Sequential estimation of Spearman rank correlation using Hermite series estimators

  title={Sequential estimation of Spearman rank correlation using Hermite series estimators},
  author={Michael Stephanou and Melvin M. Varughese},
  journal={J. Multivar. Anal.},

hermiter: R package for Sequential Nonparametric Estimation

This article introduces the R package hermiter which facilitates estimation of univariate and bivariate probability density functions and cumulative distribution functions along with full quantile

Analysis of the Salinity of the Vistula River Based on Patrol Monitoring and State Environmental Monitoring

Background: Secondary salinity of river water reduces the value of ecosystem services, negatively impacting the entire aquatic ecosystem and reducing the possibility of water use. In Poland,

A Brain Network Analysis-Based Double Way Deep Neural Network for Emotion Recognition

This article proposes a double way deep residual neural network combined with brain network analysis, which enables the classification of multiple emotional states and verifies the effectiveness of the proposed model in emotion recognition tasks.

Probabilistic and machine learning methods for uncertainty quantification in power outage prediction due to extreme events

Abstract. Strong hurricane winds damage power grids and cause cascading power failures. Statistical and machine learning models have been proposed to predict the extent of power disruptions due to

Automatic optimization of centrifugal pump based on adaptive single-objective algorithm and computational fluid dynamics

It is important to reduce carbon emissions caused by the energy consumption of pumps. This study used a centrifugal pump with a specific speed of 89.6 as the research object to improve pump

Environmental and socio-psychological drivers of building users’ behaviours: a case study of tertiary institutional offices in Auckland

Purpose Better identification of comfort preferences and occupant behaviour drivers is expected to improve buildings’ user-centred designs and energy operations. To understand the underline drivers

Study on Ethanol Coupling Reaction Based on Regression Algorithm and Spearman Rank Correlation Coefficient

C4 olefins are widely used. Ethanol is a clean energy for the preparation of C4 olefins. It is of great practical significance to study the preparation of C4 olefins by ethanol. However, different

Spatiotemporal characteristics and influencing factor analysis of universities’ technology transfer level in China: The perspective of innovation ecosystems

Universities are important parts of innovation ecosystems, and university technology transfer (UTT), which aims for the sustainable commercialization of sci-tech achievements, is closely related to

On the properties of hermite series based distribution function estimators

Hermite series based distribution function estimators have recently been applied in the context of sequential quantile estimation. These distribution function estimators are particularly useful

Sequential Quantiles via Hermite Series Density Estimation

Simulation studies and tests on real data reveal the Gauss-Hermite based algorithms to be competitive with a leading existing algorithm and provide a solution to online distribution function and online quantile function estimation on data streams.

A new quantile tracking algorithm using a generalized exponentially weighted average of observations

This work presents a lightweight quantile estimator using a generalized form of the Exponentially Weighted Average that outperforms legacy state-of-the-art quantile tracking estimators and achieves faster adaptivity in dynamic environments.

Influence functions of the Spearman and Kendall correlation measures

This paper formally study nonparametric correlation estimators as the Kendall and Spearman correlation by means of their influence functions and gross-error sensitivities, and concludes that both the Spearman and Kendall correlation estimator combine a bounded and smooth influence function with a high efficiency.

Novel Online Algorithms for Nonparametric Correlations with Application to Analyze Sensor Data

  • Wei Xiao
  • Computer Science
    2019 IEEE International Conference on Big Data (Big Data)
  • 2019
A novel online algorithm that can compute the nonparametric correlations 10 to 1,000 times faster than the corresponding batch algorithm, and it can compute them based either on all past observations or on fixed-size sliding windows.

High-Frequency Covariance Estimates With Noisy and Asynchronous Financial Data

This article proposes a consistent and efficient estimator of the high-frequency covariance (quadratic covariation) of two arbitrary assets, observed asynchronously with market microstructure noise.

Recursive estimation of fourth-order cumulants with application to identification

Multiplicative Update Methods for Incremental Quantile Estimation

A novel lightweight incremental quantile estimator which possesses far less complexity than the Tierney’s estimator and its extensions and is multiplicative which makes it highly suitable to handle quantile estimation in systems in which the underlying distribution varies with time.

Numerically stable, scalable formulas for parallel and online computation of higher-order multivariate central moments with arbitrary weights

This work empirically examines algorithm correctness for pairwise update formulas up to order four as well as condition number and relative error bounds for eight different central moment formulas, each up to degree six, to address the trade-offs between numerical accuracy and speed of the various algorithms.