Search
Search Results
-
Dependence Orders
Dependence among random variables is one of the most widely studied topics in the literature with lots of applications in diverse areas. Pearson’s...
-
On approximating dependence function and its derivatives
Bivariate extreme value distributions can be used to model dependence of observations from random variables in extreme levels. There is no finite...
-
Tail-dependence, exceedance sets, and metric embeddings
There are many ways of measuring and modeling tail-dependence in random vectors: from the general framework of multivariate regular variation and the...
-
Local Dependence Biclustering
This chapter introduces local dependence biclustering (LDB; Shojima, 2021), which incorporates biclustering (Chap. 7, p. 259) and a Bayesian network...
-
Permutation test of tail dependence
We propose and analyze a permutation test of the tail dependence between two random variables whose marginal distributions are assumed to be known....
-
Different Coefficients for Studying Dependence
Through computer simulations, we research several different measures of dependence, including Pearson’s and Spearman’s correlation coefficients, the...
-
Tail Maximal Dependence in Bivariate Models: Estimation and Applications
AbstractAssessing dependence within co-movements of financial instruments has been of much interest in risk management. Typically, indices of tail...
-
Local Dependence Latent Rank Analysis
This chapter describes a local dependence latent rank analysis (LD-LRA; Shojima, 2011), which combines a latent rank analysis (LRA; Chap. 6, p. 191)...
-
Topological Data Analysis for Directed Dependence Networks of Multivariate Time Series Data
Topological data analysis (TDA) approaches are becoming increasingly popular for studying the dependence patterns in multivariate time series data....
-
Tail Dependence Functions of Two Classes of Bivariate Skew Distributions
The tail dependence function, one method of measuring the strength of extremal dependence between two or more random variables, is attracting an...
-
Long-Range Dependence in Directional Data
The necessity of extending circular methods to time series data was recognized in early papers by Wehrly and Johnson (Biometrika 66:255–256, 1980...
-
Nonparametric estimator of the tail dependence coefficient: balancing bias and variance
A theoretical expression is derived for the mean squared error of a nonparametric estimator of the tail dependence coefficient, depending on a...
-
A Note on the Dependence Structure of Hierarchical Completely Random Measures
Hierarchical models offer a principled framework to make inference and predictions on different (groups of) observations by leveraging their common...
-
-
-
Leveraging Extremal Dependence to Better Characterize the 2021 Pacific Northwest Heatwave
In late June, 2021, a devastating heatwave affected the US Pacific Northwest and western Canada, breaking numerous all-time temperature records by...
-
Detection of multiple change-points in high-dimensional panel data with cross-sectional and temporal dependence
We consider the detection of multiple change-points in a high-dimensional time series exhibiting both cross-sectional and temporal dependence....
-
Dynamic Copulas for Monotonic Dependence Change in Time Series
A particular class of dynamic bivariate copulas, monotonically increasing or decreasing, is studied for modeling dependence in a time series. As...
-
A Simple Isotropic Correlation Family in \({\mathbb R}^3\) with Long-Range Dependence and Flexible Smoothness
Most geostatistical applications use covariance functions that display short-range dependence, in part due to the wide variety and availability of...
-
