Weak convergence for random weighting estimation of smoothed quantile processes

2014 ◽  
Vol 263 ◽  
pp. 36-42 ◽  
Author(s):  
Shesheng Gao ◽  
Yongmin Zhong ◽  
Chengfan Gu ◽  
Bijan Shirinzadeh
Keyword(s):  
Weak Convergence ◽  
Random Weighting ◽  
Quantile Processes

Random Weighting Estimation for Quantile Processes and Negatively Associated Samples

2014 ◽  
Vol 43 (3) ◽  
pp. 656-662 ◽  
Author(s):  
Shesheng Gao ◽  
Yongmin Zhong ◽  
Chunmeng Sang ◽  
Bijan Shirinzadeh
Keyword(s):  
Negatively Associated ◽  
Random Weighting ◽  
Quantile Processes

Characterization of weak convergence for smoothed empirical and quantile processes under ϕ-mixing

1996 ◽  
Vol 53 (3) ◽  
pp. 285-295 ◽  
Author(s):  
H.J.A. Degenhardt ◽  
Madan L. Puri ◽  
Shan Sun ◽  
Martien C.A. van Zuijlen
Keyword(s):  
Weak Convergence ◽  
Empirical And Quantile Processes ◽  
Quantile Processes

Common Fixed Points in Modular Spaces

2018 ◽  
pp. 502 ◽  
Author(s):  
Salwa Salman Abed ◽  
Karrar Emad Abdul Sada
Keyword(s):  
Fixed Point ◽  
Weak Convergence ◽  
Fixed Point Theorems ◽  
Active Role ◽  
Common Fixed Points ◽  
Weakly Compact ◽  
Expansive Mapping ◽  
Modular Spaces ◽  
Opial’S Property ◽  
Common Fixed Point Theorems

     In this paper,there are   new considerations about the dual of a modular spaces and weak convergence. Two common fixed point theorems for a -non-expansive mapping defined on a star-shaped weakly compact subset are proved,  Here the conditions of affineness, demi-closedness and Opial's property play an active role in the proving our results.  

scholarly journals Weak convergence of stochastic integrals with respect to the state occupation measure of a Markov chain

2021 ◽  
Vol 58 (2) ◽  
pp. 372-393
Author(s):  
H. M. Jansen
Keyword(s):  
Markov Chain ◽  
Weak Convergence ◽  
Regime Switching ◽  
Sufficient Conditions ◽  
The State ◽  
Stochastic Integrals ◽  
Occupation Measure ◽  
Generator Matrix ◽  
Markov Modulated ◽  
Erlang Loss

AbstractOur aim is to find sufficient conditions for weak convergence of stochastic integrals with respect to the state occupation measure of a Markov chain. First, we study properties of the state indicator function and the state occupation measure of a Markov chain. In particular, we establish weak convergence of the state occupation measure under a scaling of the generator matrix. Then, relying on the connection between the state occupation measure and the Dynkin martingale, we provide sufficient conditions for weak convergence of stochastic integrals with respect to the state occupation measure. We apply our results to derive diffusion limits for the Markov-modulated Erlang loss model and the regime-switching Cox–Ingersoll–Ross process.

Sign in / Sign up

Export Citation Format

Share Document