scholarly journals On asymptotic equivalence of the NPMLE of a monotone density and a Grenander-type estimator in multi-sample biased sampling models

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Kwun Chuen Gary Chan ◽  
Hok Kan Ling ◽  
Tony Sit ◽  
Sheung Chi Phillip Yam
2018 ◽  
Vol 46 (5) ◽  
pp. 2125-2152 ◽  
Author(s):  
Kwun Chuen Gary Chan ◽  
Hok Kan Ling ◽  
Tony Sit ◽  
Sheung Chi Phillip Yam

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.


2003 ◽  
Vol 45 (6-9) ◽  
pp. 1327-1337 ◽  
Author(s):  
Sung Kyu Choi ◽  
Nam Jip Koo ◽  
Hyun Sook Ryu

2015 ◽  
Vol 112 (44) ◽  
pp. 13467-13472 ◽  
Author(s):  
Danya J. Martell ◽  
Chandra P. Joshi ◽  
Ahmed Gaballa ◽  
Ace George Santiago ◽  
Tai-Yen Chen ◽  
...  

Metalloregulators respond to metal ions to regulate transcription of metal homeostasis genes. MerR-family metalloregulators act on σ70-dependent suboptimal promoters and operate via a unique DNA distortion mechanism in which both the apo and holo forms of the regulators bind tightly to their operator sequence, distorting DNA structure and leading to transcription repression or activation, respectively. It remains unclear how these metalloregulator−DNA interactions are coupled dynamically to RNA polymerase (RNAP) interactions with DNA for transcription regulation. Using single-molecule FRET, we study how the copper efflux regulator (CueR)—a Cu+-responsive MerR-family metalloregulator—modulates RNAP interactions with CueR’s cognate suboptimal promoter PcopA, and how RNAP affects CueR−PcopAinteractions. We find that RNAP can form two noninterconverting complexes at PcopAin the absence of nucleotides: a dead-end complex and an open complex, constituting a branched interaction pathway that is distinct from the linear pathway prevalent for transcription initiation at optimal promoters. Capitalizing on this branched pathway, CueR operates via a “biased sampling” instead of “dynamic equilibrium shifting” mechanism in regulating transcription initiation; it modulates RNAP’s binding–unbinding kinetics, without allowing interconversions between the dead-end and open complexes. Instead, the apo-repressor form reinforces the dominance of the dead-end complex to repress transcription, and the holo-activator form shifts the interactions toward the open complex to activate transcription. RNAP, in turn, locks CueR binding at PcopAinto its specific binding mode, likely helping amplify the differences between apo- and holo-CueR in imposing DNA structural changes. Therefore, RNAP and CueR work synergistically in regulating transcription.


2021 ◽  
Author(s):  
Wenyuan Zhang ◽  
Ben C. Sheldon ◽  
Richard Grenyer ◽  
Kevin J. Gaston

1997 ◽  
Vol 13 (2) ◽  
pp. 170-184 ◽  
Author(s):  
John L. Knight ◽  
Stephen E. Satchell

This paper deals with the use of the empirical cumulant generating function to consistently estimate the parameters of a distribution from data that are independent and identically distributed (i.i.d.). The technique is particularly suited to situations where the density function is unknown or unbounded in parameter space. We prove asymptotic equivalence of our technique to that of the empirical characteristic function and outline a six-step procedure for its implementation. Extensions of the approach to non-i.i.d. situations are considered along with a discussion of suitable applications and a worked example.


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