Lmst

'Integral Probability Metrics Meet Neural Networks: The Radon-Kolmogorov-Smirnov Test', by Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani.

http://jmlr.org/papers/v26/24-0245.html

#nonparametric #distributions #kolmogorov

'Learning causal graphs via nonlinear sufficient dimension reduction', by Eftychia Solea, Bing Li, Kyongwon Kim.

http://jmlr.org/papers/v26/24-0048.html

#causal #nonparametric #observational

'From Sparse to Dense Functional Data in High Dimensions: Revisiting Phase Transitions from a Non-Asymptotic Perspective', by Shaojun Guo, Dong Li, Xinghao Qiao, Yizhu Wang.

http://jmlr.org/papers/v26/23-1578.html

#sparse #nonparametric #smoothing

'Deep Nonparametric Quantile Regression under Covariate Shift', by Xingdong Feng, Xin He, Yuling Jiao, Lican Kang, Caixing Wang.

http://jmlr.org/papers/v25/24-0906.html

#quantile #nonparametric #reweighted

We'd like to benchmark our #rust DPMM sampler against some alternatives. What's the fastest you know of? #nonparametric #bayes #MCMC

'On the Optimality of Gaussian Kernel Based Nonparametric Tests against Smooth Alternatives', by Tong Li, Ming Yuan.

http://jmlr.org/papers/v25/20-1228.html

#nonparametric #gaussian #kernels

#statstab #214 Two-sample Mann–Whitney U Test

Thoughts: Less about the non-parametric test and more about this awesome site. It covers the test hypothesis , checking assumptions, AND full reporting, with plots and effects!

#r #tutorial #nonparametric

https://rcompanion.org/handbook/F_04.html

#statstab #210 Effect Sizes for ANOVAs {effectsize}

Thoughts: ANOVAs are rarely what ppl want to report, but if it is then report an effect size! Just mind the % for the CIs 😉

#ANOVA #effectsize #APA #reporting #nonparametric #eta2 #ordinal

https://easystats.github.io/effectsize/articles/anovaES.html

'Distribution Learning via Neural Differential Equations: A Nonparametric Statistical Perspective', by Youssef Marzouk, Zhi (Robert) Ren, Sven Wang, Jakob Zech.

http://jmlr.org/papers/v25/23-1280.html

#distributions #nonparametric #entropy

#statstab #157 Effect size measures in a two-independent-samples case with nonnormal and nonhomogeneous data

Thoughts: You can never have enough (confusing) effect size measure. At least make them appropriate for your data.

#effectsize #statistics #nonparametric #es #estimation

https://link.springer.com/article/10.3758/s13428-015-0667-z

'Optimal Locally Private Nonparametric Classification with Public Data', by Yuheng Ma, Hanfang Yang.

http://jmlr.org/papers/v25/23-1563.html

#private #classification #nonparametric

'Nonparametric Regression Using Over-parameterized Shallow ReLU Neural Networks', by Yunfei Yang, Ding-Xuan Zhou.

http://jmlr.org/papers/v25/23-0918.html

#nonparametric #estimators #minimax

'More Efficient Estimation of Multivariate Additive Models Based on Tensor Decomposition and Penalization', by Xu Liu, Heng Lian, Jian Huang.

http://jmlr.org/papers/v25/22-0578.html

#multivariate #tensor #nonparametric

#statstab #89 Estimating Monotonic Effects with {brms}

Thoughts: Super simple syntax change to ensure your predictors are treated a monotonic instead of linear; just as mo()

#brms #nonlinear #monitonic #rstats #nonparametric #statistics
https://cran.r-project.org/web/packages/brms/vignettes/brms_monotonic.html

'Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks', by Guohao Shen, Yuling Jiao, Yuanyuan Lin, Joel L. Horowitz, Jian Huang.

http://jmlr.org/papers/v25/22-0488.html

#quantile #nonparametric #estimation