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Concept

Homoscedasticity and heteroscedasticity

Related publications (20)

Anthony C Davison and Igor Rodionov's contribution to the Discussion of 'Estimating means of bounded random variables by betting' by Waudby-Smith and Ramdas

Anthony Christopher Davison, Igor Rodionov

We derive confidence intervals (CIs) and confidence sequences (CSs) for the classical problem of estimating a bounded mean. Our approach generalizes and improves on the celebrated Chernoff method, yielding the best closed-form "empirical-Bernstein" CSs and ...

Oxford Univ Press2024

Testing For The Rank Of A Covariance Operator

Victor Panaretos

How can we discern whether the covariance operator of a stochastic pro-cess is of reduced rank, and if so, what its precise rank is? And how can we do so at a given level of confidence? This question is central to a great deal of methods for functional dat ...

INST MATHEMATICAL STATISTICS-IMS2022

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Andrea Zanoni

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

The DESI N-body Simulation Project - II. Suppressing sample variance with fast simulations

Cheng Zhao, Yu Yu, Andrei Variu

Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches similar to 20 h(-3) Gpc(3). It is a great challenge to prepare high-resolution simulations with a much ...

OXFORD UNIV PRESS2022

Does jackknife scale really matter for accurate large-scale structure covariances?

Ginevra Favole

The jackknife method gives an internal covariance estimate for large-scale structure surveys and allows model-independent errors on cosmological parameters. Using the SDSS-III BOSS CMASS sample, we study how the jackknife size and number of resamplings imp ...

OXFORD UNIV PRESS2021

Geospatial Analysis of Sodium and Potassium Intake: A Swiss Population-Based Study

Stéphane Joost, Idris Guessous, David Nicolas De Ridder, Murielle Bochud

Inadequate sodium and potassium dietary intakes are associated with major, yet preventable, health consequences. Local public health interventions can be facilitated and informed by fine-scale geospatial analyses. In this study, we assess the existence of ...

MDPI2021

Drift Estimation of Multiscale Diffusions Based on Filtered Data

Assyr Abdulle, Andrea Zanoni, Giacomo Garegnani

We study the problem of drift estimation for two-scale continuous time series. We set ourselves in the framework of overdamped Langevin equations, for which a single-scale surrogate homogenized equation exists. In this setting, estimating the drift coeffic ...

2023

Euclid : Effects of sample covariance on the number counts of galaxy clusters

Yi Wang, Georges Meylan

Aims. We investigate the contribution of shot-noise and sample variance to uncertainties in the cosmological parameter constraints inferred from cluster number counts, in the context of the Euclid survey. ...

EDP SCIENCES S A2021

Optimal Stochastic Sensor Error Modeling based on Actual Impact on Quality of GNSS-INS Integrated Navigation

Jan Skaloud, Stéphane Guerrier, Mehran Khaghani

Proper modeling of stochastic errors in inertial sensors plays a crucial role in the achievable quality of GNSS-INS integration especially with low-cost inertial sensors. Generalized Method of Wavelet Moments (GMWM) can model the underlying process for suc ...

2019

Generative Models for Learning Robot Manipulation Skills from Humans

Ajay Kumar Tanwani

A long standing goal in artificial intelligence is to make robots seamlessly interact with humans in performing everyday manipulation skills. Learning from demonstrations or imitation learning provides a promising route to bridge this gap. In contrast to d ...

EPFL2018

A Prediction-Error Covariance Estimator for Adaptive Kalman Filtering in Step-Varying Processes: Application to Power-System State Estimation

Mario Paolone, Jean-Yves Le Boudec, Sidi-Rachid Cherkaoui, Lorenzo Zanni

In this paper, we present a new method for the estimation of the prediction-error covariances of a Kalman filter (KF), which is suitable for step-varying processes. The method uses a series of past innovations (i.e., the difference between the upcoming mea ...

Institute of Electrical and Electronics Engineers2017

The regularized tau estimator: A robust and efficient solution to ill-posed linear inverse problems

Martin Vetterli, Benjamin Bejar Haro, Marta Martinez-Camara

Linear inverse problems are ubiquitous. Often the measurements do not follow a Gaussian distribution. Additionally, a model matrix with a large condition number can complicate the problem further by making it ill-posed. In this case, the performance of pop ...

2017

How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices?

Andreas Loukas

How many samples are sufficient to guarantee that the eigenvectors and eigenvalues of the sample covariance matrix are close to those of the actual covariance matrix? For a wide family of distributions, including distributions with finite second moment and ...

2017

Distributionally Robust Control of Constrained Stochastic Systems

Daniel Kuhn

We investigate the control of constrained stochastic linear systems when faced with only limited information regarding the disturbance process, i.e. when only the first two moments of the disturbance distribution are known. We consider two types of distrib ...

Institute of Electrical and Electronics Engineers2016

Online Inference in Bayesian Non-Parametric Mixture Models under Small Variance Asymptotics

Sylvain Calinon, Ajay Kumar Tanwani

Adapting statistical learning models online with large scale streaming data is a challenging problem. Bayesian non-parametric mixture models provide flexibility in model selection, however, their widespread use is limited by the computational overhead of e ...

2016

Indicators of Evidence for Bioequivalence

Stephan Morgenthaler, Robert Staudte

Some equivalence tests are based on two one-sided tests, where in many applications the test statistics are approximately normal. We define and find evidence for equivalence in Z-tests and then one-and two-sample binomial tests as well as for t-tests. Mult ...

Mdpi Ag2016

Forecasting Uncertainty in Electricity Demand

Tri Kurniawan Wijaya

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and lepto ...

2015

Reference-Free Automated Magnetic Sensor Calibration for Angle Estimation in Smart Knee Prostheses

Kamiar Aminian, Arash Arami, Julien David Rechenmann

In this work, we present a method for automated calibration of an implanted anisotropic magnetoresistive (AMR) sensor for measuring the internal-external rotation (IE) in a prosthetic knee without using any reference measurement. The measurement system con ...

Institute of Electrical and Electronics Engineers2014

Expert Judgements in Risk Analysis: a Strategy to Overcome Uncertainities

Thierry Meyer, Amela Groso, David Nicolas Plüss

There is a need for a risk analysis technique specific for academic research laboratories. Since accurate accident data, normally required for quantitative risk analysis, are not available for this environment, expert judgements are often used to describe ...

2013

Anisotropic Interpolation of Sparse Generalized Image Samples

Michaël Unser, Aurélien Bourquard

Practical image-acquisition systems are often modeled as a continuous-domain prefilter followed by an ideal sampler, where generalized samples are obtained after convolution with the impulse response of the device. In this paper, our goal is to interpolate ...

Ieee-Inst Electrical Electronics Engineers Inc2013