mailitics

Category: stat.OT

  • Valid Selection among Conformal Sets

    Valid Selection among Conformal Sets arXiv:2506.20173v1 Announce Type: new Abstract: Conformal prediction offers a distribution-free framework for constructing prediction sets with coverage guarantees. In practice, multiple valid conformal prediction sets may be available, arising from different models or methodologies. However, selecting the most desirable set, such as the smallest, can invalidate the coverage guarantees. To…

  • Local Polynomial Lp-norm Regression

    Local Polynomial Lp-norm Regression arXiv:2504.18695v1 Announce Type: new Abstract: The local least squares estimator for a regression curve cannot provide optimal results when non-Gaussian noise is present. Both theoretical and empirical evidence suggests that residuals often exhibit distributional properties different from those of a normal distribution, making it worthwhile to consider estimation based on other…

  • Minimum Volume Conformal Sets for Multivariate Regression

    Minimum Volume Conformal Sets for Multivariate Regression arXiv:2503.19068v1 Announce Type: new Abstract: Conformal prediction provides a principled framework for constructing predictive sets with finite-sample validity. While much of the focus has been on univariate response variables, existing multivariate methods either impose rigid geometric assumptions or rely on flexible but computationally expensive approaches that do not…

  • Simulation of Random LR Fuzzy Intervals

    Simulation of Random LR Fuzzy Intervals arXiv:2501.10482v1 Announce Type: new Abstract: Random fuzzy variables join the modeling of the impreciseness (due to their “fuzzy part”) and randomness. Statistical samples of such objects are widely used, and their direct, numerically effective generation is therefore necessary. Usually, these samples consist of triangular or trapezoidal fuzzy numbers. In…

  • Deep Learning-based Approaches for State Space Models: A Selective Review

    Deep Learning-based Approaches for State Space Models: A Selective Review arXiv:2412.11211v1 Announce Type: new Abstract: State-space models (SSMs) offer a powerful framework for dynamical system analysis, wherein the temporal dynamics of the system are assumed to be captured through the evolution of the latent states, which govern the values of the observations. This paper provides…