Results 41 to 50 of about 204,929 (198)
Multivariate Quantile Function Forecaster
, 2022 We propose Multivariate Quantile Function Forecaster (MQF$^2$), a global probabilistic forecasting method constructed using a multivariate quantile function and investigate its application to multi-horizon forecasting. Prior approaches are either autoregressive, implicitly capturing the dependency structure across time but exhibiting error accumulation
Kan, Kelvin +6 more
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Mathematical and Computational Applications, 2023 Exploration of solar irradiance can greatly assist in understanding how renewable energy can be better harnessed. It helps in establishing the solar irradiance climate in a particular region for effective and efficient harvesting of solar energy ...
Daniel Maposa +2 more
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Bayesian analysis of a Tobit quantile regression model [PDF]
, 2007 This paper develops a Bayesian framework for Tobit quantile regression. Our approach is organized around a likelihood function that is based on the asymmetric Laplace dis- tribution, a choice that turns out to be natural in this context.
Stander, J, Yu, K
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Newdistns: An R Package for New Families of Distributions
Journal of Statistical Software, 2016 The contributed R package Newdistns written by the authors is introduced. This package computes the probability density function, cumulative distribution function, quantile function, random numbers and some measures of inference for nineteen families of ...
Saralees Nadarajah, Ricardo Rocha
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Extreme Events Analysis Using LH-Moments Method and Quantile Function Family
Hydrology, 2023 A direct way to estimate the likelihood and magnitude of extreme events is frequency analysis. This analysis is based on historical data and assumptions of stationarity, and is carried out with the help of probability distributions and different methods ...
Cristian Gabriel Anghel +2 more
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Estimation in functional linear quantile regression
, 2013 This paper studies estimation in functional linear quantile regression in which the dependent variable is scalar while the covariate is a function, and the conditional quantile for each fixed quantile index is modeled as a linear functional of the ...
Kato, Kengo
core +1 more source
Vector Quantile Regression: An Optimal Transport Approach [PDF]
, 2015 We propose a notion of conditional vector quantile function and a vector quantile regression. A \emph{conditional vector quantile function} (CVQF) of a random vector $Y$, taking values in $\mathbb{R}^d$ given covariates $Z=z$, taking values in $\mathbb{R}
Carlier, Guillaume +2 more
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Generic Inference on Quantile and Quantile Effect Functions for Discrete Outcomes [PDF]
Journal of the American Statistical Association, 2016 Quantile and quantile effect functions are important tools for descriptive and causal analyses due to their natural and intuitive interpretation. Existing inference methods for these functions do not apply to discrete random variables. This paper offers a simple, practical construction of simultaneous confidence bands for quantile and quantile effect ...
Chernozhukov, Victor +3 more
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Nonparametric Estimation of Quantile and Quantile Density Function
Journal of Biometrics & Biostatistics, 2017 In this article, we derive a new and unique method of estimating quantile and quantile density function, which is based on moments of fractional order statistics. A comparison of the proposed estimators is made with existing popular nonparametric quantile and quantile density estimators, in terms of mean squared error (MSE) for censored and uncensored ...
Yang X, Hutson AD, Wang D
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Uncertainty orders on the sublinear expectation space
Open Mathematics, 2016 In this paper, we introduce some definitions of uncertainty orders for random vectors in a sublinear expectation space. We all know that, under some continuity conditions, each sublinear expectation 𝔼 has a robust representation as the supremum of a ...
Tian Dejian, Jiang Long
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