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Kernel density estimation for undirected dyadic data

Graham, Bryan S.; Niu, Fengshi; Powell, James - 2019 - This Draft: July 2019

We study nonparametric estimation of density functions for undirected dyadic random variables (i.e., random variables …, of nodes. This di?ers from the results for nonparametric estimation of densities and regres-sion functions for monadic …

Persistent link: https://www.econbiz.de/10012053034

Model selection test for the heavy-tailed distributions under censored samples with application in financial data

Panahi, Hanieh - In: International Journal of Financial Studies : open … 4 (2016) 4, pp. 1-14

Numerous heavy-tailed distributions are used for modeling financial data and in problems related to the modeling of economics processes. These distributions have higher peaks and heavier tails than normal distributions. Moreover, in some situations, we cannot observe complete information about...

Persistent link: https://www.econbiz.de/10011606719

On Maximum Likelihood Estimates for the Shape Parameter of the Generalized Pareto Distribution

Kouider, Mohammed Ridha - 2019

The general Pareto distribution (GPD) has been widely used a lot in the extreme value for example to model exceedance over a threshold. Feature of The GPD that when applied to real data sets depends substantially and clearly on the parameter estimation process. Mostly the estimation is preferred...

Persistent link: https://www.econbiz.de/10012860148

Badescu, Andrei - 2016

In this paper, we propose a copula-free approach for modeling correlated frequency distributions using an Erlang-based multivariate mixed Poisson distribution. We investigate some of the properties possessed by this class of distributions and derive a tailor-made expectation-maximization (EM)...

Persistent link: https://www.econbiz.de/10013002480

Estimation and inference of distributional partial effects : theory and application

Shen, Shu - In: Journal of business & economic statistics : JBES ; a … 37 (2019) 1, pp. 54-66

Persistent link: https://www.econbiz.de/10012176489

Nonparametric IV estimation of local average treatment effects with covariates

Frölich, Markus - 2002

Persistent link: https://www.econbiz.de/10001717623

Characterization of Multivariate Heavy-Tailed Distribution Families via Copula

Weng, Chengguo; Zhang, Yi - 2013

The multivariate regular variation (MRV) is one of the most important tools in modelling multivariate heavy-tailed phenomena. This paper characterizes the MRV distribution through the upper tail dependence index of the copula associated with them. Along with Theorem 2.3 in Li and Sun (2009), our...

Persistent link: https://www.econbiz.de/10014184978

A Method of Moments Estimator of Tail Dependence

Einmahl, John H. J.; Krajina, Andrea; Segers, Johan - 2007

in a truly multivariate setting. We consider a semiparametric model in which the stable tail dependence function is … parameter. A method of moments estimator is proposed where a certain integral of a nonparametric, rank-based estimator of the … estimator is shown to be consistent and asymptotically normal. Moreover, a comparison between the parametric and nonparametric …

Persistent link: https://www.econbiz.de/10014223096

Cumulative Distribution Functions and UPM/LPM Analysis

Viole, Fred; Nawrocki, David N. - 2018

We show that the Cumulative Distribution Function (CDF) is represented by the ratio of the lower partial moment (LPM) ratio to the distribution for the interval in question. The addition of the upper partial moment (UPM) ratio enables us to create probability density functions (PDF) for any...

Persistent link: https://www.econbiz.de/10014165528

Nonparametric Estimation of Risk-Neutral Densities

Grith, Maria; Härdle, Wolfgang; Schienle, Melanie - 2017

This chapter deals with nonparametric estimation of the risk neutral density. We present three different approaches …

Persistent link: https://www.econbiz.de/10014123485