The Experts below are selected from a list of 273 Experts worldwide ranked by ideXlab platform
Richard Breen - One of the best experts on this subject based on the ideXlab platform.
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comparing regression coefficients between same sample nested models using logit and Probit a new method
Sociological Methodology, 2012Co-Authors: Kristian Bernt Karlson, Anders Holm, Richard BreenAbstract:Logit and Probit models are widely used in empirical sociological research. However, the common practice of comparing the coefficients of a given variable across differently specified models fitted to the same sample does not warrant the same interpretation in logits and Probits as in linear regression. Unlike linear models, the change in the coefficient of the variable of interest cannot be straightforwardly attributed to the inclusion of confounding variables. The reason for this is that the variance of the underlying latent variable is not identified and will differ between models. We refer to this as the problem of rescaling. We propose a solution that allows researchers to assess the influence of confounding relative to the influence of rescaling, and we develop a test to assess the statistical significance of confounding. A further problem in making comparisons is that, in most cases, the error distribution, and not just its variance, will differ across models. Monte Carlo analyses indicate that oth...
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comparing regression coefficients between models using logit and Probit a new method
2010Co-Authors: Kristian Bernt Karlson, Anders Holm, Richard BreenAbstract:Logit and Probit models are widely used in empirical sociological research. However, the widespread practice of comparing the coefficients of a given variable across differently specified models does not warrant the same interpretation in logits and Probits as in linear regression. Unlike in linear models, the change in the coefficient of the variable of interest cannot be straightforwardly attributed to the inclusion of confounding variables. The reason for this is that the variance of the underlying latent variable is not identified and will differ between models. We refer to this as the problem of rescaling. We propose a solution that allows researchers to assess the influence of confounding relative to the influence of rescaling, and we develop a test statistic that allows researchers to assess the statistical significance of both confounding and rescaling. We also show why y-standardized coefficients and average partial effects are not suitable for comparing coefficients across models. We present examples of the application of our method using simulated data and data from the National Educational Longitudinal Survey.
Aeilko H Zwinderman - One of the best experts on this subject based on the ideXlab platform.
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Probit Meta-regression
Modern Meta-Analysis, 2017Co-Authors: Ton J. Cleophas, Aeilko H ZwindermanAbstract:If your predictor is multiple pharmacological treatment dosages, then Probit regression may be more convenient than (multinomial) logistic regression, because your results will be reported in the form of response rates instead of odds ratios.
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Probit Meta-regression
Modern Meta-Analysis, 2017Co-Authors: Ton J. Cleophas, Aeilko H ZwindermanAbstract:If your predictor is multiple pharmacological treatment dosages, then Probit regression Probit regression may be more convenient than (multinomial) logistic regression, because your results will be reported in the form of response rates instead of odds ratios. As an example, in a dose response meta-analysis of 14 mosquito studies with different dosages of chemical (chem) and nonchemical (nonchem) repellents the numbers of mosquitos gone after administration were assessed and meta-analyzed. The Probity regression model provided adequate power for comparing the effects of different dosages.
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Probit Regression, Binary Data as Response Rates (14 Tests)
SPSS for Starters and 2nd Levelers, 2016Co-Authors: Ton J. Cleophas, Aeilko H ZwindermanAbstract:Probit regression is for estimating the effect of predictors on yes/no outcomes. If your predictor is multiple pharmacological treatment dosages, then Probit regression may be more convenient than logistic regression, because your results will be reported in the form of response rates instead of odds ratios. The dependent variable of the two methods log odds (otherwise called logit) and log prob (otherwise called Probit) are closely related to one another. It can be shown that the log odds of responding ≈ (π/√3) × log probability of responding (see Chap. 7, Machine learning in medicine part three, Probit regression, pp 63–68, 2013, Springer Heidelberg Germany, from the same authors).
John Mullahy - One of the best experts on this subject based on the ideXlab platform.
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Estimation of Multivariate Probit Models via Bivariate Probit
National Bureau of Economic Research, 2020Co-Authors: John MullahyAbstract:Models having multivariate Probit and related structures arise often in applied health economics. When the outcome dimensions of such models are large, however, estimation can be challenging owing to numerical computation constraints and/or speed. This paper suggests the utility of estimating multivariate Probit (MVP) models using a chain of bivariate Probit estimators. The proposed approach offers two potential advantages over standard multivariate Probit estimation procedures: significant reductions in computation time; and essentially unlimited dimensionality of the outcome set. The time savings arise because the proposed approach does not rely simulation methods; the dimension advantage arises because only pairs of outcomes are considered at each estimation stage. Importantly, the proposed approach provides a consistent estimator of all the MVP model's parameters under the same assumptions required for consistent estimation based on standard methods, and simulation exercises suggest no loss of estimator precision.
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Marginal effects in multivariate Probit models
Empirical Economics, 2017Co-Authors: John MullahyAbstract:Estimation of marginal or partial effects of covariates x on various conditional parameters or functionals is often a main target of applied microeconometric analysis. In the specific context of Probit models, estimation of partial effects involving outcome probabilities will often be of interest. Such estimation is straightforward in univariate models, and results covering the case of quadrant probability marginal effects in bivariate Probit models for jointly distributed outcomes y have previously been described in the literature. This paper’s goals are to extend Greene’s results to encompass the general $$M\ge 2$$ M ≥ 2 multivariate Probit context for arbitrary orthant probabilities and to extended these results to models that condition on subvectors of y and to multivariate ordered Probit data structures. It is suggested that such partial effects are broadly useful in situations, wherein multivariate outcomes are of concern.
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Estimation of Multivariate Probit Models Via Bivariate Probit
Stata Journal, 2016Co-Authors: John MullahyAbstract:In this article, I suggest the utility of fitting multivariate Probit models using a chain of bivariate Probit estimators. This approach is based on Stata’s biProbit and suest commands and is driven by a Mata function, bvpmvp(). I discuss two potential advantages of the approach over the mvProbit command (Cappellari and Jenkins, 2003, Stata Journal 3: 278–294): significant reductions in computation time and essentially unlimited dimensionality of the outcome set. Computation time is reduced because the approach does not rely on simulation methods; unlimited dimensionality arises because only pairs of outcomes are con- sidered at each estimation stage. This approach provides a consistent estimator of all the multivariate Probit model’s parameters under the same assumptions re- quired for consistent estimation via mvProbit, and simulation exercises I provide suggest no loss of estimator precision relative to mvProbit. Copyright 2016 by StataCorp LP.
Kristian Bernt Karlson - One of the best experts on this subject based on the ideXlab platform.
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comparing regression coefficients between same sample nested models using logit and Probit a new method
Sociological Methodology, 2012Co-Authors: Kristian Bernt Karlson, Anders Holm, Richard BreenAbstract:Logit and Probit models are widely used in empirical sociological research. However, the common practice of comparing the coefficients of a given variable across differently specified models fitted to the same sample does not warrant the same interpretation in logits and Probits as in linear regression. Unlike linear models, the change in the coefficient of the variable of interest cannot be straightforwardly attributed to the inclusion of confounding variables. The reason for this is that the variance of the underlying latent variable is not identified and will differ between models. We refer to this as the problem of rescaling. We propose a solution that allows researchers to assess the influence of confounding relative to the influence of rescaling, and we develop a test to assess the statistical significance of confounding. A further problem in making comparisons is that, in most cases, the error distribution, and not just its variance, will differ across models. Monte Carlo analyses indicate that oth...
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comparing regression coefficients between models using logit and Probit a new method
2010Co-Authors: Kristian Bernt Karlson, Anders Holm, Richard BreenAbstract:Logit and Probit models are widely used in empirical sociological research. However, the widespread practice of comparing the coefficients of a given variable across differently specified models does not warrant the same interpretation in logits and Probits as in linear regression. Unlike in linear models, the change in the coefficient of the variable of interest cannot be straightforwardly attributed to the inclusion of confounding variables. The reason for this is that the variance of the underlying latent variable is not identified and will differ between models. We refer to this as the problem of rescaling. We propose a solution that allows researchers to assess the influence of confounding relative to the influence of rescaling, and we develop a test statistic that allows researchers to assess the statistical significance of both confounding and rescaling. We also show why y-standardized coefficients and average partial effects are not suitable for comparing coefficients across models. We present examples of the application of our method using simulated data and data from the National Educational Longitudinal Survey.
Ton J. Cleophas - One of the best experts on this subject based on the ideXlab platform.
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Probit Meta-regression
Modern Meta-Analysis, 2017Co-Authors: Ton J. Cleophas, Aeilko H ZwindermanAbstract:If your predictor is multiple pharmacological treatment dosages, then Probit regression may be more convenient than (multinomial) logistic regression, because your results will be reported in the form of response rates instead of odds ratios.
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Probit Meta-regression
Modern Meta-Analysis, 2017Co-Authors: Ton J. Cleophas, Aeilko H ZwindermanAbstract:If your predictor is multiple pharmacological treatment dosages, then Probit regression Probit regression may be more convenient than (multinomial) logistic regression, because your results will be reported in the form of response rates instead of odds ratios. As an example, in a dose response meta-analysis of 14 mosquito studies with different dosages of chemical (chem) and nonchemical (nonchem) repellents the numbers of mosquitos gone after administration were assessed and meta-analyzed. The Probity regression model provided adequate power for comparing the effects of different dosages.
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Probit Regression, Binary Data as Response Rates (14 Tests)
SPSS for Starters and 2nd Levelers, 2016Co-Authors: Ton J. Cleophas, Aeilko H ZwindermanAbstract:Probit regression is for estimating the effect of predictors on yes/no outcomes. If your predictor is multiple pharmacological treatment dosages, then Probit regression may be more convenient than logistic regression, because your results will be reported in the form of response rates instead of odds ratios. The dependent variable of the two methods log odds (otherwise called logit) and log prob (otherwise called Probit) are closely related to one another. It can be shown that the log odds of responding ≈ (π/√3) × log probability of responding (see Chap. 7, Machine learning in medicine part three, Probit regression, pp 63–68, 2013, Springer Heidelberg Germany, from the same authors).
