Sequential estimation of Spearman rank correlation using Hermite series estimators

Spearman Rank Correlation Coefficient

Hermite Series Estimation in Nonlinear Cointegrating Models

This paper discusses nonparametric series estimation of integrable cointegration models using Hermite functions. We establish the uniform consistency and asymptotic normality of the series estimator. The Monte Carlo simulation results show that the performance of the estimator is numerically satisfactory. We then apply the estimator to estimate the stock return predictive function. The out–of–s...

Robust Motion Segmentation Using Rank Ordering Estimators

Robust estimators have become popular tools for solving a wide range of problems in computer vision. Despite many successes in this field, there is still a need for estimators, which are suited to specific problems such as recovering structures from multi-structural data. This paper offers an alternative approach to, and some practical insights into, the implementation of well-known rank orderi...

The Spearman and Kendall rank correlation coefficients between intuitionistic fuzzy sets

This paper is a continuation of our previous work on Pearson’s coefficient r and we discuss here the concept of the Spearman correlation coefficient and the Kendall correlation coefficient between Atanassov’s intuitionistic fuzzy sets (A-IFSs, for short) to measure the degree of association between the A-IFSs when the assumption that the data distributions are normal is not valid or when data a...

Multiwindow estimators of correlation

This paper is concerned with the structure of estimators of correlation matrices and correlation sequences. We argue that reasonable estimators of the correlation matrix are quadratic in the data and nonnegative definite. We also specify the structure of the estimator when the data are modulated: a property we call modulation covariance. We state a representation theorem for estimators that hav...