# Hadamard
Code implementing the methods, and reproducing the computational results, from the paper:
E. Dobriban, W. J. Su, Y. Yang, and Z. Zhang:
Robust Inference Under Heteroskedasticity via the Hadamard Estimator, https://arxiv.org/abs/1807.00347
异方差下基于Hadamard估计的稳健推断matlab实现.zip （79个子文件） 
异方差下基于Hadamard估计的稳健推断matlab实现 Hadamard-master Experiments Experiment 6 - MSE 
mean-bias-MSE-White-MW-min-n=200-p=150-n-mc=1000-design=1.png 46KB mean-bias-MSE-White-MW-min-n=200-p=100-n-mc=1000-design=1.png 48KB legend_mean-bias-MSE-White-MW-min-n=200-p=100-n-mc=1000-design=1.png 56KB legend_mean-bias-MSE-White-MW-min-n=200-p=150-n-mc=1000-design=1.png 56KB
mse_exp.m 3KB Experiment 5b - Coverage dof cover-first-n=1000-p=50-n-mc=1000-design=1-param=0.90.png 40KB cover-first-n=1000-p=500-n-mc=1000-design=1-param=0.00.png 41KB mean-cover-n=1000-p=200-n-mc=1000-design=1-param=0.90.png 49KB mean-cover-n=1000-p=50-n-mc=1000-design=1-param=0.00.png 47KB cover-first-n=1000-p=500-n-mc=1000-design=1-param=0.90.png 41KB cover-first-n=1000-p=100-n-mc=1000-design=1-param=0.00.png 40KB mean-cover-n=1000-p=900-n-mc=1000-design=1-param=0.90.png 53KB cover-first-n=1000-p=20-n-mc=1000-design=1-param=0.90.png 40KB mean-cover-n=1000-p=20-n-mc=1000-design=1-param=0.00.png 43KB mean-cover-n=1000-p=50-n-mc=1000-design=1-param=0.90.png 42KB mean-cover-n=1000-p=750-n-mc=1000-design=1-param=0.00.png 50KB mean-cover-n=1000-p=900-n-mc=1000-design=1-param=0.00.png 52KB cover-first-n=1000-p=100-n-mc=1000-design=1-param=0.90.png 40KB mean-cover-n=1000-p=100-n-mc=1000-design=1-param=0.90.png 48KB cover-first-n=1000-p=50-n-mc=1000-design=1-param=0.00.png 39KB mean-cover-n=1000-p=500-n-mc=1000-design=1-param=0.00.png 53KB mean-cover-n=1000-p=750-n-mc=1000-design=1-param=0.90.png 51KB mean-cover-n=1000-p=100-n-mc=1000-design=1-param=0.00.png 47KB cover-first-n=1000-p=200-n-mc=1000-design=1-param=0.00.png 41KB mean-cover-n=1000-p=200-n-mc=1000-design=1-param=0.00.png 47KB mean-cover-n=1000-p=20-n-mc=1000-design=1-param=0.90.png 45KB cover-first-n=1000-p=900-n-mc=1000-design=1-param=0.90.png 41KB cover-first-n=1000-p=900-n-mc=1000-design=1-param=0.00.png 40KB cover-first-n=1000-p=20-n-mc=1000-design=1-param=0.00.png 38KB cover-first-n=1000-p=750-n-mc=1000-design=1-param=0.90.png 42KB cover_exp.m 5KB mean-cover-n=1000-p=500-n-mc=1000-design=1-param=0.90.png 50KB cover-first-n=1000-p=200-n-mc=1000-design=1-param=0.90.png 41KB cover-first-n=1000-p=750-n-mc=1000-design=1-param=0.00.png 41KB Experiment(ZX) all-coordinateds-cover-n=1000-p=100-n-mc=1000-design=1.png 50KB mean-cover-n=1000-p=100-n-mc=1000-design=3.png 46KB mean-bias-MSE-White-MW-min-n=200-p=150-n-mc=1000-design=3.png 39KB all-coordinateds-cover-n=1000-p=800-n-mc=1000-design=3.png 48KB all-coordinateds-cover-n=1000-n_met=5-p=500-n-mc=1000-design=1.png 57KB all-coordinateds-cover-n=1000-p=800-n-mc=1000-design=1.png 50KB mean-bias-MSE-White-MW-min-n=200-p=150-n-mc=1000-design=1.png 46KB all-coordinateds-cover-n=1000-p=500-n-mc=1000-design=3.png 54KB mean-bias-MSE-White-MW-min-n=200-p=100-n-mc=1000-design=3.png 37KB compare4-cover-first-n=1000-n-mc=1000-design=3-plot.png 43KB all-coordinateds-cover-n=1000-n_met=5-p=100-n-mc=1000-design=1.png 55KB all-coordinateds-cover-n=1000-p=100-n-mc=1000-design=3.png 51KB mean-bias-MSE-White-MW-min-n=200-p=100-n-mc=1000-design=1.png 50KB compare3-cover-first-n=1000-n-mc=1000-design=3-plot.png 43KB all-coordinateds-cover-n=1000-n_met=5-p=100-n-mc=1000-design=3.png 57KB all-coordinateds-cover-n=1000-n_met=5-p=800-n-mc=1000-design=1.png 54KB all-coordinateds-cover-n=1000-n_met=5-p=500-n-mc=1000-design=3.png 57KB compare3-cover-second-n=1000-n-mc=1000-design=3-plot.png 45KB all-coordinateds-cover-n=1000-p=500-n-mc=1000-design=1.png 52KB cov_exp_more.m 12KB all-coordinateds-cover-n=1000-n_met=5-p=800-n-mc=1000-design=3.png 52KB Experiment(EY) compare-8methods-cover-second-n=1000-n-mc=1000-design=1-plot.png 54KB compare-jackknife-len-first-n=1000-n-mc=1000-design=3-plot.png 58KB compare4-len-second-n=1000-n-mc=1000-design=3-plot.png 53KB compare4-cover-second-n=1000-n-mc=1000-design=3-plot.png 61KB compare4-jackknife-cover-first-n=1000-n-mc=1000-design=1-plot.png 64KB compare4-jackknife-cover-second-n=1000-n-mc=1000-design=1-plot.png 65KB compare-jackknife-cover-first-n=1000-n-mc=1000-design=3-plot.png 50KB compare4-cover-first-n=1000-n-mc=1000-design=3-plot.png 50KB compare-8methods-cover-first-n=1000-n-mc=1000-design=3-plot.png 52KB compare4-jackknife-len-second-n=1000-n-mc=1000-design=3-plot.png 49KB compare-jackknife-cover-first-n=1000-n-mc=1000-design=1-plot.png 54KB compare4-len-first-n=1000-n-mc=1000-design=3-plot.png 52KB compare-8methods-cover-second-n=1000-n-mc=1000-design=3-plot.png 53KB compare4-jackknife-len-first-n=1000-n-mc=1000-design=3-plot.png 49KB compare6-cover-first-n=1000-n-mc=1000-design=3-plot.png 60KB compare6-len-first-n=1000-n-mc=1000-design=3-plot.png 64KB compare4-jackknife-cover-second-n=1000-n-mc=1000-design=3-plot.png 66KB compare4-jackknife-cover-first-n=1000-n-mc=1000-design=3-plot.png 62KB compare-8methods-cover-first-n=1000-n-mc=1000-design=1-plot.png 54KB compare_cover_dimension.m 15KB Experiment 7 - Approx normality zscore-unb-min-n=1000-p=50-n-mc=1000-design=1.png 27KB normality.m 2KB zscore-unb-min-n=1000-p=100-n-mc=1000-design=1.png 26KB README.md 252B资源评论
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