Some properties of the bivariate lognormal distribution for reliability applications

2012 ◽  
Vol 28 (6) ◽  
pp. 598-606 ◽  
Author(s):  
Pushpa L. Gupta ◽  
Ramesh C. Gupta
Keyword(s):  
Lognormal Distribution ◽  
Bivariate Lognormal

scholarly journals Approximating bivariate Suzuki distribution with bivariate Lognormal distribution

2013 ◽  
Vol 2 (11) ◽  
pp. 470-477
Author(s):  
Rui Zhang ◽  
Ji-bo Wei ◽  
Hui Wang ◽  
Gao-Sheng Li
Keyword(s):  
Lognormal Distribution ◽  
Bivariate Lognormal

On the Problem of Estimation for the Bivariate Lognormal Distribution

Biometrika ◽  
1964 ◽  
Vol 51 (3/4) ◽  
pp. 522 ◽  
Author(s):  
M. D. Mostafa ◽  
M. W. Mahmoud
Keyword(s):  
Lognormal Distribution ◽  
Bivariate Lognormal

A Bayesian Approach for Determining the Relationship Between Various Elongate Mineral Particles (EMPs) Definitions

2020 ◽  
Vol 64 (9) ◽  
pp. 993-1006
Author(s):  
Yuan Shao ◽  
Richard F MacLehose ◽  
Lifeng Lin ◽  
Jooyeon Hwang ◽  
Bruce H Alexander ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Lognormal Distribution ◽  
Occupational Safety ◽  
Epidemiological Studies ◽  
Conversion Factors ◽  
Mineral Particles ◽  
Group I ◽  
Similar Exposure Groups ◽  
Exposure Metrics ◽  
Bivariate Lognormal

Abstract A variety of dimensions (lengths and widths) of elongate mineral particles (EMPs) have been proposed as being related to health effects. In this paper, we develop a mathematical approach for deriving numerical conversion factors (CFs) between these EMP exposure metrics and applied it to the Minnesota Taconite Health Worker study which contains 196 different job exposure groups (28 similar exposure groups times 7 taconite mines). This approach comprises four steps: for each group (i) obtain EMP dimension information using ISO-TEM 10312/13794 analysis; (ii) use bivariate lognormal distribution to characterize overall EMP size distribution; (iii) use a Bayesian approach to facilitate the formation of the bivariate lognormal distribution; (iv) derive conversion factors between any pair of EMP definitions. The final CFs allow the creation of job exposure matrices (JEMs) for alternative EMP metrics using existing EMP exposures already characterized according to the National Institute of Occupational Safety and Health (NIOSH)-defined EMP exposure metric (length >5 µm with an aspect ratio ≥3.0). The relationships between the NIOSH EMP and other EMP definitions provide the basis of classification of workers into JEMs based on alternate definitions of EMP for epidemiological studies of mesothelioma, lung cancer, and non-malignant respiratory disease.

On the problem of estimation for the bivariate lognormal distribution

Biometrika ◽  
1964 ◽  
Vol 51 (3-4) ◽  
pp. 522-527 ◽  
Author(s):  
M. D. MOSTAFA ◽  
M. W. MAHMOUD
Keyword(s):  
Lognormal Distribution ◽  
Bivariate Lognormal

scholarly journals Incubation period of COVID-19: a rapid systematic review and meta-analysis of observational research

BMJ Open ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. e039652 ◽  
Author(s):  
Conor McAloon ◽  
Áine Collins ◽  
Kevin Hunt ◽  
Ann Barber ◽  
Andrew W Byrne ◽  
...  
Keyword(s):  
Systematic Review ◽  
Incubation Period ◽  
Lognormal Distribution ◽  
Meta Analysis ◽  
Observational Research ◽  
Cochrane Library ◽  
Parameter Estimates ◽  
Sufficient Information ◽  
International Studies ◽  
Shiny App

ObjectivesThe aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19.DesignRapid systematic review and meta-analysis of observational research.SettingInternational studies on incubation period of COVID-19.ParticipantsSearches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies.Primary outcome measuresParameters of a lognormal distribution of incubation periods.ResultsThe incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days.ConclusionsThe choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.

Sign in / Sign up

Export Citation Format

Share Document