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Journal articles on the topic 'Binary dependent variables'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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1

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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2

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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4

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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5

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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6

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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7

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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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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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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Peña-Macias, Victor, and Humberto Sarria - Zapata. "Characteristic-dependent linear rank inequalities in 21 variables." Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales 43, no. 169 (2019): 764–70. http://dx.doi.org/10.18257/raccefyn.928.

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In Linear Algebra over finite fields, a characteristic-dependent linear rank inequality is a linear inequality that holds by ranks of spans of vector subspaces of a finite dimensional vector space over a finite field of determined characteristic, and does not in general hold over fields with other characteristic. This paper shows a preliminary result in the production of these inequalities. We produce three new inequalities in 21 variables using as guide a particular binary matrix, with entries in a finite field, whose rank is 8, with characteristic 2; 9 with characteristic 3; or 10 with chara

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Jiang, Wenxin, and Martin A. Tanner. "RISK MINIMIZATION FOR TIME SERIES BINARY CHOICE WITH VARIABLE SELECTION." Econometric Theory 26, no. 5 (2010): 1437–52. http://dx.doi.org/10.1017/s0266466609990636.

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This paper considers the problem of predicting binary choices by selecting from a possibly large set of candidate explanatory variables, which can include both exogenous variables and lagged dependent variables. We consider risk minimization with the risk function being the predictive classification error. We study the convergence rates of empirical risk minimization in both the frequentist and Bayesian approaches. The Bayesian treatment uses a Gibbs posterior constructed directly from the empirical risk instead of using the usual likelihood-based posterior. Therefore these approaches do not r

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15

Bergtold, Jason S., Aris Spanos, and Eberechukwu Onukwugha. "Bernoulli Regression Models: Revisiting the Specification of Statistical Models with Binary Dependent Variables." Journal of Choice Modelling 3, no. 2 (2010): 1–28. http://dx.doi.org/10.1016/s1755-5345(13)70033-2.

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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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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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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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Luo, Shali, and J. Isaac Miller. "On the spatial correlation of international conflict initiation and other binary and dyadic dependent variables." Regional Science and Urban Economics 44 (January 2014): 107–18. http://dx.doi.org/10.1016/j.regsciurbeco.2013.10.004.

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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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21

Telles, Charles Roberto. "Measuring nonlinearity by means of static parameters in Bernoulli binary sequences distribution: A brief approach." International Journal of Modeling, Simulation, and Scientific Computing 11, no. 03 (2020): 2050021. http://dx.doi.org/10.1142/s179396232050021x.

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This paper analyzes Bernoulli’s binary sequences in the representation of empirical nonlinear events, analyzing the distribution of natural resources, population sizes and other variables that influence the possible outcomes of resource’s usage. Consider the event as a nonlinear system and the metrics of analysis consisting of two dependent random variables 0 and 1, with memory and probabilities in maximum finite or infinite lengths, constant and equal to 1/2 for both variables (stationary process). The expressions of the possible trajectories of metric space represented by each binary paramet

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Valsamis, Epaminondas Markos, Henry Husband, and Gareth Ka-Wai Chan. "Segmented Linear Regression Modelling of Time-Series of Binary Variables in Healthcare." Computational and Mathematical Methods in Medicine 2019 (December 6, 2019): 1–7. http://dx.doi.org/10.1155/2019/3478598.

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Introduction. In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention. A common problem faced by researchers is distinguishing change due to secular trends from change due to an intervention. Interrupted time-series analysis has been shown to be effective in describing trends in retrospective time-series and in detecting change, but methods are often biased towards the point of the intervention. Binary outcomes are typically modelled by logistic regression where the log-odds of the binary event is expressed as a function of covari

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HR, Titi Kurnianti, Muhammad Nadjib Bustan, and R. Ruliana. "Pemodelan Faktor-Faktor yang Mempengaruhi Jenis Kanker Payudara Menggunakan Regresi Logistik Biner (Kasus : Pasien Penderita Kanker Payudara di RSUP Dr. Wahidin Sudirohusodo tahun 2016)." VARIANSI: Journal of Statistics and Its application on Teaching and Research 1, no. 3 (2019): 40. http://dx.doi.org/10.35580/variansiunm12898.

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Abstrak Regresi logistik adalah suatu metode analisis statistik yang diterapkan untuk memodelkan variabel dependen yang memiliki dua kategori atau lebih dengan satu atau lebih variabel independen. Regresi Logistik biner merupakan suatu analisis statistika yang digunakan untuk menganalisis hubungan antara satu atau lebih peubah bebas dengan peubah respon yang bersifat biner atau dichotomous. Peubah bebas pada regresi logistik dapat berupa peubah skala kategorik maupun peubah yang skala kontinu sedangkan peubah respon berupa peubah berskala kategorik. Regresi Logistik Biner dapat diterapkan pada

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24

Suzana, Stojkovic, Velickovic Darko, and Moraga Claudio. "Genetic algorithm for binary and functional decision diagrams optimization." Facta universitatis - series: Electronics and Energetics 31, no. 2 (2018): 169–87. http://dx.doi.org/10.2298/fuee1802169s.

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Decision diagrams (DD) are a widely used data structure for discrete functions representation. The major problem in DD-based applications is the DD size minimization (reduction of the number of nodes), because their size is dependent on the variables order. Genetic algorithms are often used in different optimization problems including the DD size optimization. In this paper, we apply the genetic algorithm to minimize the size of both Binary Decision Diagrams (BDDs) and Functional Decision Diagrams (FDDs). In both cases, in the proposed algorithm, a Bottom-Up Partially Matched Crossover (BU-PMX

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25

MENDES, ALEXANDRE C., and NASSER FARD. "BINARY LOGISTIC REGRESSION AND PHM ANALYSIS FOR RELIABILITY DATA." International Journal of Reliability, Quality and Safety Engineering 21, no. 05 (2014): 1450023. http://dx.doi.org/10.1142/s0218539314500235.

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This study proposes a modification for the binary logistic regression to treat time-dependent covariates for reliability studies. The proportional hazard model (PHM) properties are well suited for modeling survival data when there are categorical predictors; as it compares hazards to a reference category. However, time-dependent covariates present a challenge for the analysis as stratification does not produce hazards for the covariate stratified or creation of dummy time-dependent covariates faces difficulty on selecting the time interval for the interaction and the coefficient results may be

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Donkers, Bas, Philip Hans Franses, and Peter C. Verhoef. "Selective Sampling for Binary Choice Models." Journal of Marketing Research 40, no. 4 (2003): 492–97. http://dx.doi.org/10.1509/jmkr.40.4.492.19395.

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Marketing problems sometimes pertain to the analysis of dichotomous dependent variables, such as “buy” and “not buy” or “respond” and “not respond.” One outcome can strongly outnumber the other, such as when many households do not respond (e.g., to a direct mailing). In such situations, an efficient data-collection strategy is to sample disproportionately more from the smaller group. However, subsequent statistical analysis must account for this sampling strategy. In this article, the authors put forward the econometric method that can correct for the sample selection bias, when this method do

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Zorn, Christopher. "A Solution to Separation in Binary Response Models." Political Analysis 13, no. 2 (2005): 157–70. http://dx.doi.org/10.1093/pan/mpi009.

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A common problem in models for dichotomous dependent variables is “separation,” which occurs when one or more of a model's covariates perfectly predict some binary outcome. Separation raises a particularly difficult set of issues, often forcing researchers to choose between omitting clearly important covariates and undertaking post—hoc data or estimation corrections. In this article I present a method for solving the separation problem, based on a penalized likelihood correction to the standard binomial GLM score function. I then apply this method to data from an important study on the postwar

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Parsaulian, Agustinus Salomo, Tarno Tarno, and Dwi Ispriyanti. "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENERIMA BERAS RASKIN MENGGUNAKAN REGRESI LOGISTIK BINER DENGAN GUI R." Jurnal Gaussian 10, no. 1 (2021): 31–43. http://dx.doi.org/10.14710/j.gauss.v10i1.30934.

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The Rice Subsidy Program for Low-Income Communities or the Raskin Program is one of the government's programs to eradicate poverty. However, in practice, determining the criteria for Raskin recipients is a complicated problem. The Raskin program is a cross-sectoral national program both horizontally and vertically, to help meet the rice needs of low-income citizens. Determining the criteria for Raskin recipients is often a complicated issue. This study aims to analyze the classification of the Target Households (RTS) for the Raskin Program. The method used is binary logistic regression by util

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Philips, Andrew Q. "An easy way to create duration variables in binary cross-sectional time-series data." Stata Journal: Promoting communications on statistics and Stata 20, no. 4 (2020): 916–30. http://dx.doi.org/10.1177/1536867x20976322.

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In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into

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Sartori, Anne E. "An Estimator for Some Binary-Outcome Selection Models Without Exclusion Restrictions." Political Analysis 11, no. 2 (2003): 111–38. http://dx.doi.org/10.1093/pan/mpg001.

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This article provides a new maximum-likelihood estimator for selection models with dichotomous dependent variables when identical factors affect the selection equation and the equation of interest. Such situations arise naturally in game-theoretic models where selection is typically nonrandom and identical explanatory variables influence all decisions under investigation. When identical explanatory variables influence selection and a subsequent outcome of interest, the commonly used Heckman-type estimators identify from distributional assumptions about the residuals alone. When its own identif

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Wooldridge, Jeffrey M. "Multiplicative Panel Data Models Without the Strict Exogeneity Assumption." Econometric Theory 13, no. 5 (1997): 667–78. http://dx.doi.org/10.1017/s0266466600006125.

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This paper considers estimation of multiplicative, unobserved components panel data models without imposing a strict exogeneity assumption on the conditioning variables. The method of moments estimators proposed have significant robustness properties. They require only a conditional mean assumption and apply to models with lagged dependent variables and to finite distributed lag models with arbitrary feedback from the explained to future values of the explanatory variables. The model is particularly suited to nonnegative explained variables, including count variables, continuously distributed

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Goss, Ernst P., and Bernard J. Schroer. "The use of spreadsheet packages in industrial engineering — The case of regression analysis with binary dependent variables." Computers & Industrial Engineering 18, no. 2 (1990): 129–32. http://dx.doi.org/10.1016/0360-8352(90)90023-f.

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Spiess, Martin, and Martin Kroh. "A Selection Model for Panel Data: The Prospects of Green Party Support." Political Analysis 18, no. 2 (2010): 172–88. http://dx.doi.org/10.1093/pan/mpp045.

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Although sample selection bias is a frequent problem of applied research, there has been no generalization of sample selection models with binary dependent variables of interest to data with temporal error correlations. We suggest a generalized estimating equation approach to panel data selection models, considering binary responses in both equations. We demonstrate the utility of this model by a simulation study and by analyzing highly unbalanced annual panel data taken from the German Socio-Economic Panel Study covering two decades of Green party support.

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LIPOVETSKY, STAN. "CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS." Advances in Adaptive Data Analysis 03, no. 03 (2011): 309–24. http://dx.doi.org/10.1142/s1793536911000738.

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For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations. The paper shows that by a special rearrangement of data the more complicated conditional and multinomial models can be reduced to binary logistic regression. It suggests the usage of any software widely available for logit modeling to facilitate constructing for complex conditional and multinomial regressions

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35

Hug, Simon. "The Effect of Misclassifications in Probit Models: Monte Carlo Simulations and Applications." Political Analysis 18, no. 1 (2010): 78–102. http://dx.doi.org/10.1093/pan/mpp033.

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The increased use of models with limited-dependent variables has allowed researchers to test important relationships in political science. Often, however, researchers employing such models fail to acknowledge that the violation of some basic assumptions has in part difference consequences in nonlinear models than in linear ones. In this paper, I demonstrate this for binary probit models in which the dependent variable is systematically miscoded. Contrary to the linear model, such misclassifications affect not only the estimate of the intercept but also those of the other coefficients. In a Mon

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Freese, Jeremy. "Least Likely Observations in Regression Models for Categorical Outcomes." Stata Journal: Promoting communications on statistics and Stata 2, no. 3 (2002): 296–300. http://dx.doi.org/10.1177/1536867x0200200306.

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This article presents a method and program for identifying poorly fitting observations for maximum-likelihood regression models for categorical dependent variables. After estimating a model, the program leastlikely will list the observations that have the lowest predicted probabilities of observing the value of the outcome category that was actually observed. For example, when run after estimating a binary logistic regression model, leastlikely will list the observations with a positive outcome that had the lowest predicted probabilities of a positive outcome and the observations with a negati

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Luque-Fernandez, Miguel Angel, Daniel Redondo-Sánchez, and Camille Maringe. "cvauroc: Command to compute cross-validated area under the curve for ROC analysis after predictive modeling for binary outcomes." Stata Journal: Promoting communications on statistics and Stata 19, no. 3 (2019): 615–25. http://dx.doi.org/10.1177/1536867x19874237.

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Receiver operating characteristic (ROC) analysis is used for comparing predictive models in both model selection and model evaluation. ROC analysis is often applied in clinical medicine and social science to assess the tradeoff between model sensitivity and specificity. After fitting a binary logistic or probit regression model with a set of independent variables, the predictive performance of this set of variables can be assessed by the area under the curve (AUC) from an ROC curve. An important aspect of predictive modeling (regardless of model type) is the ability of a model to generalize to

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Breunig, Christoph, Michael Kummer, Joerg Ohnemus, and Steffen Viete. "Information technology outsourcing and firm productivity: eliminating bias from selective missingness in the dependent variable." Econometrics Journal 23, no. 1 (2019): 88–114. http://dx.doi.org/10.1093/ectj/utz016.

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Summary Missing values are a major problem in all econometric applications based on survey data. A standard approach assumes data are missing at random and uses imputation methods or even listwise deletion. This approach is justified if item nonresponse does not depend on the potentially missing variables’ realization. However, assuming missingness at random may introduce bias if nonresponse is, in fact, selective. Relevant applications range from financial or strategic firm-level data to individual-level data on income or privacy-sensitive behaviors. In this paper, we propose a novel approach

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Ngudiantoro, Ngudiantoro, Irmeilyana Irmeilyana, and Mukhlizar Samsuri. "Binary Logistic Regression Modeling on Net Income of Pagar Alam Coffee Farmers." International Journal of Applied Sciences and Smart Technologies 2, no. 2 (2020): 47–66. http://dx.doi.org/10.24071/ijasst.v2i2.2734.

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Pagar Alam Coffee is a Besemah coffee originating from the Smallholder Plantation in South Sumatra, Indonesia. The majority of Pagar Alam coffee farming is a hereditary business. Coffee farmers' income is very dependent on coffee production, production costs, and coffee prices. This study aims to obtain a probability model of Pagar Alam coffee farmers income based on the factors that influence it. The independent variables studied were the number of dependents, economic conditions, number of trees, age of trees, frequency of fertilizer used, frequency of pesticide used, production at harvest t

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Muscas, Giovanni, Tommaso Matteuzzi, Eleonora Becattini, et al. "Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage." Acta Neurochirurgica 162, no. 12 (2020): 3093–105. http://dx.doi.org/10.1007/s00701-020-04484-6.

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Abstract Background Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH. Methods We consulted electronic records of patients with aneurysmal SAH treated at our institution between Janu

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Díaz-Pérez, Manuel, Ángel Carreño-Ortega, José-Antonio Salinas-Andújar, and Ángel-Jesús Callejón-Ferre. "Application of Logistic Regression Models for the Marketability of Cucumber Cultivars." Agronomy 9, no. 1 (2019): 17. http://dx.doi.org/10.3390/agronomy9010017.

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The aim of this study is to establish a binary logistic regression method to evaluate and select cucumber cultivars (Cucumis sativus L.) with a longer postharvest shelf life. Each sample was evaluated for commercial quality (fruit aging, weight loss, wilting, yellowing, chilling injury, and rotting) every 7 days of storage. Simple and multiple binary logistic regression models were applied in which the dependent variable was the probability of marketability and the independent variables were the days of storage, cultivars, fruit weight loss, and months of evaluation. The results showed that cu

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Salim, Abdurrahman, and Muhammad Rijal Alfian. "Optimalisasi Regresi Logistik Menggunakan Algoritma Genetika Pada Data Klasifikasi." Jurnal Teknologi Informasi dan Terapan 6, no. 2 (2019): 50–55. http://dx.doi.org/10.25047/jtit.v6i2.109.

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Abstract— Classification on large of data, and with a variety of features or attributes often makes the law accuracy. It required a method that has immunity in such diverse data types. One of method is Logistic Regression method. Logistic Regression is one of classification method, if response variable has binary characteristic and there are many predictor variable such as combination of category and continue.Methd of Logistic Regression requires a stage selection independent variable in improving the model accuration. So it takes a good method in fixing the deficiency is Genetic Algorithm (GA

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Kuha, Jouni, and Colin Mills. "On Group Comparisons With Logistic Regression Models." Sociological Methods & Research 49, no. 2 (2018): 498–525. http://dx.doi.org/10.1177/0049124117747306.

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It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response and the second when models are compared between groups that have different distributions of other causes of the binary response. We argue that these concerns are usually misplaced. The first of them is only relevant if the unobserved continuous response is re

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Pestova, A. "Predicting Turning Points of the Business Cycle: Do Financial Sector Variables Help?" Voprosy Ekonomiki, no. 7 (July 20, 2013): 63–81. http://dx.doi.org/10.32609/0042-8736-2013-7-63-81.

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The objective of this study is to develop a system of leading indicators of the business cycle turning points for a wide range of countries, including Russia, over a period of more than thirty years. We use a binary choice model with the dependent variable of the state of economy: the recession, there is no recession. These models allow us to assess how likely is the change of macroeconomic dynamics from positive to negative and vice versa. Empirical analysis suggests that the inclusion of financial sector variables into equation can significantly improve the predictive power of the models of

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Toyibah, Dzuriyatun. "The Gender Gap and Career Path of the Academic Profession Under the Civil Service System at a Religious University in Jakarta, Indonesia." KOMUNITAS: International Journal of Indonesian Society and Culture 10, no. 1 (2018): 1–13. http://dx.doi.org/10.15294/komunitas.v10i1.12228.

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In this article I argue that male academics under civil servant system in a religious university still dominate the highest academic positions. This study applies logistic regression (binary and ordinal regression) since the available data, especially for dependent variable, is categorical and it does not fulfil the assumption of ordinary least square. By applying ordinal regression, gender is found to be undetected compared to other variables (age, length of tenure, and educational qualifications). Nevertheless, a statistical analysis utilising binary regression indicates that gender is a sig

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Aprilia, Aprilia, Nursalam Nursalam, and Candra Panji Asmoro. "RIGHT MEDICATION RELATED TO DRUG CENTRALIZED IN RSUD SIDOARJO." INDONESIAN NURSING JOURNAL OF EDUCATION AND CLINIC (INJEC) 1, no. 2 (2017): 187. http://dx.doi.org/10.24990/injec.v1i2.112.

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Introduction. Centralized drug is a management of the entire drug which is entirely done by nurses to administration to patients. Right medication is the process of right drug administration which is done by nurses based on 6 rights of medication, and wary of side effects. The purpose of this study was to analyze the corelation between centralized drug, team leadership, and nurse`s knowledge with right medication among nurses. Methods.The design of the study was descriptive corelational with cross-sectional approach. The population was inpatient nurses in RSUD Sidoarjo. Total sample was 114 re

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Sa'diah, Chalimatus, Tatik Widiharih, and Arief Rachman Hakim. "KLASIFIKASI PEMBERIAN KREDIT SEPEDA MOTOR MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN CHI-SQUARED AUTOMATIC INTERACTION DETECTION (CHAID) DENGAN GUI R (Studi Kasus: Kredit Sepeda Motor di PT X)." Jurnal Gaussian 10, no. 2 (2021): 159–69. http://dx.doi.org/10.14710/j.gauss.v10i2.29923.

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One of the factors causing the bankruptcy of a company is bad credit. Therefore, prospective customers need to be selected so that bad credit cases can be minimized. This study aims to determine the classification of credit granting to prospective customers of company X in order to reduce the risk of bad credit. The method used is the binary logistic regression method and the Chi-Squared Automatic Interaction Detection (CHAID) method. In this study, data used in November 2019 were 690 motorcycle credit data for company X in Gresik. The independent variables in this study are the factors that a

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Et. al., Mahdi Wahhab Neamah,. "Utilizing the Logistic Regression Model in Analyzing the Categorical Data of Economic Effects." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 4 (2021): 638–46. http://dx.doi.org/10.17762/turcomat.v12i4.547.

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The categorical data has a significant role in representing statistical binary variables, and they are analyzed by means of grouping the response variable into ordered categories. Thereby, the dependent variable becomes of type binary qualitative variable. The data related to the financial position of world countries is classified within the categorical data. This work is to study the economic effects of an individual's different factors on determining the richness or poorness levels of a selected population of countries. Moreover, a logistic regression model is to be created to estimate these

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Mezhennaya, Natalia M., and Vladimir G. Mikhailov. "On the number of ones in outcome sequence of extended Pohl generator." Discrete Mathematics and Applications 30, no. 5 (2020): 327–37. http://dx.doi.org/10.1515/dma-2020-0029.

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AbstractFormulas for distributions of number of ones (non-zeroes) in the cycle of the output sequence of generalized binary Pohl generator are obtained. Limit theorems for these distributions are derived in the case when the lengths of registers are coprime and tend to infinity, the contents of different registers are independent, but cell contents within each register may be dependent. The consequences of these theorems are given for the case when the contents of cells are independent random variables having equiprobable distribution on {0, 1}.

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Liu, Yue, and Madhu S. Mohanty. "Asymptotic Variance–Covariance Matrices of Two-Stage Estimators in the Presence of Continuous and Binary Dependent Variables with an Empirical Application." Journal of Quantitative Economics 13, no. 1 (2015): 53–75. http://dx.doi.org/10.1007/s40953-015-0003-6.

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