://dx.doi.org/10.1007/s11053-010-9128-7.

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Dissertations / Theses on the topic "Binary dependent variables"

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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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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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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 variables"

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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 variables"

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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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"Binary dependent variables." In Applied Econometrics for Health Economists. CRC Press, 2007. http://dx.doi.org/10.1201/9781785230141-9.

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"Binary Dependent Variables." In Longitudinal and Panel Data. Cambridge University Press, 2004. http://dx.doi.org/10.1017/cbo9780511790928.010.

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"Binary Variables." In Generalized Linear Models for Categorical and Continuous Limited Dependent Variables. Chapman and Hall/CRC, 2013. http://dx.doi.org/10.1201/b15694-10.

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Calabrese, Raffaella, and Johan A. Elkink. "Estimating Binary Spatial Autoregressive Models for Rare Events." In Spatial Econometrics: Qualitative and Limited Dependent Variables. Emerald Group Publishing Limited, 2016. http://dx.doi.org/10.1108/s0731-905320160000037012.

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Karakara, Alhassan Abdul-Wakeel, and Evans S. C. Osabuohien. "Categorical Dependent Variables Estimations With Some Empirical Applications." In Applied Econometric Analysis. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1093-3.ch008.

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Microeconomic datasets are usually large, mainly survey data. These data are samples of hundreds of respondents or group of respondents (e.g., households). The distributions of such data are mostly not normal because some responses/variables are discrete. Handling such datasets poses some problems of summarizing/reporting the important features of the data in estimations. This study concentrates on how to handle categorical variables in estimation/reporting based on theoretical and empirical knacks. This study used Ghana Demographic and Health Survey data for 2014 for illustration and elaborates on how to interpret results of binary and multinomial outcome regressions. Comparison is made on the different binary models, and binary logit is found to be weighted over the other binary models. Multinomial logistic model is best handled when the odds of one outcome versus the other outcome are independent of other outcome alternatives as verified by the Independent of Irrelevant Alternatives (IIA). Conclusions and suggestions for handling categorical models are discussed in the study.

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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 variables. This chapter shows why it is not compulsory to restrict our dependent variable to dichotomous (yes/no) categories by establishing inherent benefits in estimating and interpreting trichotomous or polychotomous multiple category response variable. Relevant examples for developing, analyzing, and interpreting a probability model for a binary response variable using three known approaches (i.e. linear probability model, logit, and probit models) is also discussed.

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"Simple Linear Models With Categorical Dependent Variables: Binary Logistic Regression." In Regression & Linear Modeling: Best Practices and Modern Methods. SAGE Publications, Inc, 2017. http://dx.doi.org/10.4135/9781071802724.n5.

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Rahman, Mohammad Arshad, and Angela Vossmeyer. "Estimation and Applications of Quantile Regression for Binary Longitudinal Data." In Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B. Emerald Publishing Limited, 2019. http://dx.doi.org/10.1108/s0731-90532019000040b009.

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Conference papers on the topic "Binary dependent variables"

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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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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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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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Li, Hailin, Ghazi A. Karim, and A. Sohrabi. "The Lean Mixture Operational Limits of a S.I. Engine When Operated on Fuel Mixtures." In ASME 2005 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/icef2005-1109.

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The operation of S.I. engines on lean mixtures is attractive in principle since it can provide improved fuel economy, reduced tendency to knock and extremely low NOx emissions. However, the associated flame propagation rates become degraded significantly and drop sharply as the operating mixture is made increasingly lean. Consequently, there exist distinct operational lean mixture limits beyond which satisfactory engine performance cannot be maintained due to the resulting prolonged and unstable combustion processes. The paper presents experimental data obtained in a single cylinder, variable

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McClure, Emma R., and Van P. Carey. "Use of Genetic Algorithms and Machine Learning to Explore Parametric Trends in Nucleate Boiling Heat Transfer Data." In ASME 2020 Heat Transfer Summer Conference collocated with the ASME 2020 Fluids Engineering Division Summer Meeting and the ASME 2020 18th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/ht2020-9077.

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Abstract Exploring parametric effects in pool boiling is particularly challenging because the dependence of the resulting surface heat flux on many parameters is non-linear, and the mechanisms can interact in complex ways. Historically, parametric effects in nucleate boiling processes have most often been deduced by fitting relations obtained from physical models to experimental data, or looking for correlated trends in non-dimensionalized data. Using such approaches, observed trends are often influenced by the framing of the analysis that results from the modeling or the collection of dimensi

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Reports on the topic "Binary dependent variables"

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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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