Development of empirical and analytical fragility functions using kernel smoothing methods

2014 ◽  
Vol 44 (8) ◽  
pp. 1163-1180 ◽  
Author(s):  
Hae Young Noh ◽  
David Lallemant ◽  
Anne S. Kiremidjian
Keyword(s):  
Kernel Smoothing ◽  
Fragility Functions ◽  
Smoothing Methods

scholarly journals Yield Curve Estimation by Kernel Smoothing Methods

2000 ◽  
Author(s):  
Oliver B. Linton ◽  
Enno Mammen ◽  
Jens Perch Nielsen ◽  
Carsten Tanggaard
Keyword(s):  
Kernel Smoothing ◽  
Yield Curve ◽  
Curve Estimation ◽  
Smoothing Methods

Kernel Smoothing Methods

2008 ◽  
pp. 1-28 ◽  
Author(s):  
Trevor Hastie ◽  
Robert Tibshirani ◽  
Jerome Friedman
Keyword(s):  
Kernel Smoothing ◽  
Smoothing Methods

Center-based clustering of categorical data using kernel smoothing methods

2018 ◽  
Vol 12 (5) ◽  
pp. 1032-1034
Author(s):  
Xuanhui Yan ◽  
Lifei Chen ◽  
Gongde Guo
Keyword(s):  
Categorical Data ◽  
Kernel Smoothing ◽  
Smoothing Methods

Nonparametric Methods for α-Mixing Functional Random Variables

2018 ◽  
Author(s):  
Laurent Delsol
Keyword(s):  
Kernel Smoothing ◽  
Asymptotic Properties ◽  
Strong Mixing ◽  
Kernel Estimators ◽  
Mixing Conditions ◽  
Exponential Inequalities ◽  
Smoothing Methods ◽  
Mixing Coefficients ◽  
Mixing Sequences ◽  
Prediction Problems

This article considers how functional kernel methods can be used to study α-mixing datasets. It first provides an overview of how prediction problems involving dependent functional datasets may arise from the study of time series, focusing on the standard discretized model and modelization that takes into account the functional nature of the evolution of the quantity to be studied over time. It then considers strong mixing conditions, with emphasis on the notion of α-mixing coefficients and α-mixing variables introduced by Rosenblatt (1956). It also describes some conditions for a Markov chain to be α-mixing; some useful tools that provide covariance inequalities, exponential inequalities, and Central Limit Theorem (CLT) for α-mixing sequences; the asymptotic properties of functional kernel estimators; the use of kernel smoothing methods with α-mixing datasets; and various functional kernel estimators corresponding to different prediction methods. Finally, the article highlights some interesting prospects for further research.

scholarly journals Yield curve estimation by kernel smoothing methods

2001 ◽  
Vol 105 (1) ◽  
pp. 185-223 ◽  
Author(s):  
Oliver Linton ◽  
Enno Mammen ◽  
Jans Perch Nielsen ◽  
Carsten Tanggaard
Keyword(s):  
Kernel Smoothing ◽  
Yield Curve ◽  
Curve Estimation ◽  
Smoothing Methods
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