Results 31 to 40 of about 10,621 (115)

COMPARISON BETWEEN BAYESIAN QUANTILE REGRESSION AND BAYESIAN LASSO QUANTILE REGRESSION FOR MODELING POVERTY LINE WITH PRESENCE OF HETEROSCEDASTICITY IN WEST SUMATRA

Barekeng
The poverty line is the threshold income level below which a person or household is considered to be living in poverty. The poverty line is a representation of the minimum rupiah amount needed to meet the minimum basic food needs equivalent to 2100 ...
Lilis Harianti Hasibuan, Ferra Yanuar, Dodi Devianto, Maiyastri Maiyastri, Rudiyanto Rudiyanto   +4 more
doaj   +1 more source

Quantile ridge beta regression model and Bayesian regularized quantile beta regression for solving the skewness and improving the accuracy of the model selection in bounded data

Journal of Nigerian Society of Physical Sciences
Skewed distributions have an additional shape parameter that represents the direction of the asymmetry in the density. If skewness in observations is ignored, statistical inferences based on symmetric distributions may yield biased or even misleading ...
Fedaa Noeel Abdulahad, Majid Khan Majahar Ali, Raja Aqib Shamim   +2 more
doaj   +1 more source

Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

Complexity, 2020
We propose a high-dimensional copula to model the dependence structure of the seemingly unrelated quantile regression. As the conventional model faces with the strong assumption of the multivariate normal distribution and the linear dependence structure,
Nachatchapong Kaewsompong, Paravee Maneejuk, Woraphon Yamaka   +2 more
doaj   +1 more source

Bayesian Estimation of Spatial Lagged Panel Quantile Regression Model

Applied Sciences
This paper proposes a Bayesian estimation method for spatial lagged panel quantile models. The proposed model simultaneously considers spatial lag effects of the dependent variable and the quantile regression framework, enabling effective capture of ...
Man Zhao, Rushan Huang, Hanfang Li, Youxi Luo, Qiming Liu   +4 more
doaj   +1 more source

Variational Bayesian Quantile Regression with Non-Ignorable Missing Response Data

Axioms
For non-ignorable missing response variables, the mechanism of whether the response variable is missing can be modeled through logistic regression. In Bayesian computation, the lack of a conjugate prior for the logistic function poses a significant ...
Juanjuan Zhang, Weixian Wang, Maozai Tian   +2 more
doaj   +1 more source

Short-term Traffic Flow Prediction Method in Bayesian Networks Based on Quantile Regression

Promet (Zagreb), 2020
With the popularization of intelligent transportation system and Internet of vehicles, the traffic flow data on the urban road network can be more easily obtained in large quantities. This provides data support for shortterm traffic flow prediction based
Jing Luo
doaj   +1 more source

Retirement Consumption Puzzle in Malaysia: Evidence from Bayesian Quantile Regression Model

Journal of Probability and Statistics, 2019
The objective of this study is to use the Bayesian quantile regression for studying the retirement consumption puzzle, which is defined as the drop in consumption upon retirement, using the cross-sectional data of the Malaysian Household Expenditure ...
Ros Idayuwati Alaudin, Noriszura Ismail, Zaidi Isa   +2 more
doaj   +1 more source

Bayesian Quantile Regression for Partial Functional Linear Spatial Autoregressive Model

Axioms
When performing Bayesian modeling on functional data, the assumption of normality is often made on the model error and thus the results may be sensitive to outliers and/or heavy tailed data.
Dengke Xu, Shiqi Ke, Jun Dong, Ruiqin Tian   +3 more
doaj   +1 more source

Corrigendum: Modified quantile regression for modeling the low birth weight

Frontiers in Applied Mathematics and Statistics, 2023
Ferra Yanuar, Hazmira Yozza, Aidinil Zetra   +2 more
doaj   +1 more source

Generative AI for Bayesian Computation

Entropy
Generative Bayesian Computation (GBC) provides a simulation-based approach to Bayesian inference. A Quantile Neural Network (QNN) is trained to map samples from a base distribution to the posterior distribution.
Nick Polson, Vadim Sokolov
doaj   +1 more source