link 📈🤖
Nonparametric Inference on Unlabeled Histograms (Ma, Yang) Statistical inference on histograms and frequency counts plays a central role in categorical data analysis. Moving beyond classical methods that directly analyze labeled frequencies, we introduce a framework that models the multis
Nonparametric Inference on Unlabeled Histograms (Ma, Yang) Statistical inference on histograms and frequency counts plays a central role in categorical data analysis. Moving beyond classical methods that directly analyze labeled frequencies, we introduce a framework that models the multis
November 10, 2025 at 4:51 PM
link 📈🤖
Nonparametric Inference on Unlabeled Histograms (Ma, Yang) Statistical inference on histograms and frequency counts plays a central role in categorical data analysis. Moving beyond classical methods that directly analyze labeled frequencies, we introduce a framework that models the multis
Nonparametric Inference on Unlabeled Histograms (Ma, Yang) Statistical inference on histograms and frequency counts plays a central role in categorical data analysis. Moving beyond classical methods that directly analyze labeled frequencies, we introduce a framework that models the multis
A New Approach to the Nonparametric Behrens–Fisher Problem With Compatible Confidence Intervals. Stephen Schüürhuis, Frank Konietschke, Edgar Brunner. Biometrical Journal. onlinelibrary.wiley.com/doi/10.1002/...
onlinelibrary.wiley.com
November 10, 2025 at 1:53 PM
A New Approach to the Nonparametric Behrens–Fisher Problem With Compatible Confidence Intervals. Stephen Schüürhuis, Frank Konietschke, Edgar Brunner. Biometrical Journal. onlinelibrary.wiley.com/doi/10.1002/...
Yun Ma, Pengkun Yang: Nonparametric Inference on Unlabeled Histograms https://arxiv.org/abs/2511.05077 https://arxiv.org/pdf/2511.05077 https://arxiv.org/html/2511.05077
November 10, 2025 at 6:41 AM
Yun Ma, Pengkun Yang: Nonparametric Inference on Unlabeled Histograms https://arxiv.org/abs/2511.05077 https://arxiv.org/pdf/2511.05077 https://arxiv.org/html/2511.05077
November 10, 2025 at 5:10 AM
If you are wondering about their identification strategy.
Seems like they are controling for data collection site and years of education (and maybe also BMI). Seems a bit bold to assume that these were all existing confounders between soft drink consumption and depression and...the microbiome.
Seems like they are controling for data collection site and years of education (and maybe also BMI). Seems a bit bold to assume that these were all existing confounders between soft drink consumption and depression and...the microbiome.
November 9, 2025 at 7:22 AM
If you are wondering about their identification strategy.
Seems like they are controling for data collection site and years of education (and maybe also BMI). Seems a bit bold to assume that these were all existing confounders between soft drink consumption and depression and...the microbiome.
Seems like they are controling for data collection site and years of education (and maybe also BMI). Seems a bit bold to assume that these were all existing confounders between soft drink consumption and depression and...the microbiome.
link 📈🤖
Quantile Fourier Transform, Quantile Series, and Nonparametric Estimation of Quantile Spectra (Li) A nonparametric method is proposed for estimating the quantile spectra and cross-spectra introduced in Li (2012; 2014) as bivariate functions of frequency and quantile level. The method is b
Quantile Fourier Transform, Quantile Series, and Nonparametric Estimation of Quantile Spectra (Li) A nonparametric method is proposed for estimating the quantile spectra and cross-spectra introduced in Li (2012; 2014) as bivariate functions of frequency and quantile level. The method is b
November 9, 2025 at 1:33 AM
link 📈🤖
Quantile Fourier Transform, Quantile Series, and Nonparametric Estimation of Quantile Spectra (Li) A nonparametric method is proposed for estimating the quantile spectra and cross-spectra introduced in Li (2012; 2014) as bivariate functions of frequency and quantile level. The method is b
Quantile Fourier Transform, Quantile Series, and Nonparametric Estimation of Quantile Spectra (Li) A nonparametric method is proposed for estimating the quantile spectra and cross-spectra introduced in Li (2012; 2014) as bivariate functions of frequency and quantile level. The method is b
The proposed nonparametric estimator, based on uniform mixtures, overcomes the limitations of traditional parametric models by handling censored data effectively. The algorithms are simple, stable, and computationally efficient, making them easily implementable.
November 8, 2025 at 3:04 AM
The proposed nonparametric estimator, based on uniform mixtures, overcomes the limitations of traditional parametric models by handling censored data effectively. The algorithms are simple, stable, and computationally efficient, making them easily implementable.
Uncover the hidden patterns in disease transmission with our innovative nonparametric approach. Unlock the secrets of the serial interval, a crucial metric for understanding infectious di...
🧵 Thread below
Full analysis: https://helixbrief.com/article/2c05ce95-32ec-4ac1-b9aa-a152d54d7121
🧵 Thread below
Full analysis: https://helixbrief.com/article/2c05ce95-32ec-4ac1-b9aa-a152d54d7121
November 8, 2025 at 3:04 AM
Uncover the hidden patterns in disease transmission with our innovative nonparametric approach. Unlock the secrets of the serial interval, a crucial metric for understanding infectious di...
🧵 Thread below
Full analysis: https://helixbrief.com/article/2c05ce95-32ec-4ac1-b9aa-a152d54d7121
🧵 Thread below
Full analysis: https://helixbrief.com/article/2c05ce95-32ec-4ac1-b9aa-a152d54d7121
Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review
by Guido W. Imbens (2004)
by Guido W. Imbens (2004)
IDEAS/RePEc link
to RePEc:tpr:restat:v:86:y:2004:i:1:p:4-29
ideas.repec.org
November 7, 2025 at 11:50 PM
Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review
by Guido W. Imbens (2004)
by Guido W. Imbens (2004)
link 📈🤖
Nonparametric Robust Comparison of Solutions under Input Uncertainty (Gonzalez-Hodar, Milz, Song) We study ranking and selection under input uncertainty in settings where additional data cannot be collected. We propose the Nonparametric Input-Output Uncertainty Comparisons (NIOU-C) proced
Nonparametric Robust Comparison of Solutions under Input Uncertainty (Gonzalez-Hodar, Milz, Song) We study ranking and selection under input uncertainty in settings where additional data cannot be collected. We propose the Nonparametric Input-Output Uncertainty Comparisons (NIOU-C) proced
November 7, 2025 at 4:23 PM
link 📈🤖
Nonparametric Robust Comparison of Solutions under Input Uncertainty (Gonzalez-Hodar, Milz, Song) We study ranking and selection under input uncertainty in settings where additional data cannot be collected. We propose the Nonparametric Input-Output Uncertainty Comparisons (NIOU-C) proced
Nonparametric Robust Comparison of Solutions under Input Uncertainty (Gonzalez-Hodar, Milz, Song) We study ranking and selection under input uncertainty in settings where additional data cannot be collected. We propose the Nonparametric Input-Output Uncertainty Comparisons (NIOU-C) proced
link 📈🤖
Nonparametric Modeling of Continuous-Time Markov Chains (Monti, Ji, Suchard) Inferring the infinitesimal rates of continuous-time Markov chains (CTMCs) is a central challenge in many scientific domains. This task is hindered by three factors: quadratic growth in the number of rates as the
Nonparametric Modeling of Continuous-Time Markov Chains (Monti, Ji, Suchard) Inferring the infinitesimal rates of continuous-time Markov chains (CTMCs) is a central challenge in many scientific domains. This task is hindered by three factors: quadratic growth in the number of rates as the
November 7, 2025 at 4:09 PM
link 📈🤖
Nonparametric Modeling of Continuous-Time Markov Chains (Monti, Ji, Suchard) Inferring the infinitesimal rates of continuous-time Markov chains (CTMCs) is a central challenge in many scientific domains. This task is hindered by three factors: quadratic growth in the number of rates as the
Nonparametric Modeling of Continuous-Time Markov Chains (Monti, Ji, Suchard) Inferring the infinitesimal rates of continuous-time Markov chains (CTMCs) is a central challenge in many scientific domains. This task is hindered by three factors: quadratic growth in the number of rates as the
🧩 Generative modeling under misspecification
I study how generative models behave when their assumptions fail, and develop frameworks that remain robust under model mismatch—from mixtures and networks to nonparametric models.
I study how generative models behave when their assumptions fail, and develop frameworks that remain robust under model mismatch—from mixtures and networks to nonparametric models.
November 7, 2025 at 2:47 PM
🧩 Generative modeling under misspecification
I study how generative models behave when their assumptions fail, and develop frameworks that remain robust under model mismatch—from mixtures and networks to nonparametric models.
I study how generative models behave when their assumptions fail, and develop frameworks that remain robust under model mismatch—from mixtures and networks to nonparametric models.
Jaime Gonzalez-Hodar, Johannes Milz, Eunhye Song: Nonparametric Robust Comparison of Solutions under Input Uncertainty https://arxiv.org/abs/2511.04457 https://arxiv.org/pdf/2511.04457 https://arxiv.org/html/2511.04457
November 7, 2025 at 6:53 AM
Jaime Gonzalez-Hodar, Johannes Milz, Eunhye Song: Nonparametric Robust Comparison of Solutions under Input Uncertainty https://arxiv.org/abs/2511.04457 https://arxiv.org/pdf/2511.04457 https://arxiv.org/html/2511.04457
Filippo Monti, Xiang Ji, Marc A. Suchard: Nonparametric Modeling of Continuous-Time Markov Chains https://arxiv.org/abs/2511.03954 https://arxiv.org/pdf/2511.03954 https://arxiv.org/html/2511.03954
November 7, 2025 at 6:53 AM
Filippo Monti, Xiang Ji, Marc A. Suchard: Nonparametric Modeling of Continuous-Time Markov Chains https://arxiv.org/abs/2511.03954 https://arxiv.org/pdf/2511.03954 https://arxiv.org/html/2511.03954
Elvis Agbenyega, Cody Quick: Nonparametric Safety Stock Dimensioning: A Data-Driven Approach for Supply Chains of Hardware OEMs https://arxiv.org/abs/2511.04616 https://arxiv.org/pdf/2511.04616 https://arxiv.org/html/2511.04616
November 7, 2025 at 6:52 AM
Elvis Agbenyega, Cody Quick: Nonparametric Safety Stock Dimensioning: A Data-Driven Approach for Supply Chains of Hardware OEMs https://arxiv.org/abs/2511.04616 https://arxiv.org/pdf/2511.04616 https://arxiv.org/html/2511.04616
Filippo Monti, Xiang Ji, Marc A. Suchard
Nonparametric Modeling of Continuous-Time Markov Chains
https://arxiv.org/abs/2511.03954
Nonparametric Modeling of Continuous-Time Markov Chains
https://arxiv.org/abs/2511.03954
November 7, 2025 at 5:36 AM
Filippo Monti, Xiang Ji, Marc A. Suchard
Nonparametric Modeling of Continuous-Time Markov Chains
https://arxiv.org/abs/2511.03954
Nonparametric Modeling of Continuous-Time Markov Chains
https://arxiv.org/abs/2511.03954
Jaime Gonzalez-Hodar, Johannes Milz, Eunhye Song
Nonparametric Robust Comparison of Solutions under Input Uncertainty
https://arxiv.org/abs/2511.04457
Nonparametric Robust Comparison of Solutions under Input Uncertainty
https://arxiv.org/abs/2511.04457
November 7, 2025 at 5:02 AM
Jaime Gonzalez-Hodar, Johannes Milz, Eunhye Song
Nonparametric Robust Comparison of Solutions under Input Uncertainty
https://arxiv.org/abs/2511.04457
Nonparametric Robust Comparison of Solutions under Input Uncertainty
https://arxiv.org/abs/2511.04457
Why Nonparametric Models Deserve a Second Look
Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
Telegram AI Digest
#ai #news
Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
Telegram AI Digest
#ai #news
Why Nonparametric Models Deserve a Second Look
Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
towardsdatascience.com
November 6, 2025 at 7:43 PM
Why Nonparametric Models Deserve a Second Look
Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
Telegram AI Digest
#ai #news
Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
Telegram AI Digest
#ai #news
My "discovery" was discussed 30 years earlier in a research note by Martin Mächler (see lowess.ps in his unpublished manuscripts folder) people.math.ethz.ch/~maechler/
November 6, 2025 at 5:57 PM
My "discovery" was discussed 30 years earlier in a research note by Martin Mächler (see lowess.ps in his unpublished manuscripts folder) people.math.ethz.ch/~maechler/
Rong Jiang, Cong Ma: The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin https://arxiv.org/abs/2511.03708 https://arxiv.org/pdf/2511.03708 https://arxiv.org/html/2511.03708
November 6, 2025 at 6:41 AM
Rong Jiang, Cong Ma: The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin https://arxiv.org/abs/2511.03708 https://arxiv.org/pdf/2511.03708 https://arxiv.org/html/2511.03708
Rong Jiang, Cong Ma
The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin
https://arxiv.org/abs/2511.03708
The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin
https://arxiv.org/abs/2511.03708
November 6, 2025 at 5:49 AM
Rong Jiang, Cong Ma
The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin
https://arxiv.org/abs/2511.03708
The Adaptivity Barrier in Batched Nonparametric Bandits: Sharp Characterization of the Price of Unknown Margin
https://arxiv.org/abs/2511.03708
Generate synthetic datasets that capture the complex relationships in your original data. Andrew Skabar's newest article demonstrates the process using nonparametric conditional distributions.
Why Nonparametric Models Deserve a Second Look | Towards Data Science
Discover how nonparametric conditional distributions unify regression, classification, and synthetic data generation—without assuming functional forms.
towardsdatascience.com
November 5, 2025 at 8:15 PM
Generate synthetic datasets that capture the complex relationships in your original data. Andrew Skabar's newest article demonstrates the process using nonparametric conditional distributions.
Black-Box Differentially Private Nonparametric Confidence Intervals Under Minimal Assumptions
Tomer Shoham, Moshe Shenfeld, Noa Velner-Harris, Katrina Ligett
http://arxiv.org/abs/2511.01303
Tomer Shoham, Moshe Shenfeld, Noa Velner-Harris, Katrina Ligett
http://arxiv.org/abs/2511.01303
November 5, 2025 at 4:54 AM
Black-Box Differentially Private Nonparametric Confidence Intervals Under Minimal Assumptions
Tomer Shoham, Moshe Shenfeld, Noa Velner-Harris, Katrina Ligett
http://arxiv.org/abs/2511.01303
Tomer Shoham, Moshe Shenfeld, Noa Velner-Harris, Katrina Ligett
http://arxiv.org/abs/2511.01303
link 📈🤖
Nonparametric Sensitivity Analysis for Unobserved Confounding with Survival Outcomes (Hu, Westling) In observational studies, the observed association between an exposure and outcome of interest may be distorted by unobserved confounding. Causal sensitivity analysis can be used to assess
Nonparametric Sensitivity Analysis for Unobserved Confounding with Survival Outcomes (Hu, Westling) In observational studies, the observed association between an exposure and outcome of interest may be distorted by unobserved confounding. Causal sensitivity analysis can be used to assess
November 4, 2025 at 4:45 PM
link 📈🤖
Nonparametric Sensitivity Analysis for Unobserved Confounding with Survival Outcomes (Hu, Westling) In observational studies, the observed association between an exposure and outcome of interest may be distorted by unobserved confounding. Causal sensitivity analysis can be used to assess
Nonparametric Sensitivity Analysis for Unobserved Confounding with Survival Outcomes (Hu, Westling) In observational studies, the observed association between an exposure and outcome of interest may be distorted by unobserved confounding. Causal sensitivity analysis can be used to assess
Veronica Marsico, Antonio Quintero-Rincon, Hadj Batatia
Epanechnikov nonparametric kernel density estimation based feature-learning in respiratory disease chest X-ray images
https://arxiv.org/abs/2511.01098
Epanechnikov nonparametric kernel density estimation based feature-learning in respiratory disease chest X-ray images
https://arxiv.org/abs/2511.01098
November 4, 2025 at 12:13 PM
Veronica Marsico, Antonio Quintero-Rincon, Hadj Batatia
Epanechnikov nonparametric kernel density estimation based feature-learning in respiratory disease chest X-ray images
https://arxiv.org/abs/2511.01098
Epanechnikov nonparametric kernel density estimation based feature-learning in respiratory disease chest X-ray images
https://arxiv.org/abs/2511.01098