scholarly journals Power Hamza Distribution with Application to Lifetime Data

2022 ◽  
Vol 10 (01) ◽  
pp. 31-48
Author(s):  
Samuel U. Enogwe ◽  
Chike H. Nwankwo ◽  
Eric U. Oti
Keyword(s):  
Lifetime Data

scholarly journals New class of Lindley distributions: properties and applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Duha Hamed ◽  
Ahmad Alzaghal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Simulation Study ◽  
Likelihood Estimation ◽  
Statistical Properties ◽  
Data Sets ◽  
Lifetime Data ◽  
Unknown Parameters ◽  
Lindley Distribution ◽  
New Class

AbstractA new generalized class of Lindley distribution is introduced in this paper. This new class is called the T-Lindley{Y} class of distributions, and it is generated by using the quantile functions of uniform, exponential, Weibull, log-logistic, logistic and Cauchy distributions. The statistical properties including the modes, moments and Shannon’s entropy are discussed. Three new generalized Lindley distributions are investigated in more details. For estimating the unknown parameters, the maximum likelihood estimation has been used and a simulation study was carried out. Lastly, the usefulness of this new proposed class in fitting lifetime data is illustrated using four different data sets. In the application section, the strength of members of the T-Lindley{Y} class in modeling both unimodal as well as bimodal data sets is presented. A member of the T-Lindley{Y} class of distributions outperformed other known distributions in modeling unimodal and bimodal lifetime data sets.

scholarly journals LIFETIME DATA AND THE MEASUREMENT OF SELECTION

Evolution ◽  
1991 ◽  
Vol 45 (2) ◽  
pp. 454-454 ◽  
Author(s):  
T. H. Clutton-Brock
Keyword(s):  
Lifetime Data

A Practical Guide to Family Studies with Lifetime Data

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Thomas H. Scheike ◽  
Klaus Kähler Holst
Keyword(s):  
Competing Risks ◽  
Survival Data ◽  
Age Of Onset ◽  
Familial Aggregation ◽  
Threshold Model ◽  
Right Censoring ◽  
Annual Review ◽  
Publication Date ◽  
Lifetime Data ◽  
Competing Risks Data

Familial aggregation refers to the fact that a particular disease may be overrepresented in some families due to genetic or environmental factors. When studying such phenomena, it is clear that one important aspect is the age of onset of the disease in question, and in addition, the data will typically be right-censored. Therefore, one must apply lifetime data methods to quantify such dependence and to separate it into different sources using polygenic modeling. Another important point is that the occurrence of a particular disease can be prevented by death—that is, competing risks—and therefore, the familial aggregation should be studied in a model that allows for both death and the occurrence of the disease. We here demonstrate how polygenic modeling can be done for both survival data and competing risks data dealing with right-censoring. The competing risks modeling that we focus on is closely related to the liability threshold model. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Extracting Metal Contact Recombination Parameters From Effective Lifetime Data

2018 ◽  
Vol 8 (6) ◽  
pp. 1413-1420 ◽  
Author(s):  
Robert Dumbrell ◽  
Mattias K. Juhl ◽  
Thorsten Trupke ◽  
Ziv Hameiri
Keyword(s):  
Metal Contact ◽  
Lifetime Data ◽  
Effective Lifetime

Positron Annihilation Lifetime Measurements in Collagen Biopolymer

1998 ◽  
Vol 530 ◽  
Author(s):  
S. Siles ◽  
G. Moya ◽  
X.H. Li ◽  
J. Kansy ◽  
P. Moser
Keyword(s):  
Positron Annihilation ◽  
Amorphous Region ◽  
Positron Annihilation Spectroscopy ◽  
Lifetime Data ◽  
Spectroscopy Technique ◽  
Lifetime Measurements ◽  
Lifetime Distributions ◽  
Positron Annihilation Lifetime ◽  
Coincidence System ◽  
Three Components

AbstractLifetime measurement in Positron Annihilation Spectroscopy (PAS) is applied to the study of free-volume collagen characteristics as a function of concentration. The lifetimes of positrons were obtained by a conventional fast-fast coincidence system. All lifetime data are fitted in three components by using the computer program POSITRON FIT and resolved. For each concentration, lifetime distributions were analyzed in order to obtain the different components, thus we have observed three components of which a long component τ3. This long lived component can be associated with a pick-off annihilation of ortho-positronium (o-Ps) trapped in free volumes of amorphous region. This investigation shows the potential of the positron annihilation spectroscopy technique in the study of biopolymer microstructures.

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