Results 31 to 40 of about 30,799 (217)

Model Efficiency and Uncertainty in Quantile Estimation of Loss Severity Distributions

Risks, 2019
Quantiles of probability distributions play a central role in the definition of risk measures (e.g., value-at-risk, conditional tail expectation) which in turn are used to capture the riskiness of the distribution tail.
Vytaras Brazauskas, Sahadeb Upretee
doaj   +1 more source

DGQR estimation for interval censored quantile regression with varying-coefficient models.

PLoS ONE, 2020
This paper propose a direct generalization quantile regression estimation method (DGQR estimation) for quantile regression with varying-coefficient models with interval censored data, which is a direct generalization for complete observed data.
ChunJing Li, Yun Li, Xue Ding, XiaoGang Dong   +3 more
doaj   +1 more source

Kernel estimation for quantile sensitivities [PDF]

2007 Winter Simulation Conference, 2007
AbstractQuantiles, also known as value‐at‐risks in the financial industry, are important measures of random performances. Quantile sensitivities provide information on how changes in input parameters affect output quantiles. They are very useful in risk management.
Liu, Guangwu, Hong, Liu Jeff
openaire   +2 more sources

QUANTILE REGRESSION MODEL ON RAINFALL IN MAKASSAR 2019

Barekeng, 2023
Makassar is an area that has a monsoon rainfall pattern. This study aims to find a quantile regression model and to determine the factors that significantly influence rainfall in the city of Makassar.
Wahidah Sanusi, Sukarna Sukarna, Nur Harisahani   +2 more
doaj   +1 more source

Weighted quantile estimators

, 2023
In this paper, we consider a generic scheme that allows building weighted versions of various quantile estimators, such as traditional quantile estimators based on linear interpolation of two order statistics, the Harrell-Davis quantile estimator and its trimmed modification.
openaire   +2 more sources

Self-Weighted Quantile Estimation for Drift Coefficients of Ornstein–Uhlenbeck Processes with Jumps and Its Application to Statistical Arbitrage

Mathematics
The estimation of drift parameters in the Ornstein–Uhlenbeck (O-U) process with jumps primarily employs methods such as maximum likelihood estimation, least squares estimation, and least absolute deviation estimation.
Yuping Song, Ruiqiu Chen, Chunchun Cai, Yuetong Zhang, Min Zhu   +4 more
doaj   +1 more source

Bayesian Inference in Extremes Using the Four-Parameter Kappa Distribution

Mathematics, 2020
Maximum likelihood estimation (MLE) of the four-parameter kappa distribution (K4D) is known to be occasionally unstable for small sample sizes and to be very sensitive to outliers.
Palakorn Seenoi, Piyapatr Busababodhin, Jeong-Soo Park   +2 more
doaj   +1 more source

A Quantile Shift Approach to Main Effects and Interactions in a 2-by-2 Design

Methodology
When comparing two independent groups, shift functions are basically techniques that compare multiple quantiles rather than a single measure of location, the goal being to get a more detailed understanding of how the distributions differ.
Rand R. Wilcox, Guillaume A. Rousselet
doaj   +1 more source

A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models

Mathematics, 2023
In this paper, a Bayesian variable selection method for spatial autoregressive (SAR) quantile models is proposed on the basis of spike and slab prior for regression parameters.
Yuanying Zhao, Dengke Xu
doaj   +1 more source

Modeling of flood extremes using regional frequency analysis of sites of Khyber Pakhtunkhwa, Pakistan

Journal of Flood Risk Management, 2021
The study provides results of regional frequency analysis (RFA) using annual maximum peak flows (AMPF) of 36 sites located on various streams and rivers of Khyber‐Pakhtunkhwa, Pakistan.
Muhammad Shafeeq ul Rehman Khan, Zamir Hussain, Ishfaq Ahmad, Farzana Noor   +3 more
doaj   +1 more source