Academic literature on the topic 'Binary dependent variable'
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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 family and marital status. While the variable level of education, age, and distance have no effect on workforce’s decisions to do the commuter migration.
 Tujuan dari penelitian ini adalah untuk menganalisis pengaruh variabel dependen dependen keluarga, tingkat pendidikan, usia, status perkawinan, dan jarak secara parsial terhadap keputusan tenaga kerja untuk bermigrasi dari Surakarta ke Kabupaten Karanganyar. Penelitian ini menggunakan metode analisis regresi logistik biner. Sampel dalam penelitian ini adalah 100 responden. Hasil analisis model regresi logistik biner dalam penelitian ini menunjukkan bahwa dari lima variabel independen, ada dua variabel yang berpengaruh signifikan terhadap keputusan angkatan kerja untuk melakukan migrasi komuter yang merupakan variabel dependen keluarga dan status perkawinan. Sedangkan tingkat variabel pendidikan, usia, dan jarak tidak berpengaruh pada keputusan tenaga kerja untuk melakukan migrasi komuter.
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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 Angrist and William Evans (1998)) and compare with their LATE estimates.
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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 the main objective is to find the probability of something happening (probability model). One of the development methods of the regression model for data with qualitative response variables is Logistic and Probit regression. The purpose of this study was to compare the best model using binary logistic regression with binary probit regression in the case of Labor Force Participation Rate (TPAK) in Gorontalo City. The research method used is quantitative research methods, with binary logistic regression modelling and binary probit regression. The results showed that the variable that has a significant effect on TPAK Gorontalo City is the open unemployment rate, and the best model between the binary logistic regression model with an AIC value of 1.289 is smaller than the AIC value of the binary Probit regression 1.318, likewise from the R2 value the R2 value for regression is obtained. binary logistic of 12.74%, greater than the R2 value of binary probit regression of 10.70%.
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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 between the dependent variable and the explanatory variables.
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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 lasso and adaptive elastic net methods for classification on high dimensional data. The classification is used to classify the categorical data for dependent variable dependent on the independent variables, which is called the logistic regression model. The categorical data are considered a binary variable, and the independent variables are used as the continuous variable. The high dimensional data are represented when the number of independent variables is higher than the sample sizes. For this research, the simulation of the logistic regression is considered as the binary dependent variable and 20, 30, 40, and 50 as the independent variables when the sample sizes are less than the number of the independent variables. The independent variables are generated from normal distribution on several variances, and the dependent variables are obtained from the probability of logit function and transforming it to predict the binary data. For application in real data, we express the classification of the type of leukemia as the dependent variables and the subset of gene expression as the independent variables. The criterion of these methods is to compare by the average percentage of predicted accuracy value. The results are found that the higher-order of adaptive lasso method is satisfied with large dispersion, but the higher-order of adaptive elastic net method outperforms on small dispersion.
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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 qualitative short panel vector autoregression (QSP-VAR). The QSP-VAR combines binary and continuous variables into a single vector of dependent variables, making every variable endogenous a priori. The QSP-VAR identifies causal order, reveals within-subject correlation, and accounts for latent variables. Using a Bayesian approach, the QSP-VAR provides reliable inference for short time dimension longitudinal research. This is demonstrated through analysis of the durability of elite corporate agents, social networks, and firm performance in France. We provide our OpenBUGS code to enable implementation of the QSP-VAR by other researchers.
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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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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 between a dependent binary variable and independent variables. Discriminant analysis is a powerful classification based on linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and regularized discriminant analysis (RDA). These methods consider the decision boundaries by building a classifier model on the multivariate normal distribution. LDA defines the standard covariance matrix, but QDA has an individual covariance matrix. RDA extends from QDA by setting the regularized parameter to estimate the covariance matrix. In the case of the simulation study, the independent variables are generated by defining the constant correlation on the multivariate normal distribution that made the multicollinearity problem. Then the binary response variable can be approximated from the logit function. For application to actual data, we expressed the classification of type liver and non-liver patients as the dependent variables and obtained patient personal information on the nine independent variables. The highest average percentage of accuracy determines the performance of these methods. The results have shown that the logistic regression was successful when using small independent variables, but the RDA performed when using large independent variables.
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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 continuous dependent variables in the diagnostic data set. This indicates that some binary dependent variables are more predictable using a reduced set of uncorrelated independent variables, and continuous dependent variables demand more complexity.
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 implementing them is to avoid poor or fragile identifiability of the misclassification probabilities. Generally we restrict these parameters by imposing prior information on them. Such prior constraints on the parameters are simple to impose within a Bayesian framework. Hence we consider a Bayesian logistic regression model that takes into account the misclassification of the dependent variable. A very convenient way to implement such a Bayesian analysis is to estimate the hierarchical model using the WinBUGS software package developed by the MRC biostatistics group, Institute of Public Health, at Cambridge University. WinGUGS allows us to estimate the posterior distributions of all the parameters using relatively little programming and once the program is written it is trivial to change the link function, for example from logit to probit. If we wish to have more control over the sampling scheme or to deal with more complex models, then we propose a data augmentation approach using the Metropolis-Hastings algorithm within a Gibbs sampling framework. The sampling scheme can be made more efficient by using a one-step Newton-Raphson algorithm to form the Metropolis-Hastings proposal. Results from empirically analyzing real data and from the simulation studies suggest that if suitable priors are specified for the misclassification parameters and the regression parameters, then logistic regression allowing for misclassification results in better estimators than the estimators that do not take misclassification into account.
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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 can be related to the explanatory variable). Identification and estimation of the regression function is performed using the method of instrumental variables. In the time series context, it studies the implications of the assumption of exogeneity in a regression type model in continuous time. In this model, the state variable depends on its past values, but also on some external covariates and the researcher is interested in the nonparametric estimation of both the conditional mean and the conditional variance functions.
This first chapter deals with the latter topic. In particular, we give sufficient conditions under which the researcher can make meaningful inference in such a model. It shows that noncausality is a sufficient condition for exogeneity if the researcher is not willing to make any assumption on the dynamics of the covariate process. However, if the researcher is willing to assume that the covariate process follows a simple stochastic differential equation, then the assumption of noncausality becomes irrelevant.
Chapters two to four are instead completely devoted to the simple iid model. The function of interest is known to be the solution of an inverse problem.
In the second chapter, this estimation problem is considered when the regularization is achieved using a penalization on the L2-norm of the function of interest (so-called Tikhonov regularization). We derive the properties of a leave-one-out cross validation criterion in order to choose the regularization parameter.
In the third chapter, coauthored with Jean-Pierre Florens, we extend this model to the case in which the dependent variable is not directly observed, but only a binary transformation of it. We show that identification can be obtained via the decomposition of the dependent variable on the space spanned by the instruments, when the residuals in this reduced form model are taken to have a known distribution. We finally show that, under these assumptions, the consistency properties of the estimator are preserved.
Finally, chapter four, coauthored with Frédérique Fève and Jean-Pierre Florens, performs a numerical study, in which the properties of several regularization techniques are investigated. In particular, we gather data-driven techniques for the sequential choice of the smoothing and the regularization parameters and we assess the validity of wild bootstrap in nonparametric instrumental regressions.
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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 individuos con perfiles longitudinales similares a través de una variable latente, asumiendo que las variables respuesta siguen un modelo de mixtura finita. Debido a la complejidad de los modelos, los parámetros se estiman desde un punto de vista bayesiano, a partir de simulaciones MCMC utilizando el software JAGS en R. Se prueban los modelos propuestos sobre diferentes bases de datos simulados
para medir el desempeño de los mismos y se comparan con otros modelos a fin de verificar cual ajusta mejor los perfiles longitudinales de variables fraccionarias mixtas y variables binarias. Por último, se aplican los modelos propuestos a datos reales de un banco peruano, con información del ratio de uso de tarjetas de crédito en el periodo de un año, estado de default del cliente y otras covariables correspondientes al cliente poseedor de la tarjeta, con el objetivo de obtener clústeres de individuos con similar ratio de uso de tarjeta de crédito y relacionarlos con la probabilidad de caer en default que presenta cada grupo.<br>The following thesis proposes classi cation models that consist of jointly tting longitudinal
pro les of mixed fractional and binary variables modelled by zero-one beta in
ated mixed
regressions with cluster formation. The distinct proposed parametrizations allow di erent effects
to be modelled, such as modelling the marginal mean directly through independent
variables and easily interpret its e ect on it or modelling the conditional mean and the in-
ation probabilities separately. In addition, individuals with similar fractional longitudinal
pro les are grouped into a cluster through a latent variable, assuming that the response variables
follow a nite mixture model. Due to the complexity of the models, the parameters are
estimated from a Bayesian point of view by simulating a MCMC using JAGS software in R.
The proposed models are tted in various simulated datasets and are compared against other
models to measure performance in tting fractional longitudinal pro les and binary variables.
Finally, an application on real data is conducted, consisting on longitudinal information of
credit card utilization ratio and default status as dependants variables and covariates corresponding
to client information, aiming to obtain clusters of clients with similar behaviour in
evolution of credit card utilization and relate them to their probability of default.<br>Tesis
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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
(Argentina, Brazil and Mexico) and Asia emerging countries (Philippines, Indonesia,
Malaysia and Thailand). The empirical investigation is based on probabilistic models
(MPL, PROBIT and LOGIT) where the dependent variable is associated with a different
concept of financial crises (external and internal default, banking crises, inflation and
currency crises and general international financial crises). Finally, the last essay
develops an empirical investigation using panel data from 1970 to 2009 and analyzes
the main determinants of the different types of international financial crises for a sample
of 118 advanced, emerging and developing countries using six concepts of international
financial crises.<br>Os três ensaios que compõem esse trabalho têm como objetivo geral analisar teórica e
empiricamente as crises financeiras internacionais para economias avançadas,
emergentes e em desenvolvimento. Fundamentalmente, cada ensaio tem a sua
particularidade. Nestes termos, o Ensaio 1 realiza uma análise dos impactos da crise
financeira de 2008 sobre o crescimento econômico para um conjunto de países
avançados, economias emergentes e em desenvolvimento a partir da estimação de
modelos do tipo cross section com o Método dos Mínimos Quadrados Ordinários
(MQO). No segundo ensaio, a preocupação está nos determinantes da probabilidade de
ocorrência dos tipos de crises financeiras internacionais no período 1970-2009 para
países emergentes selecionados da América Latina (Argentina, Brasil e México) e da
região asiática (Filipinas, Indonésia, Malásia e Tailândia) a partir da abordagem
metodológica dos modelos com variáveis dependentes binárias do tipo MPL, PROBIT e
LOGIT, onde a variável dependente está associada aos tipos de crises financeiras
(default externo, endividamento interno, crises bancárias, crises inflacionárias, crises
cambiais e crises financeiras internacionais gerais). Por fim, o último ensaio apresenta
uma investigação empírica com dados em painel de 1970 a 2009 com modelos do tipo
PROBIT e LOGIT no intuito de analisar os principais determinantes da probabilidade
de ocorrência das crises financeiras para uma amostra de 118 países avançados,
emergentes e em desenvolvimento, a partir das seis definições quanto aos tipos de crises
financeiras internacionais.<br>Doutor em Economia
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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 random coefficients. I construct a sqrt(n) consistent estimator for the average slopes via varying coefficient regression.
The identification of binary response panel data models with unobserved heterogeneity is difficult. I base identification conditions and estimation on the framework of the model with a special regressor, which is a major approach proposed by Lewbel (1998, 2000) to solve the heterogeneity and endogeneity problem in the binary response models. With the help of the additional information on the special regressor, I can transfer a binary response CRC model to a linear moment relation. I also construct a semiparametric estimator for the average slopes and derive the sqrt(n)-normality result.
For the truncated CRC panel data model, I obtain the identification and estimation results based on the special regressor method which is used in Khan and Lewbel (2007). I construct a sqrt(n) consistent estimator for the population mean of the random coefficient. I also derive the asymptotic distribution of my estimator.
Simulations are given to show the finite sample advantage of my estimators. Further, I use a linear CRC panel data model to reexamine the return from job training. The results show that my estimation method really makes a difference, and the estimated return of training by my method is 7 times as much as the one estimated without considering the correlation between the covariates and random coefficients. It shows that on average the rate of return of job training is 3.16% per 60 hours training.
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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.
Full textBooks 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 discussion of spatial error models. Moreover, it reports the calculation of spatial multipliers. Furthermore, it presents several newer applications of spatial techniques in empirical political science research: SAR models with multiple lags, SAR models for binary dependent variables, and spatio-temporal autoregressive (STAR) models for panel data.
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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 formalizers and non-formalizers are mostly because of disparities in the number of employees, capital, and use of power-driven machinery. Education, business practices, gender, location, and sector only play a role for some of the definitions and performance variables.
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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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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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 {Pearson’s chi-square, Hosmer–Lemeshow statistic}. (3) Likelihood—assesses estimates’ goodness-of-fit to binary dependent variables {log-likelihood, deviance}, plus the Akaike and Bayesian information criteria used to penalize complexity. (4) The Holy Trinity of Statistics—i) Neyman–Pearson’s ‘likelihood ratio’—the basis for model comparisons; ii) Wald’s chi-square—for potential variable removal; iii) Rao’s score chi-square—for potential variable inclusion. These are all used in Logistic Regression.
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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 graphs that can be used to explain differences in effects across cultural groups.
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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 continuous dependent variables. In asset pricing applications, regression problems are more common, although classification methods can also be useful, for example for prediction of binary events like a corporate default.
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Chazard, Emmanuel, Pierre Balaye, Thibaut Balcaen, et al. "“Book Music” Representation for Temporal Data, as a Part of the Feature Extraction Process: A Novel Approach to Improve the Handling of Time-Dependent Data in Secondary Use of Healthcare Structured Data." In MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. IOS Press, 2022. http://dx.doi.org/10.3233/shti220141.
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Book music is extensively used in street organs. It consists of thick cardboard, containing perforated holes specifying the musical notes. We propose to represent clinical time-dependent data in a tabular form inspired from this principle. The sheet represents a statistical individual, each row represents a binary time-dependent variable, and each hole denotes the “true” value. Data from electronic health records or nationwide medical-administrative databases can then be represented: demographics, patient flow, drugs, laboratory results, diagnoses, and procedures. This data representation is suitable for survival analysis (e.g., Cox model with repeated outcomes and changing covariates) and different types of temporal association rules. Quantitative continuous variables can be discretized, as in clinical studies. The “book music” approach could become an intermediary step in feature extraction from structured data. It would enable to better account for time in analyses, notably for historical cohort analyses based on healthcare data reuse.
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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.
Full textConference papers on the topic "Binary dependent variable"
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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-parametric variables as median. The subjects were not categorized into the presence or absence of CI according to the score on the MM scale according to education. In a multivariate binary logistic regression model with dependent variable CI, independent variables were incorporated according to the clinic and whether they were associated with CI in the bivariate models. All independent variables were defined in the model. Results: There was an association between high probability of OSAS by ESS and CI by MM. X2(1) = 5.34 p = 0.021. Conclusions: There was an association between high scores on the ESS and the presence of CI at the MM, even compatible for age, BMI, gender, coronary calcification, blood pressure.
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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 materials are known, but the placement of these materials and the heat flux on the boundaries are unknown. This technique uses evolutionary optimization in the form of the Adaptive Modeling by Evolving Blocks Algorithm (AMoEBA) to determine the material configurations. The material configurations, geometry, and properties are defined by evolving binary trees. The evolved domains are solved directly and then compared with the known temperature profile. Fitness of the new designs is determined by the least squared error between the proposed and the known profile. When this fitness reaches a defined level, the material placement scheme of the real system is found, and boundary conditions matching the problem definition are identified.
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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 condition, the granular medium can be modeled as a viscous fluid with variable viscosity as in kinetic theory. Formation of particle interaction networks dramatically increases particle interaction time and results in a phase transition in the constitutive relations of the granular medium. Then, the stress relaxation time is inversely proportional to the macroscopic shear rate in simple shear flows, and the granular medium can be modeled as a viscoelastic material with a stress relaxation time depending on the macroscopic shear rate. For small shear rates, the stresses in the granular medium are independent of macroscopic shear rates in simple shear flows. Thus, as the shear rate approaches zero, the relaxation time approaches infinity, and the shear stress approaches a finite value, the yield stress, instead of zero. We also studied the relaxation behavior of the stress tensor under time-dependent shear rates. The dynamics of the particle interaction network leads to a nonlinear behavior of stress relaxation not exhibited by ordinary viscoelastic materials, such as polymeric fluids.
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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 very low ambient temperature below −60 degree Celsius. These environments, however, play a significant role in ascertaining the preservation and storage of chemical and pharmaceutical products, e.g., vaccines and protein drugs. It is therefore of great interest to establish accurate and reliable mathematical framework on simulating nucleation during droplet solidification subjected to ultra-cold conditions. This paper develops a semi-analytical heterogeneous nucleation model and anticipates nucleation phenomena of a suspended droplet under ultra-cold environment. Nucleation temperatures calculated from the presented model are validated against a set of experiments on single suspended droplets for a wide array of ambient temperatures from −20 until −160 degree Celsius. Both pure water and 20% w/w sucrose aqueous solution are examined for these droplets. Cumulative probability distributions of nucleation for both types of droplets over nucleation temperatures are also presented and comparisons are made between the model results and recent experimental data from literatures. Our preliminary findings demonstrate that drastic changes in nucleation temperature for ultra-cold surroundings are the aftermath of alterations in interfacial surface tension. Conventionally, the inter-facial surface tension is defined as a function of supercooling degree only, which fails as surrounding temperature is prescribed below −40 degree Celsius. In this study, the interfacial surface tension is linearly optimized using error minimization with experimental data fit, such that it substantially relates to both the supercooling degree and surrounding temperature under a given environment for pure water. As for sucrose aqueous solution (i.e., an example of binary mixtures), their solute concentration is also a dependent variable of interfacial surface tension. The results indicate that our proposed framework is capable of predicting heterogeneous nucleation in a droplet filled with either pure material or binary mixture. Development of this nucleation model for spray freeze-drying can expedite manufacturing process and reduce expenses in handling, transportation and storage of biological products, thus improving the shelf life of pharmaceuticals and availability of foods at large. Our model can be extended on other pure materials and binary mixtures, which will further be used to facilitate the design and implementation of spray freeze-drying technology for preserving and storing more chemicals and pharmaceutical excipients.
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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 technique for some Resource-Constrained Project Scheduling Problem (RCPSP) variants, namely the Multi-Mode RCPSP (MRCPSP) and the RCPSP with Time-Dependent Resource Capacities and Requests (RCPSP/t). The size reduction of the representation of the PB constraints lets us decrease the number of Boolean variables in the encodings by one order of magnitude. We close/certify the optimum of many instances of these problems.
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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 compression ratio, S.I., CFR engine when operated in turn on CH4, H2, CO, gasoline, iso-octane and some of their binary mixtures. A quantitative approach for determining the operational limits of S.I. engines is suggested, compared and validated against corresponding experimental results of other traditional approaches. On this basis, the dependence of the values of the lean mixture operational limits on the composition of the fuel mixtures is investigated and discussed. The operational limit for throttled operation with methane as the fuel is also established.
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Trushkin, S., A. Shevchenko, N. Bursov, P. Tsybulev, and N. Nizhelsky. "Long-term multi-frequency studies of flaring activity from microquasars." In ASTRONOMY AT THE EPOCH OF MULTIMESSENGER STUDIES. Proceedings of the VAK-2021 conference, Aug 23–28, 2021. Crossref, 2022. http://dx.doi.org/10.51194/vak2021.2022.1.1.196.
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The long-term monitoring at RATAN-600 of studies of bright X-ray binary stars in various ranges of the electromagneticspectrum, a search and detailed study of correlations between variable X-ray, radio and gamma radiation was carried out.It is a key point for understanding the formation of jet emissions from accreting matter onto a black hole (or neutron star).From April 2019 For a year, we began to use the multi-azimuth measurement mode on the Southern Sector antenna systemwith a flat reflector, when 31 measurements of flux densities at frequencies 4.7, 8.6, 15 and 30 GHz of several giant CygnusX-3 flares, SS433 [1] and GRS1915+105 bright flashes were carried out for 5–6 hours around the culmination of the source.In January 2020, Cyg X-3 switched to a hyper-soft X-ray state, the exit from which in early February led to the brightest theradio flash for the all history of its observations. The Cygnus X-3 flow density increased from 5 mJy to 20 Jy at a frequencyof 4.7 GHz and up to 22 Jy at 2.3 GHz in 2–3 days. In multi-azimuthal observations in the beginning phase, we registered alinear law of increase in the flux at times from 1 to 5 hours. Comparing the data of the space Gamma-ray telescopes (Swift,AGILE and Fermi) and the MAXI and NICER X-ray monitors on board the ISS, we found that flaring events from the radioto the Gamma-rays are interrelated, which is a reflection of the causal relationship of physical processes in the accretiondisk and in jet emissions. The spectral and time dependence of the evolution of flares allow us to model the synchrotronradiation of microquasars based on changes in the volume of jet emissions, the strength of their magnetic field and the modeof generation and absorption of radio radiation from relativistic electrons. Radio flares of the microquasar GRS1915+105,as a clear manifestation of a new jet activity, always have the character of a reaction to changes in the conditions forgenerating X-rays in the accretion disk (MAXI) and in the corona (Swift). We have studied in details periodic radio flaresfrom the X-ray binary with bright Gamma-ray radiation LSI+61d303 for more than 60 orbital periods. Undoubtedly, thenature of these flares changes dramatically depending on the known super-orbital 4.6-year period.
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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 dimensionless variables used. Machine learning strategies can be attractive alternatives because they can be constructed either to minimize biases or to emphasize specific biases that reflect knowledge of the physics of the system. The investigation summarized here explored the use of machine learning methods as a tool for determining parametric trends in boiling heat transfer data, and as a means for developing methods to predict boiling heat transfer. Results are presented that demonstrate how genetic algorithms and other machine learning tools can be used to extract heat flux dependencies on system parameters. A key element of the machine learning analysis process is preparation of the data used. Use of raw data and use of dimensionless rescaled data are explored, and the advantages and disadvantages of each are assessed. Data for nucleate boiling of a binary mixture are analyzed to determine the heat flux dependence on wall superheat, gravity, Marangoni effects and pressure. The results provide new insight into how gravity and Marangoni effects interact in boiling processes of this type. The results also demonstrate how machine learning tools can clarify how different mechanisms interact in the boiling process, as well as directly providing the ability to predict heat transfer performance for design of heat transfer devices that involve nucleate boiling. Potential use of machine learning tools on big data collections for nucleate boiling processes to more broadly assess parametric effects is also discussed.
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Niemann, Hartmut, Hermann Winner, Christof Asbach, Heinz Kaminski, and Georg Frentz. "Influence of Pad Retraction and Air Gap Width between Brake Disc and Pad on PM10 Wear Emissions During Cruising Conditions." In EuroBrake 2021. FISITA, 2021. http://dx.doi.org/10.46720/3999227eb2021-ebs-004.
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According to estimations, brake wear particle emissions contribute with a share of up to 21 % to the traffic related PM10-emissions in urban environments. Depending on the brake system, a significant proportion of those emissions occurs under cruising conditions with released brake. During a WLTP exhaust cycle with two sliding calipers used in this study 16 % respectively 30 % of the total PM10-emissions occur during off-brake-phases. The influencing parameters and formation processes of those off-brake-emissions are subject of current research to evaluate potential reduction approaches. A residual brake torque due to a disc-pad contact in the off-brake-phases as well as an air flow in the air gap between disc and pad are discussed as potential causes for the generation of those emissions. In the current state of research it is neither known how off-brake-emissions can be influenced during operation of the brake nor how a potential off-brake-emission reduction would affect dust release during the brake events and emission factors for whole test cycles. An experimental setup for the independent adjustment of the air gap and retraction force on the brake pads is used to investigate the influence on PM10-emissions during WLTP cycle with two different sliding calipers. Therefore, the air gap is varied between 0.17 mm and 5 mm and PM10-emission factors are compared to the reference brake systems without pad retraction. The share of off-brake emissions to the overall PM10-emission factor during the off-brake-phase is reduced from 16 % respectively 30 % to 3 % during a WLTP cycle with pad retraction. Associated with this decrease during the off-brake phases, an increase of the emission during the on-brake-phases occurred with pad retraction. During the performed WLTP the pad retraction leads to a decrease of the PM10-emission factor by 4.5 % for the first sliding caliper, which was smaller than standard deviation and by 6.0 % for the second sliding caliper, which was slightly higher than the standard deviation of the emission factor. No significant variance of the PM10-emission factor between 0.17 mm and 5 mm air gap could be determined. Instead a binary behavior with and without retraction was observed.</p> Based on literature and the observations in this study, two mechanisms remain as possible origin for the major share of brake wear emission during off-brake-phases. A residual torque combined with a reservoir behavior as well as an airflow through the air gap between disc and pad smaller than 0.17 mm could be the causal mechanisms for off-brake-emissions. A residual torque as a source for contious particle generations and centrifugal forces acting on adhesive particles on the disc were excluded due to findings gained in the pad retraction experiments respectively by the observations described in the state of research.
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. This project contributes to the literature as one of the few published research projects that considers all typical categories of explanatory variables (individual and household socioeconomics, local built environment features, and travel and residential choice attitudes) as well as two new variables (accessibility to multi-use paths calculated by ArcGIS and a clustering effect represented by spatial autocorrelation) at two levels (level 1: binary choice of cycling/waking; level 2: cycling/walking time if yes at level 1) to better understand active travel demand. We use data from the 2012 Utah Travel Survey. At the first level, we use a spatial probit model to identify whether and why Salt Lake City residents walked or cycled. The second level is the development of a spatial autoregressive model for walkers and cyclists to examine what factors affect their travel time when using walking or cycling modes. The results from both levels, obtained while controlling for individual, attitudinal, and built-environment variables, show that accessibility to multi-use paths and a clustering effect (spatial autocorrelation) influence active travel behavior in different ways. Specifically, a cyclist is likely to cycle more when seeing more cyclists around. These findings provide analytical evidence to decision-makers for efficiently evaluating and deciding between plans and policies to enhance active transportation based on the two modeling approaches to assessing travel behavior described above.