José E. Chacón | NICDA Research Group

NICDA Workshop 2026

Thu, 14 May 2026 00:00:00 +0000

On June 11, 2026 the annual workshop of the NICDA group will take place! It will be at the Leganés Campus of Universidad Carlos III de Madrid during the whole day.

The workshop is designed to be a space for discussion and collaboration among the members of the group. Both work in progress and completed projects will be presented, creating the opportunity to share ideas and feedback among group members. In particular, this workshop serves as a connection point for the ongoing project “Nonparametric Statistical Inference for Non-Euclidean, Dependent, and Censored Data” (NSINED; PID2024-158399NB-I00).

New book in the Statistics Reading Group: "Asymptotic Stochastics"

Wed, 14 Jan 2026 00:00:00 +0000

The next book to be read in the Statistics Reading Group will be Asymptotic Stochastics by Norbert Henze. The book provides a comprehension-oriented, detailed, and gentle introduction to asymptotic stochastics, covering basic, advanced, and current research topics. It does so in a particularly clear and pedagogical style that embraces self-questions, gives historical remarks, and provides numerous examples and solved exercises, thus being a useful resource for lecturers and students.

From the Springer description:

This textbook, which is based on the second edition of a book that has been previously published in German language, provides a comprehension-oriented introduction to asymptotic stochastics. It is aimed at the beginning of a master’s degree course in mathematics and covers the material that can be taught in a four-hour lecture with two-hour exercises. Individual chapters are also suitable for seminars at the end of a bachelor’s degree course.

In addition to more basic topics such as the method of moments in connection with the convergence in distribution or the multivariate central limit theorem and the delta method, the book covers limit theorems for U-statistics, the Wiener process and Donsker’s theorem, as well as the Brownian bridge, with applications to statistics. It concludes with a central limit theorem for triangular arrays of Hilbert space-valued random elements with applications to weighted $L^2$ statistics.

The book is deliberately designed for self-study. It contains 138 self-questions, which are answered at the end of each chapter, as well as 194 exercises with solutions.

As in the previous book, participants are expected to read the content before the sessions. Then, during the session, we will go page by page over the book and discuss its important results, implications, connections, unclear points, etc., documenting in a shared Overleaf document this process, as done previously. Who guides what parts is decided in-session. Sessions are expected to be held weekly.

A note on the geodesic normal distribution on the sphere

Thu, 01 Jan 2026 00:00:00 +0000

Blessing of dimensionality in cross-validated bandwidth selection on the sphere

Thu, 01 Jan 2026 00:00:00 +0000

Project "Nonparametric Statistical Inference for Non-Euclidean, Dependent, and Censored Data" (NSINED) gets funded

Mon, 01 Sep 2025 00:00:00 +0000

The project NSINED (PID2024-158399NB-I00), co-led by Eduardo García-Portugués and Andrea Meilán-Vila, has been funded through the “Proyectos de Generación de Conocimiento 2024” call. The project is developed at Universidad Carlos III de Madrid. Among the researchers within the project are José E. Chacón, Silvia Novo, Rebeca Peláez, Diego Serrano, Ewa Strzałkowska-Kominiak, Iñaki Úcar, and Thomas Verdebout.

NSINED is an ambitious project that aims to advance the field of nonparametric statistical inference in the context of complex data, such as non-Euclidean, dependent, and censored data. The project is set to run from 2025 to 2028 and is funded by the Spanish Ministry of Science, Innovation and Universities through the Spanish Agency for Research (AEI).

New book in the Statistics Reading Group: "Computer Age Statistical Inference"

Fri, 25 Jul 2025 00:00:00 +0000

The next book to be read in the Statistics Reading Group will be Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Bradley Efron and Trevor Hastie. The book delivers a masterful synthesis of classical statistical inference and modern computational methods, tracing their evolution over the past 60 years. Insightful, rigorous, and historically grounded, it is essential reading to understand the theoretical foundations and algorithmic developments shaping today’s data science.

From the Cambridge University Press description:

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. “Big data”, “data science”, and “machine learning” have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on a journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories – Bayesian, frequentist, Fisherian – individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The book integrates methodology and algorithms with statistical inference, and ends with speculation on the future direction of statistics and data science.

First NICDA Workshop

Fri, 16 May 2025 00:00:00 +0000

On June 6, 2025 the first workshop of the NICDA group will take place! It will be in room 7.3.J06 from 9:00 to 14:00, at the Leganés Campus of Universidad Carlos III de Madrid.

Mode-based estimation of the center of symmetry

Wed, 01 Jan 2025 00:00:00 +0000

Bayesian taut splines for estimating the number of modes

Mon, 01 Jan 2024 00:00:00 +0000

Estimation of density functionals via cross-validation

Mon, 01 Jan 2024 00:00:00 +0000

Bump hunting through density curvature features

Sun, 01 Jan 2023 00:00:00 +0000

Minimum adjusted Rand index for two clusterings of a given size

Sun, 01 Jan 2023 00:00:00 +0000

A new selection criterion for statistical home range estimation

Sat, 01 Jan 2022 00:00:00 +0000

Introduction to the special issue on Data Science for COVID-19

Sat, 01 Jan 2022 00:00:00 +0000

A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluation

Fri, 01 Jan 2021 00:00:00 +0000

Explicit agreement extremes for a 2 x 2 table with given marginals

Fri, 01 Jan 2021 00:00:00 +0000

Statistical outline of animal home ranges: An application of set estimation

Fri, 01 Jan 2021 00:00:00 +0000

Higher order differential analysis with vectorized derivatives

Wed, 01 Jan 2020 00:00:00 +0000

Modal clustering asymptotics with applications to bandwidth selection

Wed, 01 Jan 2020 00:00:00 +0000

The modal age of Statistics

Wed, 01 Jan 2020 00:00:00 +0000

Mixture model modal clustering

Tue, 01 Jan 2019 00:00:00 +0000

Multivariate Kernel Smoothing and its Applications

Mon, 01 Jan 2018 00:00:00 +0000

A population background for nonparametric density-based clustering

Thu, 01 Jan 2015 00:00:00 +0000

Efficient recursive algorithms for functionals based on higher order derivatives of the multivariate Gaussian density

Thu, 01 Jan 2015 00:00:00 +0000

A comparison of bandwidth selectors for mean shift clustering

Wed, 01 Jan 2014 00:00:00 +0000

Fourier methods for smooth distribution function estimation

Wed, 01 Jan 2014 00:00:00 +0000

Data-based choice of the number of pilot stages for plug-in bandwidth selection

Tue, 01 Jan 2013 00:00:00 +0000

Data-driven density derivative estimation, with applications to nonparametric clustering and bump hunting

Tue, 01 Jan 2013 00:00:00 +0000

Exact and asymptotically optimal bandwidths for kernel estimation of density functionals

Sun, 01 Jan 2012 00:00:00 +0000

Asymptotics for general multivariate kernel density derivative estimators

Sat, 01 Jan 2011 00:00:00 +0000

Gaussian kernels for density estimation with compositional data

Sat, 01 Jan 2011 00:00:00 +0000

Unconstrained pilot selectors for smoothed cross-validation

Sat, 01 Jan 2011 00:00:00 +0000

A note on the universal consistency of the kernel distribution function estimator

Fri, 01 Jan 2010 00:00:00 +0000

Multivariate plug-in bandwidth selection with unconstrained pilot bandwidth matrices

Fri, 01 Jan 2010 00:00:00 +0000

Data-driven choice of the smoothing parametrization for kernel density estimators

Thu, 01 Jan 2009 00:00:00 +0000

Is it possible to test if a power of the density is integrable?

Thu, 01 Jan 2009 00:00:00 +0000

Partial sufficiency and density estimation

Thu, 01 Jan 2009 00:00:00 +0000

Bootstrap bandwidth selection using an $h$-dependent pilot bandwidth

Tue, 01 Jan 2008 00:00:00 +0000

A note on kernel density estimation at a parametric rate

Mon, 01 Jan 2007 00:00:00 +0000

On the existence and limit behavior of the optimal bandwidth for kernel density estimation

Mon, 01 Jan 2007 00:00:00 +0000

On the use of Bayes factor in frequentist testing of a precise hypothesis

Mon, 01 Jan 2007 00:00:00 +0000

Stability under products of sufficient, minimal sufficient and complete $\sigma$-fields in the Bayesian case

Mon, 01 Jan 2007 00:00:00 +0000

A note on minimal sufficiency

Sun, 01 Jan 2006 00:00:00 +0000

On the ${L}_1$-consistency of wavelet density estimates

Sat, 01 Jan 2005 00:00:00 +0000

Two statistical experiments for bootstrapping

Thu, 01 Jan 2004 00:00:00 +0000