Pages that link to "Item:Q2736917"
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The following pages link to Monotonic algorithms for maximum likelihood estimation in generalized linear models (Q2736917):
Displaying 11 items.
- Bayesian skew-probit regression for binary response data (Q462146) (← links)
- Monotonicity of quadratic-approximation algorithms (Q757004) (← links)
- A modified iterative proportional scaling algorithm for estimation in regular exponential families (Q804133) (← links)
- Maximum likelihood computation based on the Fisher scoring and Gauss-Newton quadratic approximations (Q1020014) (← links)
- The lower bound method in probit regression. (Q1285476) (← links)
- Maximum likelihood estimation of link function parameters (Q1391889) (← links)
- The unifying role of iterative generalized least squares in statistical algorithms. With comments by Bent Jørgensen, Peter McCullagh, Joe R. Hill and a rejoinder by the author (Q1595978) (← links)
- Schwarz Methods for Quasi-Likelihood in Generalized Linear Models (Q3543742) (← links)
- On the principle of monotone likelihood and log-linear models (Q3713297) (← links)
- Zero-inflated generalized extreme value regression model for binary response data and application in health study (Q5887954) (← links)
- Low-dose extrapolation using the power family of response functions. (Q5941107) (← links)
