1 citations · 3 across the 5 of their papers we have counts for
6 papers
Quantile and Log-Quantile Least Squares for Robust-Efficient Fitting and Validation of Log-Location-Scale Loss Models
Mohammed Adjieteh, Vytaras Brazauskas
\begin{quote} {\bf\em Abstract\/}. ~A variety of models for insurance and other types of losses are special cases of the {\em log-location-scale\/} family, with the lognormal and P…
Quantile Least Squares: A Flexible Approach for Robust Estimation and Validation of Location-Scale Families
Mohammed Adjieteh, Vytaras Brazauskas
In this paper, the problem of robust estimation and validation of location-scale families is revisited. The proposed methods exploit the joint asymptotic normality of sample quanti…
Measuring income inequality via percentile relativities
Vytaras Brazauskas, Francesca Greselin, Ricardas Zitikis
"The rich are getting richer" implies that the population income distributions are getting more right skewed and heavily tailed. For such distributions, the mean is not the best me…
Measuring Discrete Risks on Infinite Domains: Theoretical Foundations, Conditional Five Number Summaries, and Data Analyses
Daoping Yu, Vytaras Brazauskas, Ricardas Zitikis
To accommodate numerous practical scenarios, in this paper we extend statistical inference for smoothed quantile estimators from finite domains to infinite domains. We accomplish t…
Method of Winsorized Moments for Robust Fitting of Truncated and Censored Lognormal Distributions
Chudamani Poudyal, Qian Zhao, Vytaras Brazauskas
When constructing parametric models to predict the cost of future claims, several important details have to be taken into account: (i) models should be designed to accommodate dedu…
Robust Estimation of Loss Models for Truncated and Censored Severity Data
Chudamani Poudyal, Vytaras Brazauskas
In this paper, we consider robust estimation of claim severity models in insurance, when data are affected by truncation (due to deductibles), censoring (due to policy limits), and…