On the convergence of extreme distributions under power normalization

E. M. Nigm

doi:10.4064/am35-2-2

Rejection binders and reference points on the optoelectronic satellite images. Development of the method for grading extreme distributions

Geodesy and Cartography ◽

10.22389/0016-7126-2015-899-5-33-40 ◽

2015 ◽

Vol 899 (5) ◽

pp. 33-40

Author(s):

E.G. Voronin ◽

Keyword(s):

Satellite Images ◽

Reference Points ◽

Extreme Distributions

MAX- SEMISTABLE LAWS UNDER LINEAR AND POWER NORMALIZATION

Stability Problems for Stochastic Models ◽

10.1515/9783112319062-007 ◽

1994 ◽

pp. 61-70

Keyword(s):

Power Normalization ◽

Semistable Laws

Impact of Power Normalization in Experimental MIMO Antenna Performance Studies

IEEE Antennas and Wireless Propagation Letters ◽

10.1109/lawp.2006.890745 ◽

2007 ◽

Vol 6 ◽

pp. 43-46 ◽

Cited By ~ 6

Author(s):

Pasi Suvikunnas ◽

Jari Salo ◽

Pertti Vainikainen

Keyword(s):

Performance Studies ◽

Power Normalization ◽

Mimo Antenna ◽

Antenna Performance

Questioning MLE for the estimation of environmental extreme distributions

Ocean Engineering ◽

10.1016/j.oceaneng.2014.09.034 ◽

2014 ◽

Vol 92 ◽

pp. 44-54 ◽

Cited By ~ 5

Author(s):

Franck Mazas ◽

Philippe Garat ◽

Luc Hamm

Keyword(s):

Extreme Distributions ◽

Environmental Extreme

THEORETICAL APPROACH TO EXTREME DISTRIBUTIONS WITH LOWER AND UPPER LIMITS FOR MODELING ANNUAL MAXIMA OF EARTHQUAKE GROUND MOTIONS IN SEISMIC RISK ANALYSIS

Journal of Structural and Construction Engineering (Transactions of AIJ) ◽

10.3130/aijs.63.57_2 ◽

1998 ◽

Vol 63 (506) ◽

pp. 57-65 ◽

Cited By ~ 2

Author(s):

Kazuo DAN ◽

Jun KANDA

Keyword(s):

Risk Analysis ◽

Theoretical Approach ◽

Seismic Risk ◽

Ground Motions ◽

Seismic Risk Analysis ◽

Earthquake Ground Motions ◽

Upper Limits ◽

Extreme Distributions

Diagnostic plots for identifying max domains of attraction under power normalization

Journal of Applied Statistics ◽

10.1080/02664763.2017.1421915 ◽

2018 ◽

Vol 45 (13) ◽

pp. 2394-2410 ◽

Cited By ~ 3

Author(s):

Deepesh Bhati ◽

Sreenivasan Ravi

Keyword(s):

Power Normalization ◽

Domains Of Attraction ◽

Diagnostic Plots

Waveform-based speech recognition using hidden filter models: parameter selection and sensitivity to power normalization

IEEE Transactions on Speech and Audio Processing ◽

10.1109/89.260337 ◽

1994 ◽

Vol 2 (1) ◽

pp. 80-89 ◽

Cited By ~ 34

Author(s):

H. Sheikhzadeh ◽

L. Deng

Keyword(s):

Speech Recognition ◽

Parameter Selection ◽

Power Normalization

Predictions of the aids epidemic in the U.K.: The use of the back projection method

Philosophical Transactions of the Royal Society of London Series B Biological Sciences ◽

10.1098/rstb.1989.0077 ◽

1989 ◽

Vol 325 (1226) ◽

pp. 123-134 ◽

Cited By ~ 23

Keyword(s):

Incubation Period ◽

Projection Method ◽

Hiv Infections ◽

Back Projection ◽

Aids Epidemic ◽

Period Distribution ◽

Highly Sensitive ◽

The Future ◽

Extreme Distributions

The results of this analysis illustrate three points. First, that for predictions of AIDS cases four to five years into the future, the back projection method is largely insensitive to the assumption one makes for the incubation period distribution. The two extreme distributions considered represent the fast and slow extremes of incubation period distribution usually proposed; distributions that lie between these two give predictions within the range of predictions that the two generate. The estimated number of new HIV infections, however, is highly sensitive to the assumed incubation period distribution; prediction of AIDS cases in the long term will be similarly sensitive.

On the Extreme-Value Theory for Stationary Diffusions Under Power Normalization

Lithuanian Mathematical Journal ◽

10.1023/b:lima.0000019855.45150.05 ◽

2004 ◽

Vol 44 (1) ◽

pp. 36-46 ◽

Cited By ~ 2

Author(s):

B. Grigelionis

Keyword(s):

Extreme Value Theory ◽

Value Theory ◽

Extreme Value ◽

Power Normalization

The quasi-stationary distribution of the closed endemic sis model

Advances in Applied Probability ◽

10.2307/1428186 ◽

1996 ◽

Vol 28 (3) ◽

pp. 895-932 ◽

Cited By ~ 94

Author(s):

Ingemar Nåsell

Keyword(s):

Stationary Distribution ◽

Geometric Distribution ◽

Threshold Value ◽

Basic Reproduction Ratio ◽

Sis Model ◽

Reproduction Ratio ◽

Time To Extinction ◽

Extreme Distributions ◽

Stochastic Sis Model ◽

Quasi Stationary Distribution

The quasi-stationary distribution of the closed stochastic SIS model changes drastically as the basic reproduction ratio R0 passes the deterministic threshold value 1. Approximations are derived that describe these changes. The quasi-stationary distribution is approximated by a geometric distribution (discrete!) for R0 distinctly below 1 and by a normal distribution (continuous!) for R0 distinctly above 1. Uniformity of the approximation with respect to R0 allows one to study the transition between these two extreme distributions. We also study the time to extinction and the invasion and persistence thresholds of the model.