scholarly journals The Use of Finite Mixtures of Lognormal Distribution for the Modelling of Income Distributions

2012 ◽  
Vol 20 (4) ◽  
pp. 26-39 ◽  
Author(s):  
Ivana Malá
Keyword(s):  
Lognormal Distribution ◽  
Finite Mixtures ◽  
Income Distributions

Pro-Poor Growth and the Lognormal Distribution

2011 ◽  
Author(s):  
Peter J. Lambert
Keyword(s):  
Growth Pattern ◽  
Lognormal Distribution ◽  
Income Growth ◽  
Poor Growth ◽  
Income Distributions

A widely accepted criterion for the pro-poorness of an income growth pattern is that it should reduce a (chosen) measure of poverty by \textit{more} than if all incomes were growing equi-proportionately. Inequality reduction is not generally seen as either necessary or sufficient for pro-poorness. As empirical income distributions fit well to the lognormal form, lognormality has sometimes been assumed in order to determine analytically the poverty effects of income growth. We show that in a lognormal world, growth is pro-poor in the above sense, if and only if it is inequality-reducing. It follows that lognormality may not be a good paradigm by means of which to examine pro-poorness issues. In contrast, some popular 3-parameter forms offer the ability to conduct nuanced investigation of the pro-poorness growth-inequality nexus.

scholarly journals Application of the L-Moment Method when Modelling the Income Distribution in the Czech Republic

2016 ◽  
Vol 41 (2) ◽  
Author(s):  
Diana Bílková ◽  
Ivana Malá
Keyword(s):  
Czech Republic ◽  
Maximum Likelihood ◽  
Lognormal Distribution ◽  
Moment Method ◽  
Annual Income ◽  
The Czech Republic ◽  
Income Distributions ◽  
L Moments ◽  
Per Capita ◽  
The Eu

This paper deals with modelling income distributions in the Czech Republic in 1992–2007. The net annual income per capita for Czech households is evaluated from data based on the microcensus and the EU-SILC 2005–2008. For all analysed years the distribution of incomes was estimated in the whole sample as well as in the subgroup of households, whose heads are physicists (or experts in related sciences), architects and engineers. Inthe paper the three-parametric lognormal distribution is used as a model. Unknownparameters are estimated with the use of four methods – those of maximum likelihood, quantiles, moments and L-moments.

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