Regularized Maximum Likelihood Estimation for the Random Coefficients Model in Python
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- Type
- preprint
- Published
- 2025-11-24
- Cited by
- 0
- References
- 29
- Access
- Open access
- OpenAlex
- https://openalex.org/W3187193180
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:262918455
Keywords
Python (programming language), Computer science, Algorithm, Programming language, Applied mathematics
References
- An Interior Point Algorithm for Large-Scale Nonlinear Programming
- Asymptotically Minimax Adaptive Estimation. I: Upper Bounds. Optimally Adaptive Estimates
- On nonparametric estimation of intercept and slope distributions in random coefficient regression
- Iteratively regularized Newton-type methods for general data misfit functionals and applications to Poisson data
- Convergence rates in expectation for Tikhonov-type regularization of inverse problems with Poisson data
- The Lepskii principle revisited
- ANALYZING THE RANDOM COEFFICIENT MODEL NONPARAMETRICALLY
- On parameter identification in stochastic differential equations by penalized maximum likelihood
- Asymptotically minimax adaptive estimation. II: Schemes without optimal adaptation: adaptive estimators
- Estimating Coefficient Distributions in Random Coefficient Regressions
- On the best rate of adaptive estimation in some inverse problems
- A Lepskij-type stopping rule for regularized Newton methods
- On a Problem of Adaptive Estimation in Gaussian White Noise
- Discrete radon transform
- An Almost Ideal Demand System
- Inverse problems with Poisson data: statistical regularization theory, applications and algorithms
- Tests for qualitative features in the random coefficients model
- Specification testing in random coefficient models
- Rate-optimal nonparametric estimation for random coefficient regression models
- Adaptive estimation in the linear random coefficients model when regressors have limited variation
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