Everything about Quantile-parameterized distribution
A quantile-parameterized distribution (QPD) is a probability distribution that is directly parameterized by data. They were created to meet the need for easy-to-use continuous probability distributions flexible enough to represent a wide range of uncertainties, such as those commonly encountered in business, economics, engineering, and science. Because QPDs are directly parameterized by data, they have the practical advantage of avoiding the intermediate step of parameter estimation, a time-consuming process that typically requires non-linear iterative methods to estimate probability-distribution parameters from data. Some QPDs have virtually unlimited shape flexibility and closed-form moments as well. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Quantile-parameterized distributionA 5-minute orientation
- Skim a recent paper: A New Three-Parameter Flexible Unit Distribution and Its Quantile Regression ModelInfluential work since 2022
- Read the landmark paper: Parameter and Quantile Estimation for the Generalized Pareto DistributionCited 1,305 times
- Go sideways: Metalog distributionA closely related topic
- Go sideways: Binomial regressionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Parameter and Quantile Estimation for the Generalized Pareto Distribution
Parametric Quantile Regression Based on the Generalized Gamma Distribution
Quantile-Parameterized Distributions
Halphen Distribution System. II: Parameter and Quantile Estimation
Johnson Quantile-Parameterized Distributions
The Generalized Johnson Quantile-Parameterized Distribution System
Quantile-Parameterized Distributions for Expert Knowledge Elicitation
Nonparametric Multivariate Conditional Distribution and Quantile Regression
🆕 Influential since 2022
A New Three-Parameter Flexible Unit Distribution and Its Quantile Regression Model
Estimation of distribution parameters by mean absolute deviations of a truncated distribution using quantile functions
Two-piece distribution based semi-parametric quantile regression for right censored data
Nonparametric Quantile Regression and Uniform Inference with Unknown Error Distribution
A new parametric quantile regression model based on an Owen distribution
📚 Books worth reading
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🔗 Related topics
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