课程描述 Description
Content: Research results of the Instructor.
Topics include : MLE's Bias Pathology, model updated MLE and Wallace's MM method. Artificially augmented samples, shrinkage and MSE reduction.
Elegant Nonparametric Estimation of a density and Matching Estimation with convergence rates via Minimum Distance Methods.
Tukey's Poly-efficiency. More topics if time permits.
预备知识 Prerequisites
参考资料 References
0 INTRODUCTION UPDATED LEC1.pdf
1 MLE AND MM LEC1.pdf
01A FACTORS, SVM, K-MEANS, MVCS.pdf
03 YYbootstrapf2002 FIG 1.pdf
02 BACH unsupervised_learning_ML_ENS_2021.pdf
2 MLE AND MM LEC 2.pdf
05 EDI EXAMPLE 3 TO BE PRINTED NON-IDENTIFIABILITY NORMAL MIXT.pdf
04 PITMAN CLOSENESS GRAPH GBYY2012.pdf
YYGB2012.pdf
YYIEEETrInfTh2015.pdf
cluster.zip
MUMLE GMM HANSEN 2 PROOFS WITH PARTITION.pdf
MUMLE GMM HANSEN 1982 GMM 1912775.pdf
BOOTSTRAP PRELIM.pdf
YYbootstrapf2002 FIG 1.pdf YYbootstrapf2002 FIG 2.pdf YYbootstrapf2002 FIG 3.pdf BOOTSTRAP 2020 TSINGHUA.pdf YYbootstrapf2002.pdf
TUKEYS POLYEFFICIENCY 1.pdf
WELL SPREAD ARTIFICIALLY AUGMENTED SAMPLES SHRINKAGE AND MSE REDUCTION-PITMAN CLOSENESS.pdf
YYJASApaper2006.pdf
YY Polyefficiency 1991.pdf
MDE AND MATCHING.pdf
3. MDE FOR REGRESSION.pdf FABC 30 A 12pt 26 lines .pdf EDI 25.pdf
