Kernel estimation of extropy function under length-biased sampling

2021 ◽  
pp. 109290
Author(s):  
Richu Rajesh ◽  
Rajesh G. ◽  
S.M. Sunoj
Methodology ◽  
2012 ◽  
Vol 8 (2) ◽  
pp. 71-80 ◽  
Author(s):  
Juan Botella ◽  
Manuel Suero

In Reliability Generalization (RG) meta-analyses, the importance of bearing in mind the problems of range restriction or biased sampling and their influence on reliability estimation has often been highlighted. Nevertheless, the presence of heterogeneous variances in the included studies has been diagnosed in a subjective way and has not been taken into account in later analyses. Procedures to detect the presence of a variety of sampling schemes and to manage them in the analyses are proposed. The procedures are further explained with an example, by applying them to 25 estimates of Cronbach’s alpha coefficient in the Hamilton Scale for Depression.


2021 ◽  
Vol 58 (2) ◽  
pp. 314-334
Author(s):  
Man-Wai Ho ◽  
Lancelot F. James ◽  
John W. Lau

AbstractPitman (2003), and subsequently Gnedin and Pitman (2006), showed that a large class of random partitions of the integers derived from a stable subordinator of index $\alpha\in(0,1)$ have infinite Gibbs (product) structure as a characterizing feature. The most notable case are random partitions derived from the two-parameter Poisson–Dirichlet distribution, $\textrm{PD}(\alpha,\theta)$, whose corresponding $\alpha$-diversity/local time have generalized Mittag–Leffler distributions, denoted by $\textrm{ML}(\alpha,\theta)$. Our aim in this work is to provide indications on the utility of the wider class of Gibbs partitions as it relates to a study of Riemann–Liouville fractional integrals and size-biased sampling, and in decompositions of special functions, and its potential use in the understanding of various constructions of more exotic processes. We provide characterizations of general laws associated with nested families of $\textrm{PD}(\alpha,\theta)$ mass partitions that are constructed from fragmentation operations described in Dong et al. (2014). These operations are known to be related in distribution to various constructions of discrete random trees/graphs in [n], and their scaling limits. A centerpiece of our work is results related to Mittag–Leffler functions, which play a key role in fractional calculus and are otherwise Laplace transforms of the $\textrm{ML}(\alpha,\theta)$ variables. Notably, this leads to an interpretation within the context of $\textrm{PD}(\alpha,\theta)$ laws conditioned on Poisson point process counts over intervals of scaled lengths of the $\alpha$-diversity.


Author(s):  
Lucas Böttcher ◽  
Maria R. D’Orsogna ◽  
Tom Chou

AbstractFactors such as varied definitions of mortality, uncertainty in disease prevalence, and biased sampling complicate the quantification of fatality during an epidemic. Regardless of the employed fatality measure, the infected population and the number of infection-caused deaths need to be consistently estimated for comparing mortality across regions. We combine historical and current mortality data, a statistical testing model, and an SIR epidemic model, to improve estimation of mortality. We find that the average excess death across the entire US from January 2020 until February 2021 is 9$$\%$$ % higher than the number of reported COVID-19 deaths. In some areas, such as New York City, the number of weekly deaths is about eight times higher than in previous years. Other countries such as Peru, Ecuador, Mexico, and Spain exhibit excess deaths significantly higher than their reported COVID-19 deaths. Conversely, we find statistically insignificant or even negative excess deaths for at least most of 2020 in places such as Germany, Denmark, and Norway.


Automatica ◽  
2017 ◽  
Vol 82 ◽  
pp. 324-327 ◽  
Author(s):  
Georgios Birpoutsoukis ◽  
Anna Marconato ◽  
John Lataire ◽  
Johan Schoukens

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