Andere Kunden interessierten sich auch für
![Option Pricing with Long Memory Stochastic Volatility Models]( "Option Pricing with Long Memory Stochastic Volatility Models")
Option Pricing with Long Memory Stochastic Volatility Models44,99 €![Parametric Bootstrap for Linear Regression with Long-memory Errors]( "Parametric Bootstrap for Linear Regression with Long-memory Errors")
Parametric Bootstrap for Linear Regression with Long-memory Errors32,99 €![Estimation in the Presence of Measurement errors]( "Estimation in the Presence of Measurement errors")
Estimation in the Presence of Measurement errors32,99 €![Time Series Analysis with Long Memory in View]( "Time Series Analysis with Long Memory in View")
Time Series Analysis with Long Memory in View149,99 €![Time Series Analysis of Long Memory versus Structural Breaks]( "Time Series Analysis of Long Memory versus Structural Breaks")
Time Series Analysis of Long Memory versus Structural Breaks32,99 €![Comparison between M-estimator and OLS]( "Comparison between M-estimator and OLS")
Comparison between M-estimator and OLS26,99 €![On Additive Transformation based Markov Chain Monte Carlo]( "On Additive Transformation based Markov Chain Monte Carlo")
On Additive Transformation based Markov Chain Monte Carlo32,99 €
In this book, we analyze the effects of additive outliers and inliers on the long memory fractional parameter. Based on Monte-Carlo simulations, two different data sets have been generated for simulation. First set generates additive outliers and second set generates additive inliers. The results indicate that additive outliers and inliers affect the bias and MSE of the estimated fractional parameter. Particularly, the size of the additive outliers, the probability of occurrence of additive outliers and a drift parameter in data generating process appear to have important effects on the estimated fractional parameter taking the value of the true fractional parameter as a threshold.