Robust Estimators for the Parameters of Multivariate Lognormal Distribution

2003 ◽  
Vol 32 (7) ◽  
pp. 1405-1417 ◽  
Author(s):  
Aida Toma
Keyword(s):  
Lognormal Distribution ◽  
Robust Estimators ◽  
Multivariate Lognormal Distribution

Two moment closure techniques for an interacting species model

2021 ◽  
pp. 237-248
Author(s):  
Jennifer Switkes
Keyword(s):  
Lognormal Distribution ◽  
Moment Closure ◽  
Multivariate Normal ◽  
Interacting Species ◽  
Stable Equilibria ◽  
Expected Values ◽  
Multivariate Lognormal Distribution ◽  
Two Populations ◽  
Over Time ◽  
Good Agreement

We explore a stochastic analogue for a generalized deterministic interacting species model for two species. By applying two moment closure techniques, we approximate the expected values, variances, and covariance for the two populations and compare the results. First, we assume a multivariate normal distribution with standard additive variances and covariance. Next, we assume a multivariate lognormal distribution with multiplicative variances and covariance. There is good agreement between the two moment closure techniques. For the stable equilibria, the expected values of the populations converge to values that are similar to, but not equal to, the values of the deterministic equilibria. The variances and covariance also converge over time.

Robust SURE estimates of profitability in the Egyptian insurance market

2021 ◽  
pp. 1-13
Author(s):  
Ahmed H. Youssef ◽  
Amr R. Kamel ◽  
Mohamed R. Abonazel
Keyword(s):  
Robust Regression ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Insurance Market ◽  
Insurance Companies ◽  
Regression Equations ◽  
Robust Estimators ◽  
Exogenous Variables ◽  
Net Profit ◽  
Mm Estimation

This paper proposed three robust estimators (M-estimation, S-estimation, and MM-estimation) for handling the problem of outlier values in seemingly unrelated regression equations (SURE) models. The SURE model is one of regression multivariate cases, which have especially assumption, i.e., correlation between errors on the multivariate linear models; by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Moreover, the effects of outliers may permeate through the system of equations; the primary aim of SURE which is to achieve efficiency in estimation, but this is questionable. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we study and compare the performance of robust estimations with the traditional non-robust (ordinary least squares and Zellner) estimations based on a real dataset of the Egyptian insurance market during the financial year from 1999 to 2018. In our study, we selected the three most important insurance companies in Egypt operating in the same field of insurance activity (personal and property insurance). The effect of some important indicators (exogenous variables) issued by insurance corporations on the net profit has been studied. The results showed that robust estimators greatly improved the efficiency of the SURE estimation, and the best robust estimation is MM-estimation. Moreover, the selected exogenous variables in our study have a significant effect on the net profit in the Egyptian insurance market.

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.

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