Tootfinder

Multinomial probit model based on joint quantile regression
Masaaki Okabe, Koki Matsuoka, Jun Tsuchida, Hiroshi Yadohisa
https://arxiv.org/abs/2508.13556 https://

Multinomial probit model based on joint quantile regression
The multinomial probit model is a typical statistical model for multiple-choice data applied in many research areas. When we are interested in some quantiles of relative utilities for understanding the distribution of these utilities, the multinomial probit model is unsuitable because we only interpret the expectation of relative utilities based on it. We thus propose quantile regression analysis methods for multinomial choice data based on joint quantile regression and multinomial probit model…

Robust Topology and the Hausdorff-Smyth Monad on Metric Spaces over Continuous Quantales
Francesco Dagnino, Amin Farjudian Eugenio Moggi
https://arxiv.org/abs/2508.11623 https:/…

Robust Topology and the Hausdorff-Smyth Monad on Metric Spaces over Continuous Quantales
We define a (preorder-enriched) category $\mathsf{Met}$ of quantale-valued metric spaces and uniformly continuous maps, with the essential requirement that the quantales are continuous. For each object $(X,d,Q)$ in this category, where $X$ is the carrier set, $Q$ is a continuous quantale, and $d: X \times X \to Q$ is the metric, we consider a topology $τ_d$ on $X$, which generalizes the open ball topology, and a topology $τ_{d,R}$ on the powerset $\mathsf{P}(X)$, called the robust topology, w…

From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
Alessandro Brusaferri, Danial Ramin, Andrea Ballarino
https://arxiv.org/abs/2509.14113

From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
While neural networks are achieving high predictive accuracy in multi-horizon probabilistic forecasting, understanding the underlying mechanisms that lead to feature-conditioned outputs remains a significant challenge for forecasters. In this work, we take a further step toward addressing this critical issue by introducing the Quantile Neural Basis Model, which incorporates the interpretability principles of Quantile Generalized Additive Models into an end-to-end neural network training framewo…

Monotone tail functions: definitions, properties, and application to risk-reducing strategies
Hamza Hanbali, Daniel Linders
https://arxiv.org/abs/2508.12608 https://

Monotone tail functions: definitions, properties, and application to risk-reducing strategies
This paper studies properties of functions having monotone tails. We extend Theorem 1 of Dhaene et al. (2002a) and show how the tail quantiles of a random variable transformed with a monotone tail function can be expressed as the transformed tail quantiles of the original random variable. The main result is intuitive, in that Dhaene et al. (2002a) properties still hold, but only for certain quantile values. However, the proof presents some complications that arise especially when the function i…

Piquant$\varepsilon$: Private Quantile Estimation in the Two-Server Model
Hannah Keller, Jacob Imola, Fabrizio Boninsegna, Rasmus Pagh, Amrita Roy Chowdhury
https://arxiv.org/abs/2509.14035

Piquant$\varepsilon$: Private Quantile Estimation in the Two-Server Model
Quantiles are key in distributed analytics, but computing them over sensitive data risks privacy. Local differential privacy (LDP) offers strong protection but lower accuracy than central DP, which assumes a trusted aggregator. Secure multi-party computation (MPC) can bridge this gap, but generic MPC solutions face scalability challenges due to large domains, complex secure operations, and multi-round interactions. We present Piquant$\varepsilon$, a system for privacy-preserving estimation of…

ColdPress: Efficient Quantile-Based Compression of Photometric Redshift PDFs
Antonio Hern\'an-Caballero
https://arxiv.org/abs/2507.12481 https://

ColdPress: Efficient Quantile-Based Compression of Photometric Redshift PDFs
ColdPress is a Python module that compresses photometric redshift probability distribution functions (PDFs) by encoding quantiles of their cumulative distribution. For a fixed packet size (the default is 80 bytes per PDF), ColdPress attains a reconstruction accuracy comparable to the sparse-basis representation method implemented in the pdf_storage module of Carrasco-Kind & Brunner (2014), yet reduces the computational cost by a factor of ~7000. I describe the implementation and quantify its co…

A sequential classification learning for estimating quantile optimal treatment regimes
Junwen Xia, Jingxiao Zhang, Dehan Kong
https://arxiv.org/abs/2507.11255

A sequential classification learning for estimating quantile optimal treatment regimes
Quantile optimal treatment regimes (OTRs) aim to assign treatments that maximize a specified quantile of patients' outcomes. Compared to treatment regimes that target the mean outcomes, quantile OTRs offer fairer regimes when a lower quantile is selected, as it focuses on improving outcomes for individuals who would otherwise experience relatively poor results. In this paper, we propose a novel method for estimating quantile OTRs by reformulating the problem as a sequential classification task.…

Differentially Private Conformal Prediction via Quantile Binary Search
Ogonnaya M. Romanus, Roberto Molinari
https://arxiv.org/abs/2507.12497 https://

Differentially Private Conformal Prediction via Quantile Binary Search
Most Differentially Private (DP) approaches focus on limiting privacy leakage from learners based on the data that they are trained on, there are fewer approaches that consider leakage when procedures involve a calibration dataset which is common in uncertainty quantification methods such as Conformal Prediction (CP). Since there is a limited amount of approaches in this direction, in this work we deliver a general DP approach for CP that we call Private Conformity via Quantile Search (P-COQS).…

A novel approach to generate distributions
Subhankar Dutta, Roberto Vila, Terezinha K. A. Ribeiro
https://arxiv.org/abs/2508.11861 https://arxiv.org/pdf/25…

A novel approach to generate distributions
A novel approach to adding two additional parameters to a family of distributions for better adaptability has been put forth. This approach yields a versatile class of distributions supported on the positive real line. We proceed to analyze its mathematical characteristics, such as critical points, modality, stochastic representation, identifiability, quantiles, moments, and truncated moments. We present a new regression model for unimodal continuous data based on a submodel of the newly propos…

Statistical Model Checking Beyond Means: Quantiles, CVaR, and the DKW Inequality (extended version)
Carlos E. Budde, Arnd Hartmanns, Tobias Meggendorfer, Maximilian Weininger, Patrick Wienh\"oft
https://arxiv.org/abs/2509.11859

Statistical Model Checking Beyond Means: Quantiles, CVaR, and the DKW Inequality (extended version)
Statistical model checking (SMC) randomly samples probabilistic models to approximate quantities of interest with statistical error guarantees. It is traditionally used to estimate probabilities and expected rewards, i.e. means of different random variables on paths. In this paper, we develop methods using the Dvoretzky-Kiefer-Wolfowitz-Massart inequality (DKW) to extend SMC beyond means to compute quantities such as quantiles, conditional value-at-risk, and entropic risk. The DKW provides conf…