Fitting to Future Observations
July 21, 2022
This note is for Jiang, Y., & Liu, C. (2022). Estimation of Over-parameterized Models via Fitting to Future Observations (arXiv:2206.01824). arXiv.
Machine Learning for Multi-omics Data
July 15, 2022
This note is based on Cai, Z., Poulos, R. C., Liu, J., & Zhong, Q. (2022). Machine learning for multi-omics data integration in cancer. IScience, 25(2), 103798.
Review on Multi-omics Data
July 14, 2022
This note is based on Subramanian, I., Verma, S., Kumar, S., Jere, A., & Anamika, K. (2020). Multi-omics Data Integration, Interpretation, and Its Application. Bioinformatics and Biology Insights, 14, 1177932219899051.
Monotone Multi-Layer Perceptron
July 04, 2022
This note is for monotonic Multi-Layer Perceptron Neural network, and the references are from the R package monmlp.
Test of Monotonicity by Calibrating for Linear Functions
May 11, 2022 (Update: )
Test of Monotonicity and Convexity by Splines
April 23, 2022
Test of Monotonicity by U-processes
April 23, 2022
This note is for Ghosal, S., Sen, A., & van der Vaart, A. W. (2000). Testing Monotonicity of Regression. The Annals of Statistics, 28(4), 1054–1082.
Monotonicity in Asset Returns
April 20, 2022
Test of Monotonicity
April 20, 2022
This note is for Chetverikov, D. (2019). TESTING REGRESSION MONOTONICITY IN ECONOMETRIC MODELS. Econometric Theory, 35(4), 729–776.
Big Data Paradox
April 07, 2022
Adaptive Ridge Estimate
March 30, 2022
Mixture of Location-Scale Families
March 25, 2022
This note is for Chen, J., Li, P., & Liu, G. (2020). Homogeneity testing under finite location-scale mixtures. Canadian Journal of Statistics, 48(4), 670–684.
Scale Mixture Models
March 25, 2022
This note is for scale mixture models.
Prediction Risk for the Horseshoe Regression
March 24, 2022
The note is for Bhadra, A., Datta, J., Li, Y., Polson, N. G., & Willard, B. (2019). Prediction Risk for the Horseshoe Regression. 39.
Estimation of Location and Scale Parameters of Continuous Density
March 22, 2022 (Update: )
This note is for Pitman, E. J. G. (1939). The Estimation of the Location and Scale Parameters of a Continuous Population of any Given Form. Biometrika, 30(3/4), 391–421. and Kagan, AM & Rukhin, AL. (1967). On the estimation of a scale parameter. Theory of Probability \& Its Applications, 12, 672–678.
Equivariance
March 22, 2022
This post is for Chapter 3 of Lehmann, E. L., & Casella, G. (1998). Theory of point estimation (2nd ed). Springer.
Applications with Scale Parameters
March 22, 2022
This note contains several papers related to scale parameter.
Leave-one-out CV for Lasso
March 14, 2022
This note is for Homrighausen, D., & McDonald, D. J. (2013). Leave-one-out cross-validation is risk consistent for lasso. ArXiv:1206.6128 [Math, Stat].
Neuronized Priors for Bayesian Sparse Linear Regression
January 16, 2022
This note is for Shin, M., & Liu, J. S. (2021). Neuronized Priors for Bayesian Sparse Linear Regression. Journal of the American Statistical Association, 1–16.
Empirical Bayes
January 16, 2022
This note is based on Sec. 4.6 of Lehmann, E. L., & Casella, G. (1998). Theory of point estimation (2nd ed). Springer.
Magnetic Field Orientations in Star Formation
January 12, 2022
Generalizing Ridge Regression
December 14, 2021
This note is for Chapter 3 of van Wieringen, W. N. (2021). Lecture notes on ridge regression. ArXiv:1509.09169 [Stat].
Gaussian Processes for Regression
December 13, 2021
This note is for Chapter 4 of Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.
Additive Model with Linear Smoother
December 07, 2021
This note is for Buja, A., Hastie, T., & Tibshirani, R. (1989). Linear Smoothers and Additive Models. The Annals of Statistics, 17(2), 453–510. JSTOR.
Asymptotics of Cross Validation
December 03, 2021
This note is for Austern, M., & Zhou, W. (2020). Asymptotics of Cross-Validation. ArXiv:2001.11111 [Math, Stat].
Review on Random Matrix Theory
December 01, 2021
This note is for Paul, D., & Aue, A. (2014). Random matrix theory in statistics: A review. Journal of Statistical Planning and Inference, 150, 1–29.
Probabilistic Principal Curves
November 22, 2021
This note is for Chang, K.-Y., & Ghosh, J. (2001). A unified model for probabilistic principal surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(1), 22–41., but only involves the principal curves.
Regularization-Free Principal Curves
November 21, 2021
The note is for Gerber, S., & Whitaker, R. (2013). Regularization-Free Principal Curve Estimation. 18.
Invariant Risk Minimization
November 19, 2021 0 Comments
This note is for Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2020). Invariant Risk Minimization. ArXiv:1907.02893 [Cs, Stat].
Causal Inference by Invariant Prediction
November 19, 2021 0 Comments
Infinite Relational Model
November 18, 2021 (Update: ) 0 Comments
This note is based on Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., & Ueda, N. (n.d.). Learning Systems of Concepts with an Infinite Relational Model. 8. and Saad, F. A., & Mansinghka, V. K. (2021). Hierarchical Infinite Relational Model. ArXiv:2108.07208 [Cs, Stat].
Multidimensional Monotone Bayesian Additive Regression Tree
November 17, 2021 0 Comments
This note is for Chipman, H. A., George, E. I., McCulloch, R. E., & Shively, T. S. (2021). mBART: Multidimensional Monotone BART. ArXiv:1612.01619 [Stat].
Debiased ML via NN for GLM
November 16, 2021 0 Comments
Biclustering on Gene Expression Data
November 10, 2021 0 Comments
The note is based on Padilha, V. A., & Campello, R. J. G. B. (2017). A systematic comparative evaluation of biclustering techniques. BMC Bioinformatics, 18(1), 55.
Asymptotic Properties of High-Dimensional Random Forests
November 09, 2021 0 Comments
Bayesian Leave-One-Out Cross Validation
October 20, 2021 0 Comments
Benign Overfitting in Linear Regression
October 11, 2021 0 Comments
This note is for Bartlett, P. L., Long, P. M., Lugosi, G., & Tsigler, A. (2020). Benign Overfitting in Linear Regression. ArXiv:1906.11300 [Cs, Math, Stat].
Multiple Descent of Minimum-Norm Interpolants
October 11, 2021 0 Comments
Exploring DNN via Layer-Peeled Model
September 25, 2021 0 Comments
Conformal Inference
September 22, 2021 0 Comments
The note is based on Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018). Distribution-Free Predictive Inference for Regression. Journal of the American Statistical Association, 113(523), 1094–1111. and Tibshirani, R. J., Candès, E. J., Barber, R. F., & Ramdas, A. (2019). Conformal Prediction Under Covariate Shift. Proceedings of the 33rd International Conference on Neural Information Processing Systems, 2530–2540.
Generative Bootstrap/Multi-purpose Samplers
September 22, 2021 (Update: )
This post is based on the first version of Shin, M., Wang, L., & Liu, J. S. (2020). Scalable Uncertainty Quantification via GenerativeBootstrap Sampler., which is lately updated as Shin, M., Wang, S., & Liu, J. S. (2022). Generative Multiple-purpose Sampler for Weighted M-estimation (arXiv:2006.00767; Version 2). arXiv.
Bayesian Sparse Multiple Regression
September 16, 2021 0 Comments
Cross-Validation for High-Dimensional Ridge and Lasso
September 16, 2021 (Update: ) 0 Comments
This note collects several references on the research of cross-validation.
Local Tracklets Filtering and Global Tracklets Association
July 05, 2021 (Update: ) 0 Comments
Multiple Object Tracking via Minimizing Energy
July 05, 2021 0 Comments
Knowledge Graph and Electronic Medical Records
June 29, 2021 0 Comments
This note covers several papers on Knowledge Graph and Electronic Medical Records.
Self-organized Maps of Document Collections
June 12, 2021 0 Comments
This note is for Kaski, S., Honkela, T., Lagus, K., & Kohonen, T. (1998). WEBSOM – Self-organizing maps of document collections