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Journal articles on the topic 'Binary logit'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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1

LIPOVETSKY, STAN. "CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS." Advances in Adaptive Data Analysis 03, no. 03 (July 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. In addition, for binary logit, it is possible to obtain meaningful coefficients of regression by transforming data to the linear link function, which opens a possibility to obtain meaningful parameters of the complicated models with categorical dependent variables.

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2

Shekhar, Suraj Kushe, Rehin K. R. Rehin K.R, and Dr P. T. Raveendran Dr. P.T Raveendran. "Demographic Variables & Nutritional Risk: The Predictive Power Using Binary Logit Model." Indian Journal of Applied Research 3, no. 11 (October 1, 2011): 278–80. http://dx.doi.org/10.15373/2249555x/nov2013/91.

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3

Purhadi, Purhadi, and M. Fathurahman. "A Logit Model for Bivariate Binary Responses." Symmetry 13, no. 2 (February 16, 2021): 326. http://dx.doi.org/10.3390/sym13020326.

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This article provides a bivariate binary logit model and statistical inference procedures for parameter estimation and hypothesis testing. The bivariate binary logit (BBL) model is an extension of the binary logit model that has two correlated binary responses. The BBL model responses were formed using a 2 × 2 contingency table, which follows a multinomial distribution. The maximum likelihood and Berndt–Hall–Hall–Hausman (BHHH) methods were used to obtain the BBL model. Hypothesis testing of the BBL model contains the simultaneous test and the partial test. The test statistics of the simultaneous test and the partial test were determined using the maximum likelihood ratio test method. The likelihood ratio statistics of the simultaneous test and the partial test were approximately asymptotically chi-square distributed with 3p degrees of freedom. The BBL model was applied to a real dataset, and the BBL model with the single covariate was better than the BBL model with multiple covariates.

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4

Moon, C. G. "Simulataneous specicication test in a binary logit." Communications in Statistics - Theory and Methods 17, no. 10 (January 1988): 3361–87. http://dx.doi.org/10.1080/03610928808829809.

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5

Sarkar, S. K., Habshah Midi, and Sohel Rana. "Adequacy of Multinomial Logit Model with Nominal Responses over Binary Logit Model." Trends in Applied Sciences Research 6, no. 8 (August 1, 2011): 900–909. http://dx.doi.org/10.3923/tasr.2011.900.909.

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6

Horowitz, Joel L., and N. E. Savin. "Binary Response Models: Logits, Probits and Semiparametrics." Journal of Economic Perspectives 15, no. 4 (November 1, 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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7

Rungie, Cam, and Gilles Laurent. "Repeated Binary Logit: Analysing Variation in Behavioural Loyalty." Australasian Marketing Journal (AMJ) 13, no. 2 (January 2005): 27–36. http://dx.doi.org/10.1016/s1441-3582(05)70075-6.

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8

Magnac, Thierry. "Panel Binary Variables and Sufficiency: Generalizing Conditional Logit." Econometrica 72, no. 6 (November 2004): 1859–76. http://dx.doi.org/10.1111/j.1468-0262.2004.00556.x.

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9

Gunby, Molly, and Ivan Damnjanovic. "Identifying Project Value Interests: A Binary Logit Model." International Journal of Architecture, Engineering and Construction 1, no. 3 (September 2012): 121–33. http://dx.doi.org/10.7492/ijaec.2012.014.

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10

Nugraha, Ryan Septiady. "Commuter Train Mode Choice Modelling Using Binary Logit Model." WIDYAKALA JOURNAL 6, no. 1 (February 22, 2019): 1. http://dx.doi.org/10.36262/widyakala.v6i1.137.

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Car production in Malaysia increasing dramatically. This situation created serious impact such as pollution and congestion. The Malaysian government should find a proper solution to prevent the vehicles growth by controlling them and improve public transportation services. The only way to get people to switch to public transportation is by improving the public transport system becomes more efficient. To find out the solution, an understanding of traveler behavior by applying to mode choice model using binary logit approach is necessary. Stated preferences method was adopted in order to construct hypothetical choice in current and future situations. A total of 250 respondents were selected as the sample based on the research study. This research employed a discrete choice analysis to examine the relationship between the independent variables (travel time, fares, comfort and safety). With variation of trip purpose (school, work, leisure activity, and shopping), model has been developed and tested to check the validity. The result shows that the potential of new train services to compete with the current commuter (KTM) and private car user are quite competitive. This is no doubt due to the characteristics of the respondent to choose a good level of services especially a better comfortability and safety with an affordable price (fares). It can be concluded that scenario 2 has great potential to be implemented since forecasting demand reached above 90%.

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11

Guerre, Emmanuel, and Hyungsik Roger Moon. "A note on the nonstationary binary choice logit model." Economics Letters 76, no. 2 (July 2002): 267–71. http://dx.doi.org/10.1016/s0165-1765(02)00052-6.

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12

Mitchell, Michael N., and Xiao Chen. "Visualizing Main Effects and Interactions for Binary Logit Models." Stata Journal: Promoting communications on statistics and Stata 5, no. 1 (February 2005): 64–82. http://dx.doi.org/10.1177/1536867x0500500111.

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This paper considers the role of covariates when using predicted probabilities to interpret main effects and interactions in logit models. While predicted probabilities are very intuitive for interpreting main effects and interactions, the pattern of results depends on the contribution of covariates. We introduce a concept called the covariate contribution, which reflects the aggregate contribution of all of the remaining predictors (covariates) in the model and a family of tools to help visualize the relationship between predictors and the predicted probabilities across a variety of covariate contributions. We believe this strategy and the accompanying tools can help researchers who wish to use predicted probabilities as an interpretive framework for logit models acquire and present a more comprehensive interpretation of their results. These visualization tools could be extended to other models (such as binary probit, multinomial logistic, ordinal logistic models, and other nonlinear models).

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13

Chen, Ming-Hui, Dipak K. Dey, and Qi-Man Shao. "Bayesian Analysis of Binary Data Using Skewed Logit Models." Calcutta Statistical Association Bulletin 51, no. 1-2 (March 2001): 11–30. http://dx.doi.org/10.1177/0008068320010102.

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14

Padzil, Siti Aisyah Mohd, Khuneswari Gopal Pillay, and Rohayu Mohd Salleh. "Model-Building of Multiple Binary Logit using Model Averaging." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 224. http://dx.doi.org/10.14419/ijet.v7i4.30.22125.

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Many researchers had been carried out on the study of statistical modelling, making it easier for new researchers in many sectors (social sciences, economics, medical, and etc.) to obtain knowledge in order to ease their research study. Nevertheless, there is still no agreed guidelines in obtaining the best model for multiple binary logit (MBL) using model averaging (MA). This research will demonstrate the proper guidelines to obtain best MBL model by using MA. Upper Gastrointestinal Bleed (UGIB) data were studied to illustrate the process of model-building using the proposed guidelines. This study will pinpoint the factors with high possibility leading to mortality of UGIB patients using obtained best model. Corrected Akaike Information Criteria (AICc) and Bayesian Information Criteria (BIC) were used to compute the weights in model averaging method. The performance of the models was computed by using Root mean square error (RMSE) and mean absolute error (MAE). Model obtained by using BIC weights showed a better performance since the RMSE and MAE values are lower compared to model obtained using AICc weights. The factors that affects the survivability of UGIB patients are shock score, comorbidity and rebleed. In conclusion, model-building of multiple binary logit using model averaging showed a better performance when using BIC.

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15

Nugraha, Muhammad Ryan Septiady. "Commuter Train Mode Choice Modelling Using Binary Logit Model." Jurnal Perkotaan 10, no. 2 (March 27, 2019): 112–26. http://dx.doi.org/10.25170/perkotaan.v10i2.311.

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Car production in Malaysia is increase dramatically. This situation created serious impact such as pollution and congestion. The Malaysian government should find a proper solution to prevent the vehicles growth by controlling them and improve public transportation services. The only way to get people switch to pubic transportation is by improving the public transport system becomes more efficient. To find out the solution, an understanding of traveller behavior by apply mode choice model using binary logit approach is necessary. Stated preferences method was adopted in order to construct hypothetical choice in current and future situations. A total of 250 respondents were selected as the sample based on the research study. This research employed a discrete choice analysis to examine the relationship between the independent variables (travel time, fares, comfort and safety). With variation of trip purpose (school, work, leisure activity, and shopping), model has been developed and tested to check the validity. The result shows that the potential of new train services to compete with the current commuter (KTM) and private car user are quite competitive. This is no doubt due to the characteristics of the respondent to choose a good level of services especially a better comfortability and safety with an affordable price (fares). It can be concluded that scenario 2 has great potential to be implemented since forecasting demand reached above 90%.

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16

Beggs, John J. "A simple model for heterogeneity in binary logit models." Economics Letters 27, no. 3 (January 1988): 245–49. http://dx.doi.org/10.1016/0165-1765(88)90178-4.

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17

Clark, Robert G., and Margo Barr. "A blended link approach to relative risk regression." Statistical Methods in Medical Research 27, no. 11 (March 13, 2017): 3325–39. http://dx.doi.org/10.1177/0962280217698174.

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A binary health outcome may be regressed on covariates using a log link, rather than more typical link functions such as the logit. This allows the exponentiated regression coefficient for each covariate to be interpreted as a relative risk conditional on the remaining covariates. Relative risks are simpler to interpret than the odds ratios which arise with a logit link. There are practical and conceptual challenges in log-link binary regression, mainly due to the requirement that probabilities are less than or equal to 1. Viable probabilities are now usually achieved by the imposition of a constraint on the parameter space, but the log link function is still more work to apply in practice. We propose instead a new smooth link function which is equal to the log up to a cutoff and a linearly scaled logit function above the cutoff. The new approach is conceptually clearer, simpler to implement and generally less biased, and it retains the relative risk interpretation for all but the highest risk individuals. Alternative binary regressions are compared using a simulation study and a diabetic retinopathy dataset.

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18

Jones, Bradford S., and Regina P. Branton. "Beyond Logit and Probit: Cox Duration Models of Single, Repeating, and Competing Events for State Policy Adoption." State Politics & Policy Quarterly 5, no. 4 (December 2005): 420–43. http://dx.doi.org/10.1177/153244000500500406.

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Since 1990, the standard statistical approach for studying state policy adoption has been an event history analysis using binary link models, such as logit or probit. In this article, we evaluate this logit-probit approach and consider some alternative strategies for state policy adoption research. In particular, we discuss the Cox model, which avoids the need to parameterize the baseline hazard function and, therefore, is often preferable to the logit-probit approach. Furthermore, we demonstrate how the Cox model can be modified to deal effectively with repeatable and competing events, events that the logit-probit approach cannot be used to model.

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19

Purnomo, Didit, and Chuzaimah Chuzaimah. "STUDI TENTANG NIATAN MENETAP MIGRAN SIRKULER (KASUS MIGRAN SIRKULER ASAL WONOGIRI KE JAKARTA)." Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi dan Pembangunan 5, no. 2 (May 2, 2017): 135. http://dx.doi.org/10.23917/jep.v5i2.4037.

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This aim of the study will analyse factors that influenced intention the migration of the circular migrant. To achieve the aim, this study used the binary logit analysis technique of the Logistics, with made use of the primary data from 100 people who were made the respondent. This model tried to find the model that was best with four scenarios in testing the hypothesis. Results of the analysis showed that the scenario 4 was the best model. The factor that significantly influencedkeniatan resided the migration, he as follows: education (EDUC), and the income in the urban field (INCOME). Results of the prediction from the binary analysis logit the Respondent that said they stayed consistent to continue to become the circular migrant (did not reside) was relatively big, by chance him to reached 92.9%. On the whole the Binary Logit Regression model that was worn to explain factors that influenced niatan resided the circular migrant to this Jakarta had percentage of correct prediction as big as 78%. This at the same time explained that the behaviour the respondents in this research continue to tended as the circular migrant.

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20

Liu, Bingquan, Yonghong Zhang, and Wei Du. "A Simplified C-Logit Stochastic User Equilibrium Model on Bimodal Transportation Network." Mathematical Problems in Engineering 2020 (April 25, 2020): 1–8. http://dx.doi.org/10.1155/2020/3702965.

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This paper investigates the C-logit stochastic user equilibrium (SUE) problem on a bimodal transportation network with road and rail travel modes. The C-logit model captures the overlapping effect among the different paths via commonality factors; sequentially, it has ability to obtain a more realistic traffic flow distribution pattern. In this paper, when we redefine the link travel cost functions and employ a binary Logit model for the mode split, the bimodal C-logit SUE model can be simplified into an unconstrained nonlinear mathematical programming formulation. Such model is verified to satisfy the bimodal C-logit SUE conditions at its stationary point and can be solved by existing algorithms. So, the simplified model can be convenient to be used on the general bimodal transportation network.

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21

Bel, Koen, Dennis Fok, and Richard Paap. "Parameter estimation in multivariate logit models with many binary choices." Econometric Reviews 37, no. 5 (April 9, 2016): 534–50. http://dx.doi.org/10.1080/07474938.2015.1093780.

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22

Li, Hui, Young-Chan Lee, Yan-Chun Zhou, and Jie Sun. "The random subspace binary logit (RSBL) model for bankruptcy prediction." Knowledge-Based Systems 24, no. 8 (December 2011): 1380–88. http://dx.doi.org/10.1016/j.knosys.2011.06.015.

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23

Cramer, J. S. "Predictive Performance of the Binary Logit Model in Unbalanced Samples." Journal of the Royal Statistical Society: Series D (The Statistician) 48, no. 1 (March 1999): 85–94. http://dx.doi.org/10.1111/1467-9884.00173.

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24

Wu, Rongning, and Yunwei Cui. "A PARAMETER-DRIVEN LOGIT REGRESSION MODEL FOR BINARY TIME SERIES." Journal of Time Series Analysis 35, no. 5 (May 8, 2014): 462–77. http://dx.doi.org/10.1111/jtsa.12076.

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25

Clarke, Judith A., and Marsha J. Courchane. "Implications of Stratified Sampling for Fair Lending Binary Logit Models." Journal of Real Estate Finance and Economics 30, no. 1 (February 2005): 5–31. http://dx.doi.org/10.1007/s11146-004-4829-5.

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26

Sukhanova, E. I., S. Y. Shirnaeva, and E. G. Repina. "Binary Choice Models-based Assessment of Company’s Financial Sustainability." SHS Web of Conferences 62 (2019): 13002. http://dx.doi.org/10.1051/shsconf/20196213002.

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Assessment of financial sustainability is a key instrument that every company should use to successfully operate in the contemporary marketplace. In this paper profit was chosen as one of the sustainability indices and binary choice model logistics regression model (logit model) was built for that index. The research data for this study is drawn from accounting statements of a textile industry business in Samara city. A combination of econometric approaches was used in the data analysis. Binary choice models were adopted in this research. Then those models were estimated for validity. Also scenario forecasts methodology was employed in this study. Several logit models with a set of explanatory variables were constructed. After the comparison of those models the preferred one was determined. Based on that model a scenario for profits was forecasted including both the worst-case and the best-case ones. The average-case scenario forecast was also made.

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27

Popovic-Stijacic, Milica, Ljiljana Mihic, and Dusica Filipovic-Djurdjevic. "Analyzing data from memory tasks - comparison of ANOVA, logistic regression and mixed logit model." Psihologija 51, no. 4 (2018): 469–88. http://dx.doi.org/10.2298/psi170615023p.

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We compared three statistical analyses over binary outcomes. As applying ANOVA over proportions violates at least two classical assumptions of linear models, two alternatives are described: the binary logistic regression and the mixed logit model. Firstly, we compared the effects obtained by the three methods over the same data from a previous memory research. All three methods gave similar results: the effects of the tasks and the number of sensory modalities were observed, but not their interaction. Secondly, by using the bootstrap estimates of the parameters, the efficacy of each method was explored. As predicted, the bootstrap parameter estimates of the ANOVA had large bias and standard errors, and consequently wide confidence intervals. On the other hand, the bootstrap parameter estimates of the binary logistic regression and the mixed logit models were similar ? both had low bias and standard errors and narrow confidence intervals.

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28

Schimek, Michae. "Penalized Binary Regression for Gene Expression Profiling." Methods of Information in Medicine 43, no. 05 (2004): 439–44. http://dx.doi.org/10.1055/s-0038-1633894.

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Summary Objectives: A typical bioinformatics task in microarray analysis is the classification of biological samples into two alternative categories. A procedure is needed which, based on the expression levels measured, allows us to compute the probability that a new sample belongs to a certain class. Methods: For the purpose of classification the statistical approach of binary regression is considered. High-dimensionality and at the same time small sample sizes make it a challenging task. Standard logit or probit regression fails because of condition problems and poor predictive performance. The concepts of frequentist and of Bayesian penalization for binary regression are introduced. A Bayesian interpretation of the penalized log-likelihood is given. Finally the role of cross-validation for regularization and feature selection is discussed. Results: Penalization makes classical binary regression a suitable tool for microarray analysis. We illustrate penalized logit and Bayesian probit regression on a well-known data set and compare the obtained results, also with respect to published results from decision trees. Conclusions: The frequentist and the Bayesian penalization concept work equally well on the example data, however some method-specific differences can be made out. Moreover the Bayesian approach yields a quantification (posterior probabilities) of the bias due to the constraining assumptions.

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29

D.V., Domashova, Kripak Е.М., Pisarchik Е.Е., and Ulanova Zh.U. "Modeling of Credit Institution License Withdrawal Based on Panel Data." KnE Social Sciences 3, no. 2 (February 15, 2018): 309. http://dx.doi.org/10.18502/kss.v3i2.1558.

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The research paper involves the construction of panel data binary response models to forecast the probability of a credit institution's license withdrawal based on its financial performance, including the construction of logit and probit models using various sets of source data offers a technique for shaping a general model. Keywords: credit institutions, panel data, binary response models, license withdrawal

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30

Mohammed, Abdela, Solomon Bizuayehu Wassie, and Ermias Tesfaye Teferi. "Determinants of Smallholders’ Food Security Status in Kalu District, Northern Ethiopia." Challenges 12, no. 2 (June 25, 2021): 17. http://dx.doi.org/10.3390/challe12020017.

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This study analyzed the determinants of rural households’ food security in the Kallu district of the Amhara region, Northern Ethiopia. The study used primary data collected from 395 randomly selected rural households. The study employed descriptive statistics and a binary logit model to estimate the status and determinants of smallholders’ food security, respectively. Of the total sample households, 47.30% are food insecure. The binary logit model results showed that sex and education level of the household head, livestock ownership, credit access, and technology adoption have positive and significant effects on food security, while age and market distance are negatively associated with the probability to be food secure. The results suggest that improving access to marketing and financial services will contribute to improving the food security status of smallholders.

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31

Jones, Stewart, and David A. Hensher. "Predicting Firm Financial Distress: A Mixed Logit Model." Accounting Review 79, no. 4 (October 1, 2004): 1011–38. http://dx.doi.org/10.2308/accr.2004.79.4.1011.

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Over the past three decades the literature on financial distress prediction has largely been confined to simple multiple discriminant analysis, binary logistic or probit analysis, or rudimentary multinomial logit models (MNL). There has been a conspicuous absence of modeling innovation in this literature as well as a failure to keep abreast of important methodological developments emerging in other fields of the social sciences. In particular, there has been no recognition of major advances in discrete choice modeling over the last 15 years, which has increasingly relaxed behaviorally questionable assumptions associated with the independently and identically distributed errors (IID) condition and allowed for observed and unobserved heterogeneity. In contrast to standard logit, the mixed logit model fulfils this purpose and provides a superior framework for explanation and prediction. We explain the theoretical and econometric underpinnings of mixed logit and demonstrate its empirical usefulness in the context of a specific but topical area of accounting research: financial distress prediction. Comparisons of model-fits and out-of-sample forecasts indicate that mixed logit outperforms standard logit by significant margins. While mixed logit has valuable applications in financial distress research, its potential usefulness in other areas of accounting research should not be overlooked.

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32

Sitter, R. R., and I. Fainaru. "Optimal designs for the logit and probit models for binary data." Canadian Journal of Statistics 25, no. 2 (June 1997): 175–90. http://dx.doi.org/10.2307/3315730.

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33

Onyeiwu, Steve. "Some Determinants of Core Competencies: Evidence from a Binary-Logit Analysis." Technology Analysis & Strategic Management 15, no. 1 (March 2003): 43–63. http://dx.doi.org/10.1080/0953732032000046042.

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34

김연준 and Geiguen Shin. "A Research on the Contagion Effect using the Binary Logit Model." Journal of Contemporary European Studies 30, no. 3 (December 2012): 273–96. http://dx.doi.org/10.17052/jces.2012.30.3.273.

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35

Hawas, Yaser E., and Faisal Ahmed. "A binary logit-based incident detection model for urban traffic networks." Transportation Letters 9, no. 1 (March 30, 2016): 49–62. http://dx.doi.org/10.1080/19427867.2016.1157132.

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36

Sutradhar, Brajendra C., Wasimul Bari, and Kalyan Das. "On probit versus logit dynamic mixed models for binary panel data." Journal of Statistical Computation and Simulation 80, no. 4 (April 2010): 421–41. http://dx.doi.org/10.1080/00949650802695656.

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37

Lipovetsky, Stan. "Analytical closed-form solution for binary logit regression by categorical predictors." Journal of Applied Statistics 42, no. 1 (June 27, 2014): 37–49. http://dx.doi.org/10.1080/02664763.2014.932760.

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38

Revuelta, Javier. "The generalized Logit-Linear Item Response Model for Binary-Designed Items." Psychometrika 73, no. 3 (December 15, 2007): 385–405. http://dx.doi.org/10.1007/s11336-007-9048-6.

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39

Lai, Xinjun, and Jun Li. "Modelling Stochastic Route Choice Behaviours with a Closed-Form Mixed Logit Model." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/729089.

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A closed-form mixed Logit approach is proposed to model the stochastic route choice behaviours. It combines both the advantages of Probit and Logit to provide a flexible form in alternatives correlation and a tractable form in expression; besides, the heterogeneity in alternative variance can also be addressed. Paths are compared by pairs where the superiority of the binary Probit can be fully used. The Probit-based aggregation is also used for a nested Logit structure. Case studies on both numerical and empirical examples demonstrate that the new method is valid and practical. This paper thus provides an operational solution to incorporate the normal distribution in route choice with an analytical expression.

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40

Liu, E. H., S. Tian, and Q. Chen. "Mechanism Research of Bus Dynamic Information Impact on the Commuter Travel Behavior." Applied Mechanics and Materials 743 (March 2015): 660–66. http://dx.doi.org/10.4028/www.scientific.net/amm.743.660.

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In this paper, according to the characteristics of the bus travel decision-making, the traffic behavior selection data in Nanjing were collected by designing traffic wishes questionnaires and a binary logit model was built on dynamic information service under the bus commuters travel route choice behavior of binary logit model. This paper analyses the effect by using the model parameter calibration including bus-taking time, bus congestion and personal information e.g. age and gender on the bus commuters travel route choice behavior. Studies have shown that public transport information are closely related to travel routes and travel activities, and bus commuters will make adjustments on travel route after obtaining travel information. Public transportation information can change the passengers’ state of participating in transportation and improve the level of the public transport system service in some ways.

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41

Breen, Richard, Kristian Bernt Karlson, and Anders Holm. "Interpreting and Understanding Logits, Probits, and Other Nonlinear Probability Models." Annual Review of Sociology 44, no. 1 (July 30, 2018): 39–54. http://dx.doi.org/10.1146/annurev-soc-073117-041429.

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Methods textbooks in sociology and other social sciences routinely recommend the use of the logit or probit model when an outcome variable is binary, an ordered logit or ordered probit when it is ordinal, and a multinomial logit when it has more than two categories. But these methodological guidelines take little or no account of a body of work that, over the past 30 years, has pointed to problematic aspects of these nonlinear probability models and, particularly, to difficulties in interpreting their parameters. In this review, we draw on that literature to explain the problems, show how they manifest themselves in research, discuss the strengths and weaknesses of alternatives that have been suggested, and point to lines of further analysis.

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42

Grilli, L., S. Metelli, and C. Rampichini. "Bayesian estimation with integrated nested Laplace approximation for binary logit mixed models." Journal of Statistical Computation and Simulation 85, no. 13 (July 11, 2014): 2718–26. http://dx.doi.org/10.1080/00949655.2014.935377.

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43

Ouyang, Yanfeng, Venky Shankar, and Toshiyuki Yamamoto. "Modeling the Simultaneity in Injury Causation in Multivehicle Collisions." Transportation Research Record: Journal of the Transportation Research Board 1784, no. 1 (January 2002): 143–52. http://dx.doi.org/10.3141/1784-18.

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Constrained by the single-equation specification, traditional logit or probit models for traffic accident severities can only model one severity at a time. Single-equation models fail to account for the simultaneity of injury severity outcomes in multivehicle collisions. An attempt to account for this simultaneity is made by presenting a simultaneous logit model of endogenous interrelationships among injury severities in multivehicle collisions. Car-truck collision data in Washington State between 1990 and 1996 are used for a simultaneous binary logit model to determine the effects of various exogenous factors in the presence of injury simultaneity, and empirical comparisons with traditional single-equation logit models are also provided. The methodology presented provides for significant efficiency gain when correlation between injury severities is high and can easily be extended to multiple severity categories in multiple-vehicle collisions. Empirically, it is also found that parameter estimates for key roadway design variables may vary greatly between a simultaneous logit and its single-equation counterparts. Another finding from this methodology is that preventive measures for head-on collisions, or enforcement of drunk-driving regulations among truck drivers, as well as limiting the occurrence of high-speed curve designs on truck routes, appear to be the most likely factors for significant potential savings in social costs from car-truck collisions.

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de Faria, Rute Q., Amanda R. P. dos Santos, Deoclecio J. Amorim, Renato F. Cantão, Edvaldo A. A. da Silva, and Maria M. P. Sartori. "Probit or Logit? Which is the better model to predict the longevity of seeds?" Seed Science Research 30, no. 1 (March 2020): 49–58. http://dx.doi.org/10.1017/s0960258520000136.

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AbstractThe prediction of seed longevity (P50) is traditionally performed by the use of the Probit model. However, due to the fact that the survival data are of binary origin (0,1), the fit of the model can be compromised by the non-normality of the residues. Consequently, this leads to prediction losses, despite the data being partially smoothed by Probit and Logit models. A possibility to reduce the effect of non-normality of the data would be to apply the principles of the central limit theorem, which states that non-normal residues tend to be normal as the n sample is increased. The Logit and Probit models differ in their normal and logistic distribution. Therefore, we developed a new estimation procedure by using a small increase of the n sample and tested it in the Probit and Logit functions to improve the prediction of P50. The results showed that the calculation of P50 by increasing the n samples from 4 to 6 replicates improved the index of correctness of the prediction. The Logit model presented better performance when compared with the Probit model, indicating that the estimation of P50 is more adequate when the adjustment of the data is performed by the Logit function.

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Mohajeri, Fatemeh, and Babak Mirbaha. "Studying the Role of Behavioral Characteristics in Individuals Travel Choice Behavior in Response to Earthquake Disaster Using Discrete Choice Models." Mathematical Problems in Engineering 2021 (September 14, 2021): 1–17. http://dx.doi.org/10.1155/2021/6917548.

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The study of decision-making in response to earthquake disaster including evacuation and destination choice behavior is necessary for emergency planners in order to provide emergency traffic management and solve evacuation problems in transportation network. In this research, it is attempted to study the pattern of choice behavior in Qazvin transport network by considering physical and behavioral factors simultaneously. The required data were collected through a questionnaire from 546 inhabitants of Qazvin based on the stated preferences (SP) survey for six designated earthquake scenarios at different severities and times. The effects of socioeconomic factors, trip daily characteristics, and behavioral factors on decisions to evacuate and choose destinations are studied in this research. The binary logit model is applied for analyzing the evacuation decision, and the multinomial logit model is applied for the destination choice in response to the earthquake disaster. The binary logit model analysis with 73% correct prediction indicates that socioeconomic characteristics including age, marital status, family size, and car ownership have positive effects on evacuation choice. Furthermore, the analysis of behavioral factors indicates that moderate religious belief decreases the likelihood of evacuation, while following previous experiences, trust in acquired trainings, and following decisions made by others moderately increase the likelihood of evacuation. Also, socioeconomic and behavioral factors are the determinants of the destination decision, and the results indicate that individuals following previous experiences highly are more likely to choose gas station and relative’s home destination versus going to urban open spaces.

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Bell, Caroline D., Roland K. Roberts, Burton C. English, and William M. Park. "A Logit Analysis of Participation in Tennessee's Forest Stewardship Program." Journal of Agricultural and Applied Economics 26, no. 2 (December 1994): 463–72. http://dx.doi.org/10.1017/s1074070800026389.

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AbstractThis study determines the likely effect of cost-share incentives on participation in the Tennessee forest Stewardship Program and identifies other factors that may contribute to participation. A random utility model is used to determine the probability that a landowner will choose to participate in the program. A binary choice model is specified to represent the dichotomous decision and a logit procedure is used to fit the model. Data are obtained from mail surveys of 4,000 randomly selected landowners. Results indicate that attitudes and knowledge of forestry programs may be more influential in a landowner's decision to participate than monetary incentives.

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Oskonbaeva, Zamira. "Determinants of credit ratings: evidence from panel discrete model." Economics and Business Letters 9, no. 3 (December 8, 2020): 240–47. http://dx.doi.org/10.17811/ebl.9.3.2020.240-247.

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This study aims to explore how changes in explanatory variables may affect the probability of sovereign credit ratings assigned by Fitch, which is assumed to be a binary choice variable. For this purpose annual data of selected developed and developing countries for the period 2000-2016 have been used. All the data have been collected from World Bank database and Fitch website. In the empirical analysis the binary logit model has been applied. It can be concluded that the determinants of sovereign credit ratings can help sovereigns to better understand the drivers of their credit rating.

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Li, Wenhua, and Tsuyoshi Adachi. "Quantitative estimation of resource nationalism by binary choice logit model for panel data." Resources Policy 53 (September 2017): 247–58. http://dx.doi.org/10.1016/j.resourpol.2017.07.002.

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Puan, O. C., Y. A. H. Hassan, N. Mashros, M. K. Idham, N. A. Hassan, M. N. M. Warid, and M. R. Hainin. "Transportation mode choice binary logit model: a case study for Johor Bahru city." IOP Conference Series: Materials Science and Engineering 527 (May 29, 2019): 012066. http://dx.doi.org/10.1088/1757-899x/527/1/012066.

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Zheng, Nan, and Brajendra C. Sutradhar. "Inferences in dynamic logit models in semi-parametric setup for repeated binary data." Journal of Statistical Computation and Simulation 88, no. 7 (January 30, 2018): 1295–313. http://dx.doi.org/10.1080/00949655.2018.1430800.

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