13 citations · 29 across the 7 of their papers we have counts for
7 papers · 1 filter
Robust and Computationally Efficient Trimmed L-Moments Estimation for Parametric Distributions
Chudamani Poudyal, Qian Zhao, Hari Sitaula
This paper proposes a robust and computationally efficient estimation framework for fitting parametric distributions based on trimmed L-moments. Trimmed L-moments extend classical…
-estimation of Claim Severity Models Weighted by Kumaraswamy Density
Chudamani Poudyal, Gokarna R. Aryal, Keshav Pokhrel
Statistical modeling of claim severity distributions is essential in insurance and risk management, where achieving a balance between robustness and efficiency in parameter estimat…
Robust Estimation of the Tail Index of a Single Parameter Pareto Distribution from Grouped Data
Chudamani Poudyal
Numerous robust estimators exist as alternatives to the maximum likelihood estimator (MLE) when a completely observed ground-up loss severity sample dataset is available. However,…
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…
Robust Estimation of Loss Models for Lognormal Insurance Payment Severity Data
Chudamani Poudyal
The primary objective of this scholarly work is to develop two estimation procedures - maximum likelihood estimator (MLE) and method of trimmed moments (MTM) - for the mean and var…
Truncated, Censored, and Actuarial Payment-type Moments for Robust Fitting of a Single-parameter Pareto Distribution
Chudamani Poudyal
With some regularity conditions maximum likelihood estimators (MLEs) always produce asymptotically optimal (in the sense of consistency, efficiency, sufficiency, and unbiasedness)…