Estimation of FARIMA Parameters in the Case of Negative Memory and Stable Noise

2013 ◽  
Vol 61 (11) ◽  
pp. 2825-2835 ◽  
Author(s):  
Krzysztof Burnecki ◽  
Grzegorz Sikora
Keyword(s):  
Author(s):  
Melanie K. T. Takarangi ◽  
Deryn Strange

When people are told that their negative memories are worse than other people’s, do they later remember those events differently? We asked participants to recall a recent negative memory then, 24 h later, we gave some participants feedback about the emotional impact of their event – stating it was more or less negative compared to other people’s experiences. One week later, participants recalled the event again. We predicted that if feedback affected how participants remembered their negative experiences, their ratings of the memory’s characteristics should change over time. That is, when participants are told that their negative event is extremely negative, their memories should be more vivid, recollected strongly, and remembered from a personal perspective, compared to participants in the other conditions. Our results provide support for this hypothesis. We suggest that external feedback might be a potential mechanism in the relationship between negative memories and psychological well-being.


1989 ◽  
Vol 57 (2) ◽  
pp. 351-357 ◽  
Author(s):  
Tom Pyszczynski ◽  
James C. Hamilton ◽  
Fred H. Herring ◽  
Jeff Greenberg

2016 ◽  
Vol 190 ◽  
pp. 880-888 ◽  
Author(s):  
Wenjing Yang ◽  
Qunlin Chen ◽  
Peiduo Liu ◽  
Hongsheng Cheng ◽  
Qian Cui ◽  
...  

2012 ◽  
Vol 60 (10) ◽  
pp. 2990-3000 ◽  
Author(s):  
Ahmed Mahmood ◽  
Mandar Chitre ◽  
Marc Andre Armand
Keyword(s):  

Author(s):  
Xiaolin Li ◽  
Raghunath Singh Rao ◽  
Om Narasimhan ◽  
Xing Gao
Keyword(s):  

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Riqing Chen ◽  
Jun Wang ◽  
Ruiquan Lin ◽  
Xiangning Zhao

Cognitive radio is regarded as a core technology to support wireless information systems. Spectrum sensing is one of the key steps to achieve cognitive radio technology. To address this problem in the presence of Alpha stable noise in wireless communication systems, we propose a nonparametric autocorrelation method, which takes advantages of the characteristics of signal autocorrelation and noise nonstationarity. The autocorrelated signal is distinguished from Alpha stable noise. As a result, the proposed method is immune from noise uncertainty. Simulation results show the validity of the proposed method under Alpha stable noise, for example, impulsive noise in wireless information systems.


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