>> fig09_k_regularity_rough
Signal to Noise ratio: 5
Size of the sample: 1024=2^10
Test function: Bumps
Wavelet type: Daubechies
Wavelet filter size: 6
Number of random samples: 1000
Optimal K: 1.7
K | AMSE | STD | BIAS^2 | VARIANCE
---------------+------------+---------------+----------------+-------------
0.7 |1.244002e-004 | 1.001017e-005 | 5.908597e-002 | -5.896157e-002 |
1.7 |8.614942e-005 | 9.418655e-006 | 5.908597e-002 | -5.899982e-002 |
2.7 |1.319257e-004 | 1.616230e-005 | 5.908597e-002 | -5.895405e-002 |
3.7 |1.809139e-004 | 2.221813e-005 | 5.908597e-002 | -5.890506e-002 |
4.7 |2.305649e-004 | 2.776607e-005 | 5.908597e-002 | -5.885541e-002 |
5.7 |2.761304e-004 | 3.167392e-005 | 5.908597e-002 | -5.880984e-002 |
Signal to Noise ratio: 5
Size of the sample: 1024=2^10
Test function: Blocks
Wavelet type: Haar
Wavelet filter size: Number of random samples: 1000
Optimal K: 1.7
K | AMSE | STD | BIAS^2 | VARIANCE
---------------+------------+---------------+----------------+-------------
0.7 |3.131387e-004 | 2.726948e-005 | 2.020339e-001 | -2.017208e-001 |
1.7 |8.153701e-005 | 1.501343e-005 | 2.020339e-001 | -2.019524e-001 |
2.7 |1.279842e-004 | 2.634187e-005 | 2.020339e-001 | -2.019059e-001 |
3.7 |1.970668e-004 | 4.018278e-005 | 2.020339e-001 | -2.018369e-001 |
4.7 |2.780693e-004 | 5.238954e-005 | 2.020339e-001 | -2.017559e-001 |
5.7 |3.599510e-004 | 6.211565e-005 | 2.020339e-001 | -2.016740e-001 |
Signal to Noise ratio: 5
Size of the sample: 1024=2^10
Test function: HeaviSine
Wavelet type: Symmlet
Wavelet filter size: 8
Number of random samples: 1000
Optimal K: 1.7
K | AMSE | STD | BIAS^2 | VARIANCE
---------------+------------+---------------+----------------+-------------
0.7 |7.839154e-004 | 7.032204e-005 | 2.670418e-001 | -2.662579e-001 |
1.7 |1.367115e-004 | 2.376863e-005 | 2.670418e-001 | -2.669051e-001 |
2.7 |1.612943e-004 | 3.105922e-005 | 2.670418e-001 | -2.668805e-001 |
3.7 |2.007394e-004 | 3.430061e-005 | 2.670418e-001 | -2.668411e-001 |
4.7 |2.362454e-004 | 3.783947e-005 | 2.670418e-001 | -2.668056e-001 |
5.7 |2.619174e-004 | 4.037459e-005 | 2.670418e-001 | -2.667799e-001 |
Signal to Noise ratio: 5
Size of the sample: 1024=2^10
Test function: Doppler
Wavelet type: Symmlet
Wavelet filter size: 8
Number of random samples: 1000
Optimal K: 1.7
K | AMSE | STD | BIAS^2 | VARIANCE
---------------+------------+---------------+----------------+-------------
0.7 |6.219376e-004 | 5.390153e-005 | 3.099309e-001 | -3.093090e-001 |
1.7 |1.923584e-004 | 2.696067e-005 | 3.099309e-001 | -3.097386e-001 |
2.7 |2.583551e-004 | 3.704261e-005 | 3.099309e-001 | -3.096726e-001 |
3.7 |3.362808e-004 | 4.967519e-005 | 3.099309e-001 | -3.095946e-001 |
4.7 |4.104539e-004 | 5.788325e-005 | 3.099309e-001 | -3.095205e-001 |
5.7 |4.752816e-004 | 6.057336e-005 | 3.099309e-001 | -3.094556e-001 |
>> fig07_k_regularity_rough
Signal to Noise ratio: 5
Size of the sample: 1024=2^10
Test function: Bumps
Wavelet type: Daubechies
Wavelet filter size: 6
Number of random samples: 1000
Optimal K: 1.7
K | AMSE | STD | BIAS^2 | VARIANCE
---------------+------------+---------------+----------------+-------------
0.7 |1.244002e-004 | 1.001017e-005 | 5.908597e-002 | -5.896157e-002 |
1.7 |8.614942e-005 | 9.418655e-006 | 5.908597e-002 | -5.899982e-002 |
2.7 |1.319257e-004 | 1.616230e-005 | 5.908597e-002 | -5.895405e-002 |
3.7 |1.809139e-004 | 2.221813e-005 | 5.908597e-002 | -5.890506e-002 |
4.7 |2.305649e-004 | 2.776607e-005 | 5.908597e-002 | -5.885541e-002 |
5.7 |2.761304e-004 | 3.167392e-005 | 5.908597e-002 | -5.880984e-002 |
Signal to Noise ratio: 5
Size of the sample: 1024=2^10
Test function: Blocks
Wavelet type: Haar
Wavelet filter size: Number of random samples: 1000
Optimal K: 1.7
K | AMSE | STD | BIAS^2 | VARIANCE
---------------+------------+---------------+----------------+-------------
0.7 |3.131387e-004 | 2.726948e-005 | 2.020339e-001 | -2.017208e-001 |
1.7 |8.153701e-005 | 1.501343e-005 | 2.020339e-001 | -2.019524e-001 |
2.7 |1.279842e-004 | 2.634187e-005 | 2.020339e-001 | -2.019059e-001 |
3.7 |1.970668e-004 | 4.018278e-005 | 2.020339e-001 | -2.018369e-001 |
4.7 |2.780693e-004 | 5.238954e-005 | 2.020339e-001 | -2.017559e-001 |
5.7 |3.599510e-004 | 6.211565e-005 | 2.020339e-001 | -2.016740e-001 |
Signal to Noise ratio: 5
Size of the sample: 1024=2^10
Test function: HeaviSine
Wavelet type: Symmlet
Wavelet filter size: 8
Number of random samples: 1000
Optimal K: 1.7
K | AMSE | STD | BIAS^2 | VARIANCE
---------------+------------+---------------+----------------+-------------
0.7 |7.839154e-004 | 7.032204e-005 | 2.670418e-001 | -2.662579e-001 |
1.7 |1.367115e-004 | 2.376863e-005 | 2.670418e-001 | -2.669051e-001 |
2.7 |1.612943e-004 | 3.105922e-005 | 2.670418e-001 | -2.668805e-001 |
3.7 |2.007394e-004 | 3.430061e-005 | 2.670418e-001 | -2.668411e-001 |
4.7 |2.362454e-004 | 3.783947e-005 | 2.670418e-001 | -2.668056e-001 |
5.7 |2.619174e-004 | 4.037459e-005 | 2.670418e-001 | -2.667799e-001 |
Signal to Noise ratio: 5
Size of the sample: 1024=2^10
Test function: Doppler
Wavelet type: Symmlet
Wavelet filter size: 8
Number of random samples: 1000
Optimal K: 1.7
K | AMSE | STD | BIAS^2 | VARIANCE
---------------+------------+---------------+----------------+-------------
0.7 |6.219376e-004 | 5.390153e-005 | 3.099309e-001 | -3.093090e-001 |
1.7 |1.923584e-004 | 2.696067e-005 | 3.099309e-001 | -3.097386e-001 |
2.7 |2.583551e-004 | 3.704261e-005 | 3.099309e-001 | -3.096726e-001 |
3.7 |3.362808e-004 | 4.967519e-005 | 3.099309e-001 | -3.095946e-001 |
4.7 |4.104539e-004 | 5.788325e-005 | 3.099309e-001 | -3.095205e-001 |
5.7 |4.752816e-004 | 6.057336e-005 | 3.099309e-001 | -3.094556e-001 |
LPM.zip_Daubechies_decomposition_multi resolution
版权申诉
198 浏览量
2022-09-21
08:20:03
上传
评论
收藏 2.69MB ZIP 举报
LPM.zip （67个子文件） 
mathematica 
fig5_InverseGammapr.nb 153KB fig2_lambda.nb 347KB fig1_posterior.nb 239KB fig3_exactrisk.nb 647KB fig4_ExpPr.nb 169KB matlab MakeSignalNewb.m 4KB mysure.m 2KB recMode2_exp.m 1KB bigphi.m 68B gammarule.m 282B neighcoeff.m 933B recABE.m 2KB blockJS.m 2KB fig06_k_ssize_bump_exp.m 4KB CV.M 2KB fig06_k_ssize_bump_ig.m 4KB rechybsure.m 1KB table1_comparing_models_exp.asv 5KB simulepsmod.m 3KB recblockJS.m 2KB afm.dat 26KB simuleps.m 3KB fig09_k_regularity_rough.m 5KB output minkmat_exp.mat 497KB
table1_exp.txt 1KB fig06_output.txt 6KB fig09_output.txt 7KB table1_ig.txt 1KB table1_bs.txt 1KB minkmat_bs.mat 497KB minAMSE_exp.mat 497KB minAMSE_ig.mat 595KB minkmat_ig.mat 595KB fig07_output.txt 6KB fig10_output.txt 4KB minAMSE_bs.mat 497KB table1_comparing_models.m 4KB RECVISU.M 1KB recMode2.m 1KB table1_comparing_models_exp.m 5KB recmybams.m 1KB fig08_10_comparing_methods.m 9KB bayesrule.m 1KB ThreshWaveb.m 3KB fig06_k_ssize_bump.m 3KB simulreal.m 2KB phi.m 70B table1_comparing_models_ig.m 5KB all_simul.m 9KB figure n1024snr5AMSE_meth.eps 47KB snr5AMSE_dt_bump_exp.eps 57KB n1024snr5ALL.eps 146KB n1024snr5AMSE_dt.eps 67KB n1024snr5Noise_meth.eps 112KB snr5AMSE_dt_bump.eps 57KB snr5AMSE_dt_bump_ig.eps 56KB n1024snr5AMSE.eps 30KB n1024snr5ALL_meth.eps 33KB RECSURE.M 1KB recbams.m 965B plotrule.m 797B RECCV.M 2KB fig07_k_regularity_detail.m 4KB recMode2_ig.m 1KB recneighcoeff_s.m 1KB bamsmooth.m 2KB recfdr.m 3KB资源评论
