Optimal spacing of the selected sample quantiles for the joint estimation of the location and scale parameters of a symmetric distribution

1998 ◽  
Vol 70 (2) ◽  
pp. 345-360 ◽  
Author(s):  
Junjiro Ogawa
Keyword(s):  
Joint Estimation ◽  
Symmetric Distribution ◽  
Scale Parameters ◽  
Optimal Spacing ◽  
Sample Quantiles

scholarly journals Recent Developments in Recursive Estimation for Time Series Models

2016 ◽  
Vol 5 (2) ◽  
pp. 59
Author(s):  
You Liang ◽  
A. Thavaneswaran ◽  
B. Abraham
Keyword(s):  
Location Parameter ◽  
Recursive Estimation ◽  
Joint Estimation ◽  
Infinite Variance ◽  
Finite Variance ◽  
Estimating Functions ◽  
Unified Approach ◽  
Scale Parameters ◽  
Combined Estimation ◽  
Recent Developments

Recently there has been a growing interest in joint estimation of the location and scale parameters using combined estimation functions. Combined estimating functions had been studied in Liang et al. (2011) for models with finite variance errors  and in Thavaneswaran et al. (2013) for models with infinite variance<br />stable errors. In this paper, first a theorem on recursive estimation based on estimating functions is extended to multi-parameter setup and it is shown that the unified approach can be used to estimate the location parameter recursively for models with finite variance/infinite variance  errors. The method is applied for the joint estimation of the location and scale parameters for regression models with ARCH errors and RCA models with GARCH errors.

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