
Accurate inference in negative binomial regression
Negative binomial regression is commonly employed to analyze overdispers...
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Estimation of Dirichlet distribution parameters with biasreducing adjusted score functions
The Dirichlet distribution, also known as multivariate beta, is the most...
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The DeBiased Whittle Likelihood
The Whittle likelihood is a widely used and computationally efficient ps...
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Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are NonOverlapping Lists
Multiple systems estimation strategies have recently been applied to qua...
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A Maximum Entropy Procedure to Solve Likelihood Equations
In this article we provide initial findings regarding the problem of sol...
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Replica analysis of overfitting in generalized linear models
Nearly all statistical inference methods were developed for the regime w...
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Supervised Autoencoders Learn Robust Joint Factor Models of Neural Activity
Factor models are routinely used for dimensionality reduction in modelin...
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Bias Reduction as a Remedy to the Consequences of Infinite Estimates in Poisson and Tobit Regression
Data separation is a wellstudied phenomenon that can cause problems in the estimation and inference from binary response models. Complete or quasicomplete separation occurs when there is a combination of regressors in the model whose value can perfectly predict one or both outcomes. In such cases, and such cases only, the maximum likelihood estimates and the corresponding standard errors are infinite. It is less widely known that the same can happen in further microeconometric models. One of the few works in the area is Santos Silva and Tenreyro (2010) who note that the finiteness of the maximum likelihood estimates in Poisson regression depends on the data configuration and propose a strategy to detect and overcome the consequences of data separation. However, their approach can lead to notable bias on the parameter estimates when the regressors are correlated. We illustrate how biasreducing adjustments to the maximum likelihood score equations can overcome the consequences of separation in Poisson and Tobit regression models.
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