#nonparametric — Public Fediverse posts

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'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

'A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables', by Wei Luo, Yeying Zhu, Xuekui Zhang, Lin Lin.

http://jmlr.org/papers/v25/21-1181.html

#nonparametric #causal #estimation

'Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces', by Hao Liu, Haizhao Yang, Minshuo Chen, Tuo Zhao, Wenjing Liao.

http://jmlr.org/papers/v25/22-0719.html

#deep #estimation #nonparametric

'Nonparametric Inference under B-bits Quantization', by Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang.

http://jmlr.org/papers/v25/20-075.html

#nonparametric #spline #quantization

'Nonparametric Inference under B-bits Quantization', by Kexuan Li, Ruiqi Liu, Ganggang Xu, Zuofeng Shang.

http://jmlr.org/papers/v25/20-075.html

#nonparametric #spline #quantization

`Introduction to the Cox model`

https://www.youtube.com/watch?v=CzHt22YL_-w

#coxModel #proportionalHazardModel #davidCox #sirDavidCox #survivalAnalysis #failureAnalysis #failureTimeAnalysis #forceOfMortality #reliabilityEngineering #kaplanMeier #failureDistribution #failureTimeDistribution #failureRate #hazardFunction #hazardRate #failureRate #nonParametric #model #modeling #statistics #stats #mortality #function #mathematics #math #proportionality #regression #regressionModel #leastSquares

`Introduction to the Cox model`

https://www.youtube.com/watch?v=CzHt22YL_-w

#coxModel #proportionalHazardModel #davidCox #sirDavidCox #survivalAnalysis #failureAnalysis #failureTimeAnalysis #forceOfMortality #reliabilityEngineering #kaplanMeier #failureDistribution #failureTimeDistribution #failureRate #hazardFunction #hazardRate #failureRate #nonParametric #model #modeling #statistics #stats #mortality #function #mathematics #math #proportionality #regression #regressionModel #leastSquares

`Introduction to the Cox model`

https://www.youtube.com/watch?v=CzHt22YL_-w

#coxModel #proportionalHazardModel #davidCox #sirDavidCox #survivalAnalysis #failureAnalysis #failureTimeAnalysis #forceOfMortality #reliabilityEngineering #kaplanMeier #failureDistribution #failureTimeDistribution #failureRate #hazardFunction #hazardRate #failureRate #nonParametric #model #modeling #statistics #stats #mortality #function #mathematics #math #proportionality #regression #regressionModel #leastSquares

`Introduction to the Cox model`

https://www.youtube.com/watch?v=CzHt22YL_-w

#coxModel #proportionalHazardModel #davidCox #sirDavidCox #survivalAnalysis #failureAnalysis #failureTimeAnalysis #forceOfMortality #reliabilityEngineering #kaplanMeier #failureDistribution #failureTimeDistribution #failureRate #hazardFunction #hazardRate #failureRate #nonParametric #model #modeling #statistics #stats #mortality #function #mathematics #math #proportionality #regression #regressionModel #leastSquares

'Distributed Nonparametric Regression Imputation for Missing Response Problems with Large-scale Data', by Ruoyu Wang, Miaomiao Su, Qihua Wang.

http://jmlr.org/papers/v24/21-0673.html

#imputation #nonparametric #semiparametric

'A Permutation-Free Kernel Independence Test', by Shubhanshu Shekhar, Ilmun Kim, Aaditya Ramdas.

http://jmlr.org/papers/v24/23-0248.html

#nonparametric #distributions #tests

'Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models', by Molei Liu, Yi Zhang, Katherine P. Liao, Tianxi Cai.

http://jmlr.org/papers/v24/22-0700.html

#regression #nonparametric #models

'On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators', by Zejian Liu, Meng Li.

http://jmlr.org/papers/v24/21-1110.html

#estimation #estimators #nonparametric

'Conditional Distribution Function Estimation Using Neural Networks for Censored and Uncensored Data', by Bingqing Hu, Bin Nan.

http://jmlr.org/papers/v24/22-0657.html

#censored #uncensored #nonparametric

'Gaussian Processes with Errors in Variables: Theory and Computation', by Shuang Zhou, Debdeep Pati, Tianying Wang, Yun Yang, Raymond J. Carroll.

http://jmlr.org/papers/v24/21-1480.html

#bayesian #nonparametric #gaussian

'Convergence Rates of a Class of Multivariate Density Estimation Methods Based on Adaptive Partitioning', by Linxi Liu, Dangna Li, Wing Hung Wong.

http://jmlr.org/papers/v24/20-060.html

#densities #density #nonparametric

'Bayesian Data Selection', by Eli N. Weinstein, Jeffrey W. Miller.

http://jmlr.org/papers/v24/21-1067.html

#nonparametric #stein #nonparametrically

'Learning-augmented count-min sketches via Bayesian nonparametrics', by Emanuele Dolera, Stefano Favaro, Stefano Peluchetti.

http://jmlr.org/papers/v24/21-0096.html

#priors #nonparametrics #nonparametric

New tutorial paper describing the use of a simple #NonParametric #statistics method for analysing #RepeatedMeasures data with a focus on individual-level results https://pubs.asha.org/doi/10.1044/2022_JSLHR-22-00133

All data and #Rstats code needed to reproduce the analyses is available here https://osf.io/w32dk/

An #Rstats package which implements the method is available on CRAN https://CRAN.R-project.org/package=opa

The latest development version can be downloaded from githib https://github.com/timbeechey/opa

A little while back I wrote an #rstats package for #NonParametric analysis of #RepeatedMeasures data https://CRAN.R-project.org/package=opa using #ordinal pattern analysis.

A tutorial with #rstats code is available here: https://osf.io/w32dk/

Hello everyone out there in the fediverse!

I'm currently a Academy of Finland postdoctoral fellow at Aalto University working on probabilistic #machinelearning. Most of my interests are related to probabilistic #machinelearning methods that are flexible (#nonparametric), efficient (#tractable), and exact or come with guarantees (or a subset of thereof).

In my research, I work a lot with #JuliaLang and I'm always eager to learn new things.

Curious to see where this all goes.

#introduction