Noise Outsourcing
October 10, 2019
I learnt the term Noise Outsourcing in kjytay’s blog, which is based on Teh Yee Whye’s IMS Medallion Lecture at JSM 2019.
Linear Regression with Partially Shuffled Data
October 08, 2019
This post is based on Slawski, M., Diao, G., & Ben-David, E. (2019). A Pseudo-Likelihood Approach to Linear Regression with Partially Shuffled Data. ArXiv:1910.01623 [Cs, Stat].
Sub Gaussian
October 05, 2019
This post is based on Wainwright (2019).
Kernel Ridgeless Regression Can Generalize
September 30, 2019
This note is based on Liang, T., & Rakhlin, A. (2018). Just Interpolate: Kernel “Ridgeless” Regression Can Generalize. ArXiv:1808.00387 [Cs, Math, Stat].
Optimality for Sparse Group Lasso
September 29, 2019
This note is based on Cai, T. T., Zhang, A., & Zhou, Y. (2019). Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference. ArXiv:1909.09851 [Cs, Math, Stat].
ABC for Socks
September 24, 2019 0 Comments
This post is based on Prof. Robert’s slides on JSM 2019 and an intuitive blog from Rasmus Bååth.
Multiple human tracking with RGB-D data
September 20, 2019
This note is based on the survey paper Camplani, M., Paiement, A., Mirmehdi, M., Damen, D., Hannuna, S., Burghardt, T., & Tao, L. (2016). Multiple human tracking in RGB-depth data: A survey. IET Computer Vision, 11(4), 265–285.
Functional PCA
September 20, 2019
This post is based on Ramsay, J. O., & Silverman, B. W. (2005). Functional data analysis (Second edition). New York, NY: Springer.
Exponential Twisting in Importance Sampling
September 18, 2019
This note is based on Ma, J., Du, K., & Gu, G. (2019). An efficient exponential twisting importance sampling technique for pricing financial derivatives. Communications in Statistics - Theory and Methods, 48(2), 203–219.
Feature Annealed Independent Rules
September 17, 2019 0 Comments
This note is based on Fan, J., & Fan, Y. (2008). High-dimensional classification using features annealed independence rules. The Annals of Statistics, 36(6), 2605–2637.
Sparse LDA
September 17, 2019
This note is based on Shao, J., Wang, Y., Deng, X., & Wang, S. (2011). Sparse linear discriminant analysis by thresholding for high dimensional data. The Annals of Statistics, 39(2), 1241–1265.
Basic of $B$-splines
September 09, 2019 0 Comments
This note is based on de Boor, C. (1978). A Practical Guide to Splines, Springer, New York.
Likelihood-free inference by ratio estimation
September 09, 2019 0 Comments
This note is for Thomas, O., Dutta, R., Corander, J., Kaski, S., & Gutmann, M. U. (2016). Likelihood-free inference by ratio estimation. ArXiv:1611.10242 [Stat]., and I got this paper from Xi’an’s blog.
Debiased Lasso
September 08, 2019
This post is based on Section 6.4 of Hastie, Trevor, Robert Tibshirani, and Martin Wainwright. “Statistical Learning withSparsity,” 2016, 362.
Optimal estimation of functionals of high-dimensional mean and covariance matrix
August 26, 2019
This post is based on Fan, J., Weng, H., & Zhou, Y. (2019). Optimal estimation of functionals of high-dimensional mean and covariance matrix. ArXiv:1908.07460 [Math, Stat].
Interior-point Method
August 16, 2019
Nocedal and Wright (2006) and Boyd and Vandenberghe (2004) present slightly different introduction on Interior-point method. More specifically, the former one only considers equality constraints, while the latter incorporates the inequality constraints.
Dantzig Selector
August 16, 2019
This post is based on Candes, E., & Tao, T. (2007). The Dantzig selector: Statistical estimation when $p$ is much larger than $n$. The Annals of Statistics, 35(6), 2313–2351.
Additive Bayesian Variable Selection
August 05, 2019
This post is based on Rossell, D., & Rubio, F. J. (2019). Additive Bayesian variable selection under censoring and misspecification. ArXiv:1907.13563 [Math, Stat].
Reluctant Interaction Modeling
July 23, 2019
This note is based on Yu, G., Bien, J., & Tibshirani, R. (2019). Reluctant Interaction Modeling. ArXiv:1907.08414 [Stat].
The Simplex Method
July 23, 2019
This note is based on Chapter 13 of Nocedal, J., & Wright, S. (2006). Numerical optimization. Springer Science & Business Media.
An Adaptive Algorithm for online FDR
July 21, 2019
This post is based on Ramdas, A., Zrnic, T., Wainwright, M., & Jordan, M. (2018). SAFFRON: An adaptive algorithm for online control of the false discovery rate. ArXiv:1802.09098 [Cs, Math, Stat].
High-dimensional linear mixed-effect model
July 21, 2019
This post is based on Li, S., Cai, T. T., & Li, H. (2019). Inference for high-dimensional linear mixed-effects models: A quasi-likelihood approach. ArXiv:1907.06116 [Stat].
A Optimal Control Approach for Deep Learning
July 19, 2019
This note is based on Li, Q., & Hao, S. (2018). An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. ArXiv:1803.01299 [Cs].
Rare Variant Association Testing
July 18, 2019
This note is based on
- Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., & Lin, X. (2011). Rare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association Test. American Journal of Human Genetics, 89(1), 82–93.
- Wang, M. H., Weng, H., Sun, R., Lee, J., Wu, W. K. K., Chong, K. C., & Zee, B. C.-Y. (2017). A Zoom-Focus algorithm (ZFA) to locate the optimal testing region for rare variant association tests. Bioinformatics, 33(15), 2330–2336.
Multi-estimate extraction for SMC-PHD
July 17, 2019
This post is based on Li, T., Corchado, J. M., Sun, S., & Fan, H. (2017). Multi-EAP: Extended EAP for multi-estimate extraction for SMC-PHD filter. Chinese Journal of Aeronautics, 30(1), 368–379.
SMC-PHD Filter
July 17, 2019
This post is based on Ristic, B., Clark, D., & Vo, B. (2010). Improved SMC implementation of the PHD filter. 2010 13th International Conference on Information Fusion, 1–8.
Canonical Variate Analysis
July 16, 2019
This note is based on Campbell, N. A. (1979). CANONICAL VARIATE ANALYSIS: SOME PRACTICAL ASPECTS. 243.
High Dimensional LDA
July 15, 2019
Global data association for MOT using network flows
July 10, 2019
This note is based on Li Zhang, Yuan Li, & Nevatia, R. (2008). Global data association for multi-object tracking using network flows. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1–8.