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1. Penalized-Likelihood-Estimation-of-GLMs Penalized-Likelihood-Estimation-of-GLMs Public

   Penalized likelihood estimation for generalized linear models (GLMs) is introduced. First, the maximum likelihood estimation of the GLM and the algorithm for solving it by Newton's method are deriv…

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2. An-Introduction-to-Double-Descent An-Introduction-to-Double-Descent Public

   Introduce the work of Belkin et al.(2019, 2020) and Nakkiran et al.(2019) on the double descent phenomenon: In machine learning models, when the dimension of parameter space grows beyond population…

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3. Solution-of-Lasso-Regression-by-Quadratic-Approxim-and-Newton-method.pdf Solution-of-Lasso-Regression-by-Quadratic-Approxim-and-Newton-method.pdf Public

   Based on the quadratic approximation (LQA) penalty terms and Newton's method for solving Lasso. MM algorithm, Optimal Subset Selection (BIC&AIC) and Adaptive Lasso are also simulated for comparison…

   R 1

4. Bayesian-MCMC-presentation Bayesian-MCMC-presentation Public

   The application of MCMC to computational Bayesian estimation is presented, with a theoretical derivation of both MCMC and Bayesian estimation, computational formulas and simulation results based on…

   R

5. Robust-Regression-Based-on-Bootstrap Robust-Regression-Based-on-Bootstrap Public

   Use several Bootstrap samples to calculate an average OLS estimation, which could reduce the effects of outliers rather than changing the loss function classically. Simulation results show that the…

6. Doubly-Debiased-Lasso-in-Partially-Linear-Model Doubly-Debiased-Lasso-in-Partially-Linear-Model Public

   This is my undergraduate dissertation. By combining Partial Linear Model and Doubly Debiased Lasso proposed by ZIJIAN GUO et al. its application is extended to nonlinear models. The new model has a…